# A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

Authors: Bo Li; Siyuan Yu; Jing Ma; Liying Tan
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

In this paper, a neural network approach is used to conduct an in-depth study and analysis of the fast capture tracking method for laser links between nonorbiting platforms. The experimental platform of the convolutional neural network- (CNN-) based free-space optical communication (FSO) wavefront correction system is built indoors, and the wavefront distortion correction performance of the CNN-based wavefront correction method is investigated. The experimental results show that the coupling power loss can be reduced to small after the CNN method correction under weak and strong turbulence. The accuracy of the above model is verified by comparing the simulation data with the experimentally measured data, thus realizing the coordinate decoupling of the coarse aiming mechanism and weakening the influence of structural factors on the tracking accuracy of the system. The tracking correlation equation of the influence of beam far-field dynamic characteristics on the tracking stability of the link is established, and the correlation factor variance of beam far-field dynamic characteristics is used to provide a quantitative analysis method for the evaluation and prediction of the comprehensive performance of the link tracking stability. The influence of beam divergence angle, wavefront distortion, detector accuracy, and atmospheric turbulence disturbance on the correlation factor variance of beam far-field dynamic characteristics of laser link beacons is modelled, and the link tracking stability optimization method is proposed under the requirement of link tracking accuracy, which provides an effective solution analysis method to realize the improvement of laser link tracking stability.


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## Body

## 1. Introduction

With the rapid development of science and technology, people’s demand for information transmission rate is getting higher and higher. The current microwave communication system is affected by low bandwidth, slow modulation rate, and other reasons, and its development has encountered a bottleneck. Compared with microwave communication, laser communication has the characteristics of fast transmission rate, large optical gain, small beam dispersion angle, anti-interference, and anti-interception ability [1]. Despite the many advantages of satellite laser communication, it is very difficult to implement because it involves many interdisciplinary disciplines such as optical and mechanical computing, of which the targeting, capturing, and tracking technology is one of the difficult points [2]. The existing capture systems in the space link largely use the beacon light capture mode, which uses a wide beam beacon at the beginning of the scan capture; the beacon beam scattering angle is generally dozens of times the size of the signal beam scattering angle and then switch to signal light for tracking after the basic alignment of the two ends [3]. However, for the more demanding deep-space laser communication, the use of a large beam scatter angle of the capture beacon light will inevitably increase the output power of the system, resulting in the introduction of a large peak power consumption at the early stage of laser communication chain building. At the same time, the size, weight, and complexity of the system also increase significantly [4]. Therefore, compared with the classical beacon capture technique, the beacon-free capture technique can simplify the size, structure, and power consumption of the onboard terminal while ensuring system capture performance. However, it makes the scanning algorithm and scanning strategy complicated.Most of the current research on satellite laser link stabilization and maintenance techniques is based on beam near-field scenarios, with only a simple plane-wave approximation of the beam far-field characteristics. However, the quality of the emitted beam from the terminal itself, the laser link tracking angle deviation, and the atmospheric turbulent transmission medium all affect the variation of the dynamic characteristics of the far-field of the beam [5]. The receiving detector detects the spot in the far-field of the beam, and the beam far-field dynamic characteristics will directly affect the detector received light intensity distribution. Especially in the long distance, closed-loop two-way laser link conditions, the tracking system control accuracy, beam quality, and detection accuracy, and other high requirements, in the tracking characteristics and the beam far-field optical dynamic characteristics, do not meet the stability requirements, and the overall performance of the link tracking degradation will reduce the quality of data communication [6]. In the field of satellite laser link fast establishment and stability maintenance technology research, there are still some problems to be improved and solved: how to analyse the link tracking stability based on the beam far-field dynamic characteristics; the quantitative analysis of link tracking stability being insufficient; the ground equivalent experiment not meeting the demand of in-orbit tracking stability optimization, and difficulty in realizing the evaluation and prediction of laser link tracking stability. For the working principle of space laser communication PAT system, the key elements affecting its performance are analysed and the general idea of the research is proposed accordingly. Firstly, the basic concept of space laser communication PAT technology explained, the composition and function of classical space laser communication PAT system, the working process and its characteristics are introduced. Then, the structural composition of space laser communication fiducial-free PAT system is analysed and selected, and the two parts of system composition and system control structure are introduced, in which there is the coarse tracking servo [7]. In the system composition, the coarse tracking servo-table and fine tracking oscillator are introduced and selected, the working process of the image acquisition module is described, the single-detector composite axis structure and the dual-detector composite axis structure in the control structure are analysed, and the single-detector composite axis structure with strong coupling and low system complexity is selected. Finally, the power consumption of the beacon-free PAT system is analysed, and the key technologies that need to be solved are analysed and summarized.Compared with other research results, the efficiency and accuracy of fast motion and method tracking between nonrail transit platforms using neural networks are improved by about 8.45%. The design scheme of the spindle servo system of the heel catching terminal is based on the new iterative learning algorithm. The decoupling model of the two-dimensional servo turntable is established based on the changing optical transmission relationship of the incident beam at the receiving end. The model is verified through experiments, and the characteristics of periodic disturbance factors and their effects on the control system are analysed in detail. The iterative learning method is used to reduce the periodic torque pulsation of the permanent magnet synchronous motor servo system, suppress the system disturbance, and achieve good dynamic tracking, and the suppression effect of the designed control strategy on the system torque pulsation is verified through experiments.Section2 mainly sorts out and explains related research work and puts forward the shortcomings of existing research. In Section 3, the neural network algorithm and the corresponding system construction are mainly introduced, and the neural network algorithm has been improved and upgraded. In Section 4, the results of the algorithm and the performance test results of the system are analysed and discussed in depth. Section 5 is a summary of the research of this article.


## 2. Related Work

Satellite-ship laser communication mainly refers to the data transmission between satellites and ships using lasers. Telescope capture, tracking, and targeting is the key technology of laser communication, which requires rapid capture and alignment of the apparent axis between the ship and the satellite to establish a communication link with high accuracy, high probability, and high dynamic tracking [8]. The link is usually initiated by a telescope at one end, which uses beacon light to scan or gaze at an uncertain region, and then a telescope at the other end scans or gazes to establish a communication link [9]. This method of establishing an optical link is the most common method used in laser communications and requires a high level of pointing accuracy. For short-range communication links of tens of kilometres on ships or in the air, the difficulty of capture can be reduced by increasing the divergence angle of the beacon light and increasing the optical power to reduce the pointing accuracy required by the telescope. An alternative approach to achieving close-range ship-to-ship laser communications is to use modulated rearward reflectors (MRRs). An active telescope is used at one end, and a small semipassive MRR is used at the other end [10]. For one end of the link, the MRR no longer has a strict pointing requirement. However, the Star Sea laser communications shipboard telescope is very different from the ground-based telescope system [11]. To achieve long range, strongly perturbed satellite-to-ship quantum or laser communications, precise alignment, and fast capture of invisible satellite terminals using narrow beams under shipboard conditions must be addressed, and capture alignment of the apparent axis is a prerequisite for successful quantum/laser communications in space and key technology to improve communication capabilities. Star-ship laser communication or quantum communication is still a gap at home and abroad. Therefore, it is of great importance to study star-ship apparent-axis capture technology [12].Target detection is an important research direction in the field of computer vision, where the main task is to extract the coordinates and analyse the category of the target to be extracted in the picture. Target detection is the most fundamental part in the context of intelligent recognition and is the basis for other advanced computer vision tasks [13]. Target detection research has high use in the direction of computer vision intelligent security protection, human-computer interaction, robot space exploration, and low-altitude target tracking. Target detection, along with tracking and classification, is an important task in video analysis [14]. In a practical target tracking system, the detection task is first performed to get the position in the first frame of the target and subsequently locate and track it in the subsequent frames of the video. Therefore, target detection is the reorder task of the target tracking task, and target detection algorithms have undergone a long development at home and abroad, mainly divided into traditional algorithms and deep learning-based target detection algorithms [15]. For networking, there are three common topology construction methods, Mesh network topology with high connectivity.Motion Standard Times (MST) has the simplest idea and the simplest path selection, Testament debit (DT) algorithm has the advantages of both, and DT algorithm can be localized to form a hierarchical distributed network topology. To achieve high-capacity information transmission in real-time, high-speed wireless optical communication is the main trend of future optical communication development. The key to high-speed wireless optical communication lies in the combination of high modulation rate and high-power output, high sensitivity detection and communication low error code, and other technology implementation. At present, most wireless optical communication systems use point-to-point communication. In contrast, the communication link cannot have obscurants, which seriously affects the use of optical communication time and coverage. To solve these problems, a laser networking structure can be used to reprogram the optical communication link under extreme weather conditions, thus improving the usage time and coverage of optical communication. Wireless laser communication is widely used in near-ground, air-to-air, intersatellite, and intersatellite communication links and will be expanded toward deep-space laser communication and subsea laser communication. In this paper, we focus on the capture, alignment, and tracking technology in wireless laser communication systems. By reviewing the data, summarizing the previous experience, and combining it with the specific requirements of practical experiments, the design of the beacon-free optical APT system is completed. Finally, the performance of the APT system is tested by establishing an optical communication link for outfield experiments. Based on this, a nested structure APT system was designed to cope with long-distance communication.


## 3. Neural Network Design of the Fast Capture Tracking Method for Laser Links between Nonorbiting Platforms

### 3.1. Analysis of the Improved Neural Network Algorithm

As a primitive algorithm for the transition from traditional saliency detection algorithms to deep neural network approaches, these algorithms still retain shades of traditional algorithms. Multilayer perceptron-based detection approaches typically provide depth features [16]. Suggested object-based approaches use object proposals or bounding boxes as the basic processing units of the network to encode object information. Region proposals are first generated; then a DNN is used to classify each region proposal as a predefined type of standard binary mapping; and finally, region proposals of similar shape are integrated into a salient prediction map [17]. This approach requires first using other algorithms to a rough target outline or suggestion box whose input possesses a primary segmentation effect and then using a convolutional neural network to feature-extract this suggestion map with segmentation information and a multilayer perceptual classifier to achieve saliency prediction. Such primary deep networks based on multilayer perceptron’s have achieved good results compared to traditional algorithms proposed recurrent structures with multiple encoding networks to optimize saliency features by cleverly passing saliency features from a course to a more refined tributary, with the output features of both tributaries supervised using a true labelled salient object mask.Convolutional layers first use local feature processing to perceive local features in the process of extracting image feature information; different layers have different regions and effective areas for feature processing; and by stacking convolutional layers, there can be effective implementation of perception from local to global information. By defining different sizes and numbers of convolution kernels and further convolving the image pixel by pixel using specific step size and specific complementary zero way, a feature map of a specific size (width, height, and several channels) will be output. Each pixel of the feature map is a complex collection of information formed by different convolution and convolution, which is the result of the perception of certain local information of the image, as shown in Figure1.Figure 1
Improved neural network algorithm.The convolution layer is a downsampling convolution operation on the image feature map, which aims to reduce the size (width and height) of the feature map in a specific multiplicative relationship and keep the number of channels constant. Assuming that the window size of the convolution layer is 2 and the step number is 2, the feature map is reduced to half of its original size after the convolution operation. The convolution operation serves to reduce the size of the feature map, which in turn reduces the parameters of the network and suppresses the overfitting phenomenon that exists during the training process. On the contrary, the convolution operation can guarantee the translation invariance of the convolution operation to a certain extent and further reduce the error of the convolutional neural network by suppressing the offset of feature information through mean convolution or maximum convolution. The fully connected layer does the integration of local features by connecting with each pixel of the feature map to obtain the global information of the image. It should be particularly emphasized that the fully connected layer at the end of the network acts as a classifier in the network structure and the output layer of the network, mapping the semantic features learned by the convolutional network to the same space as the labels. In concrete terms, the convolution operation is done with a convolution kernel, and the output can be either a representation vector for image-level classification or a representation matrix for pixel-level classification. After convolution and full serial operation, the network outputs feature vectors, and the training process is further supervised. Specific loss functions are used to train the network through trainers, so as to obtain the desired output.The topology, or the shape of the network, is often formed by topology control algorithms. Furthermore, the main purpose of topology control is to ensure the connectivity of the network, reduce the energy consumption of the nodes, and prolong the survival of the network. In general, topology control algorithms can be divided into two subproblems: topology construction and topology reconfiguration. On the one hand, the nodes in the network have neighbouring nodes, and if they are not neighbours then their communication distance is not enough and the nodes cannot communicate with them, so the nodes can only transmit information with a limited number of neighbouring nodes. On the other hand, due to the characteristics of the free-space optical communication itself transceiver, each node has a maximum number of connections, which means that it can only establish a link with a limited number of nodes at most. Due to these two factors, the nodes in the FSO network are limited; they have a maximum access degree, which is described in detail and analytically derived below. The maximum access degree of a node is represented in graph theory as a vertex in a topology graph can only connect to a finite number of vertices:(1)E=kdn2,where E denotes the network node energy, k denotes the coefficient of the energy model, d denotes the distance between nodes, and n denotes that there is more than one energy loss model. The model at n=2 is used in this paper due to the small transmission distance of free-space optical communication:(2)ETXm,d,φ=ETX_PEm−EPXm,d,φ,EPXm,d,φ=mεφdn2,ETXm,d,φ=mETX_PE−mεφdn2.The lower layer network topology is formed by learning the number of noncluster head nodes in the cluster head management area using machine learning, predicting the number of nonnumber of nodes in that management area, and calculating the average number of nodes in the cluster head its management area, and finally forming the optimal lower layer network topology by a greedy algorithm. The topology formed by using this algorithm is a lower layer network topology with self-learning, adaptive, and high destructive resistance. As the limited degree of nodes in the FSO network is the main problem, compared to microwave wireless communication, FSO communication cannot send electromagnetic waves all around like microwave. FSO is communicated by laser with a narrow beam, so it cannot communicate with many nodes like microwave wireless communication. In this paper, the maximum access degree of FSO nodes is derived by combining the physical degree of FSO nodes and the initial energy of FSO nodes:(3)∑i=1ctiETXi−ERXi=E0.The lower-layer network topology is formed by learning the number of noncluster head nodes in the cluster head’s management area by machine learning, predicting the number of nonnumber nodes in that management area, and calculating the average degree of nodes in the cluster head’s management area, and finally forming the optimal lower layer network topology by a greedy algorithm. The lower layer network topology formed using this algorithm is self-learning, adaptive, and highly destructive lower layer network topology. After the topology is formed, the nodes in the topology are not stationary and the relative motion of wireless optical network nodes is generally large due to their fast movement, plus other external factors such as rain and fog days. The topology in the network tends to change, and how to cope with such changes is crucial for a mature and robust network. This paper mainly uses the Tyson local dynamics of polygons and the predictive role of machine learning to achieve dynamic management of upper cluster heads and lower nodes, as shown in Figure2.Figure 2
Topology formed by the algorithm.Neural network is a model in machine learning. It is an algorithmic mathematical model that imitates the behavioural characteristics of animal neural networks and performs distributed and parallel information processing. This kind of network relies on the complexity of the system and achieves the purpose of processing information by adjusting the interconnection between many internal nodes. The specific form of expression is that the network will memorize the previous information and apply it to the calculation of the current output; that is, the nodes between the hidden layers are no longer unconnected but connected, and the input of the hidden layer includes not only the output of the input layer but also the output of the hidden layer at the previous moment.The algorithm forms the upper topology through steps such as nodes determining their information, obtaining the set of neighbouring node information, determining the cluster head and noncluster head nodes, performing Delaunay for triangulation, constructing Tyson polygons, and determining the management region where the cluster head and noncluster head nodes are located, with the cluster head determining the number of nonquantities within it. The process of forming the management region: the Tyson polygon formed according to the previous process is determined as the management region of the cluster head, and then the cluster head records the nonquantity of its management region in preparation for the next step of machine learning to predict the nonquantity within its management region:(4)EDi=∑i=1zEiEjdij.Although the links in the network are unstable and the nodes are in constant motion, the maximum access degree of the cluster heads is known since it is known. Therefore, we only need to predict the number of nonnumbers in the cluster head management region to determine the optimal topological distribution in that region. Furthermore, since the movement of nodes is generally regular or the movement of nodes is periodic, it is sufficient for us to predict the number of no quantities within just one cycle. After that, we construct the wireless optical network topology based on the MST algorithm (greedy algorithm). To learn the prediction of the nonquantity within the cluster head management region, the training set is necessary. Therefore, we need to record the number of non-s over some time by initializing the network as our training set. It then assumed that the number of non in the period to be predicted is correlated with the number of non in several precious moments:(5)Stf=w1Stf−w2S2f−⋯−wnSnf.Common methods using least-squares include finding the sum of squares of residuals, finding the squared difference, and finding the variance. This example uses finding the sum of squares of residuals to determine whether a preset threshold is satisfied, determine the parameters of the multiple linear regression model, and construct the multiple linear regression model. In this paper, we determine the number of hops of a multihop by finding the average node degree, thus achieving a fixed multihop model, which has many benefits. Firstly, for the movement of lower-level nodes, we do not need to manage for each lower-level node. We only need to know the number of lower-level nodes in the management area of each cluster head to give it the access service.


### 3.2. Experimental Design

In a two-way intersatellite laser link, the satellite laser communication terminal tracking device detects the tracking angle deviation of the link to achieve rapid link establishment and maintenance. After the laser link is established, under the large closed-loop condition of the tracking link, the tracking beams emitted by the two terminals will be correlated in the far field, and we consider that the far-field dynamic characteristics of the beams are correlated at this time [18]. Disturbed by the divergence angle, wavefront aberration, and the spatial environment, the beams will produce tracking angle deviations in the high-speed dynamics. In this section, a theoretical analysis model of the far-field dynamic characteristics of the interstellar link beam and a reference coordinate system for the far-field dynamic inertia of the beam are established to analyse the effect of the dynamic tracking angle deviation on the correlation of the far-field dynamic characteristics of the beam. To establish the laser link, two optical terminals are aimed at the other side simultaneously. First, the aiming device must complete the advance aiming and coarse tracking process using the known orbit parameters. Under the large closed-loop conditions of high-speed dynamic tracking, the tracking angular deviation generated by the tracking link causes the tracking beam to vary in the far-field dynamic characteristics.In the satellite laser tracking link, the satellite orbit and attitude information are inaccurate due to the long-distance and relative motion of the two terminals, and the autonomous turntable tracking method is used, where two optical terminals simultaneously track the tracking beacon beam of the other optical terminal. Therefore, tracking angle deviations are detected in the receiving detectors of both optical terminals. The tracking angle deviation affects the far-field dynamic characteristics of the beam during the transmission of the beam emitted by the optical antenna through free space [19]. Based on the numerical simulation results, the data curves are fitted, and the curves of the detected light intensity at the FO point with the tracking angle deviation A are shown in Figure 3. When the tracking angle deviation is less than 6 μrad, the normalized far-field intensity distribution shows a linearly decreasing trend as a decrease; when it is larger than 6 μrad, the normalized intensity at the FO point tends to a constant value, indicating no correlation between the far field of the two link beams when the tracking angle deviation reaches a certain angle.Figure 3
Normalized light intensity versus angular deviation with tracking.When Programming Ability Test (PAT) enters the capture state, both ends are in the open-loop scanning state, which cannot be fed back to correct the errors generated in the scanning process. In the scanning state of the signal light to the uncertain region, the real scanning trajectory will deviate from the original set scanning trajectory due to the jitter of its visual axis, resulting in the scanning trajectory not being able to completely cover the uncertain region, which eventually affects the probability of successful capture [20]. To improve the coverage of the uncertain region by the scattering angle of the scanning beam, the overlap factor between adjacent scanning beams can be increased, or the missed scanning due to the jitter of the visual axis can be reduced as much as possible. Therefore, it is important to choose the appropriate overlap factor for the scan trajectory to cover the uncertain region based on reducing the jitter of the visual axis because the composite scanning method contains two parts of the overlap factor.The working sequence of the oscillator and the turntable in the composite scan is that the oscillator starts the spiral scan from the centre of the uncertain region, and then the turntable jumps to the next scan point after the scan is completed and continues until the scan trajectory completely covers the whole uncertain region and the motion trajectory of the turntable is a rectangular spiral scan during the whole process. The spiral scan time in the subregion has been analysed in the previous subsection, and this subsection mainly analyses the time of the turntable moving in the adjacent subregion and the number of steps to jump, to deduce the scan time of the whole compound scan. Compared to the two scanning modes of continuous scanning and stepping scanning, the vibrating spiral scanning within the subregion is suitable for continuous scanning and the rectangular spiral scanning by the turntable between adjacent subregions is more suitable for stepping scanning, which can be regarded as a special case of stepping scanning. Therefore, the composite scanning uses stepping scanning, and the stepping scanning equation is(6)ΔtI=2Lc−1Fac−2tres.The cooperative process of the coarse and fine alignment system is shown in Figure4. The coarse alignment process is shown in Figure 4. The upper computer at the receiving end performs image processing on the light spot on the rear projection screen captured by the CCD camera. It uses the light spot position information obtained from the processing as the feedback quantity to drive the 2D aiming head at the transmitting end to control the antenna pointing for coarse alignment of the beam. However, due to the limited execution accuracy of the 2D aiming head, the beam cannot be precisely aligned with the viewing axis of the receiving antenna. Hence, the main purpose of the coarse alignment is to move the dynamic range of the fine 2D piezo platform to a range that can contain the target bullseye for the effective execution of the fine alignment stage. The fine alignment process is shown in Figure 4. After completing the coarse alignment work, the target bullseye is contained within the executable range of the piezoelectric stage. Then, the pointing of the beam can be controlled by the piezoelectric microdynamics stage to further eliminate the residuals left by the coarse alignment.Figure 4
Schematic diagram of the coarse and fine alignment process.Numerous problems within the optoelectronic tracking system involve optimization problems. The deep learning framework for target detection and tracking algorithm training requires the use of optimization algorithms. Online learning of target tracking requires the use of suitable optimization algorithms to reduce the loss function. Similarly, numerous optimization scenarios within the optoelectronic system require optimization, such as adaptive optics and fiber coupling. Adaptive optics requires multidimensional optimization that can enable clear imaging in optoelectronic tracking systems, making image quality much better, and reducing the difficulty of target detection and target tracking algorithms [21]. Fiber-optic coupling is a simplified version of adaptive optics, and generally, needles are optimized in only two dimensions. For the many scenarios that require optimization, many are complex problems that cannot be modelled and require high real-time algorithms and most solutions are modelled directly using model-free optimization algorithms, which construct the optimization target as a suitable objective function and optimize the objective function using the relevant model-free algorithm to make it optimal.


## 3.1. Analysis of the Improved Neural Network Algorithm

As a primitive algorithm for the transition from traditional saliency detection algorithms to deep neural network approaches, these algorithms still retain shades of traditional algorithms. Multilayer perceptron-based detection approaches typically provide depth features [16]. Suggested object-based approaches use object proposals or bounding boxes as the basic processing units of the network to encode object information. Region proposals are first generated; then a DNN is used to classify each region proposal as a predefined type of standard binary mapping; and finally, region proposals of similar shape are integrated into a salient prediction map [17]. This approach requires first using other algorithms to a rough target outline or suggestion box whose input possesses a primary segmentation effect and then using a convolutional neural network to feature-extract this suggestion map with segmentation information and a multilayer perceptual classifier to achieve saliency prediction. Such primary deep networks based on multilayer perceptron’s have achieved good results compared to traditional algorithms proposed recurrent structures with multiple encoding networks to optimize saliency features by cleverly passing saliency features from a course to a more refined tributary, with the output features of both tributaries supervised using a true labelled salient object mask.Convolutional layers first use local feature processing to perceive local features in the process of extracting image feature information; different layers have different regions and effective areas for feature processing; and by stacking convolutional layers, there can be effective implementation of perception from local to global information. By defining different sizes and numbers of convolution kernels and further convolving the image pixel by pixel using specific step size and specific complementary zero way, a feature map of a specific size (width, height, and several channels) will be output. Each pixel of the feature map is a complex collection of information formed by different convolution and convolution, which is the result of the perception of certain local information of the image, as shown in Figure1.Figure 1
Improved neural network algorithm.The convolution layer is a downsampling convolution operation on the image feature map, which aims to reduce the size (width and height) of the feature map in a specific multiplicative relationship and keep the number of channels constant. Assuming that the window size of the convolution layer is 2 and the step number is 2, the feature map is reduced to half of its original size after the convolution operation. The convolution operation serves to reduce the size of the feature map, which in turn reduces the parameters of the network and suppresses the overfitting phenomenon that exists during the training process. On the contrary, the convolution operation can guarantee the translation invariance of the convolution operation to a certain extent and further reduce the error of the convolutional neural network by suppressing the offset of feature information through mean convolution or maximum convolution. The fully connected layer does the integration of local features by connecting with each pixel of the feature map to obtain the global information of the image. It should be particularly emphasized that the fully connected layer at the end of the network acts as a classifier in the network structure and the output layer of the network, mapping the semantic features learned by the convolutional network to the same space as the labels. In concrete terms, the convolution operation is done with a convolution kernel, and the output can be either a representation vector for image-level classification or a representation matrix for pixel-level classification. After convolution and full serial operation, the network outputs feature vectors, and the training process is further supervised. Specific loss functions are used to train the network through trainers, so as to obtain the desired output.The topology, or the shape of the network, is often formed by topology control algorithms. Furthermore, the main purpose of topology control is to ensure the connectivity of the network, reduce the energy consumption of the nodes, and prolong the survival of the network. In general, topology control algorithms can be divided into two subproblems: topology construction and topology reconfiguration. On the one hand, the nodes in the network have neighbouring nodes, and if they are not neighbours then their communication distance is not enough and the nodes cannot communicate with them, so the nodes can only transmit information with a limited number of neighbouring nodes. On the other hand, due to the characteristics of the free-space optical communication itself transceiver, each node has a maximum number of connections, which means that it can only establish a link with a limited number of nodes at most. Due to these two factors, the nodes in the FSO network are limited; they have a maximum access degree, which is described in detail and analytically derived below. The maximum access degree of a node is represented in graph theory as a vertex in a topology graph can only connect to a finite number of vertices:(1)E=kdn2,where E denotes the network node energy, k denotes the coefficient of the energy model, d denotes the distance between nodes, and n denotes that there is more than one energy loss model. The model at n=2 is used in this paper due to the small transmission distance of free-space optical communication:(2)ETXm,d,φ=ETX_PEm−EPXm,d,φ,EPXm,d,φ=mεφdn2,ETXm,d,φ=mETX_PE−mεφdn2.The lower layer network topology is formed by learning the number of noncluster head nodes in the cluster head management area using machine learning, predicting the number of nonnumber of nodes in that management area, and calculating the average number of nodes in the cluster head its management area, and finally forming the optimal lower layer network topology by a greedy algorithm. The topology formed by using this algorithm is a lower layer network topology with self-learning, adaptive, and high destructive resistance. As the limited degree of nodes in the FSO network is the main problem, compared to microwave wireless communication, FSO communication cannot send electromagnetic waves all around like microwave. FSO is communicated by laser with a narrow beam, so it cannot communicate with many nodes like microwave wireless communication. In this paper, the maximum access degree of FSO nodes is derived by combining the physical degree of FSO nodes and the initial energy of FSO nodes:(3)∑i=1ctiETXi−ERXi=E0.The lower-layer network topology is formed by learning the number of noncluster head nodes in the cluster head’s management area by machine learning, predicting the number of nonnumber nodes in that management area, and calculating the average degree of nodes in the cluster head’s management area, and finally forming the optimal lower layer network topology by a greedy algorithm. The lower layer network topology formed using this algorithm is self-learning, adaptive, and highly destructive lower layer network topology. After the topology is formed, the nodes in the topology are not stationary and the relative motion of wireless optical network nodes is generally large due to their fast movement, plus other external factors such as rain and fog days. The topology in the network tends to change, and how to cope with such changes is crucial for a mature and robust network. This paper mainly uses the Tyson local dynamics of polygons and the predictive role of machine learning to achieve dynamic management of upper cluster heads and lower nodes, as shown in Figure2.Figure 2
Topology formed by the algorithm.Neural network is a model in machine learning. It is an algorithmic mathematical model that imitates the behavioural characteristics of animal neural networks and performs distributed and parallel information processing. This kind of network relies on the complexity of the system and achieves the purpose of processing information by adjusting the interconnection between many internal nodes. The specific form of expression is that the network will memorize the previous information and apply it to the calculation of the current output; that is, the nodes between the hidden layers are no longer unconnected but connected, and the input of the hidden layer includes not only the output of the input layer but also the output of the hidden layer at the previous moment.The algorithm forms the upper topology through steps such as nodes determining their information, obtaining the set of neighbouring node information, determining the cluster head and noncluster head nodes, performing Delaunay for triangulation, constructing Tyson polygons, and determining the management region where the cluster head and noncluster head nodes are located, with the cluster head determining the number of nonquantities within it. The process of forming the management region: the Tyson polygon formed according to the previous process is determined as the management region of the cluster head, and then the cluster head records the nonquantity of its management region in preparation for the next step of machine learning to predict the nonquantity within its management region:(4)EDi=∑i=1zEiEjdij.Although the links in the network are unstable and the nodes are in constant motion, the maximum access degree of the cluster heads is known since it is known. Therefore, we only need to predict the number of nonnumbers in the cluster head management region to determine the optimal topological distribution in that region. Furthermore, since the movement of nodes is generally regular or the movement of nodes is periodic, it is sufficient for us to predict the number of no quantities within just one cycle. After that, we construct the wireless optical network topology based on the MST algorithm (greedy algorithm). To learn the prediction of the nonquantity within the cluster head management region, the training set is necessary. Therefore, we need to record the number of non-s over some time by initializing the network as our training set. It then assumed that the number of non in the period to be predicted is correlated with the number of non in several precious moments:(5)Stf=w1Stf−w2S2f−⋯−wnSnf.Common methods using least-squares include finding the sum of squares of residuals, finding the squared difference, and finding the variance. This example uses finding the sum of squares of residuals to determine whether a preset threshold is satisfied, determine the parameters of the multiple linear regression model, and construct the multiple linear regression model. In this paper, we determine the number of hops of a multihop by finding the average node degree, thus achieving a fixed multihop model, which has many benefits. Firstly, for the movement of lower-level nodes, we do not need to manage for each lower-level node. We only need to know the number of lower-level nodes in the management area of each cluster head to give it the access service.


## 3.2. Experimental Design

In a two-way intersatellite laser link, the satellite laser communication terminal tracking device detects the tracking angle deviation of the link to achieve rapid link establishment and maintenance. After the laser link is established, under the large closed-loop condition of the tracking link, the tracking beams emitted by the two terminals will be correlated in the far field, and we consider that the far-field dynamic characteristics of the beams are correlated at this time [18]. Disturbed by the divergence angle, wavefront aberration, and the spatial environment, the beams will produce tracking angle deviations in the high-speed dynamics. In this section, a theoretical analysis model of the far-field dynamic characteristics of the interstellar link beam and a reference coordinate system for the far-field dynamic inertia of the beam are established to analyse the effect of the dynamic tracking angle deviation on the correlation of the far-field dynamic characteristics of the beam. To establish the laser link, two optical terminals are aimed at the other side simultaneously. First, the aiming device must complete the advance aiming and coarse tracking process using the known orbit parameters. Under the large closed-loop conditions of high-speed dynamic tracking, the tracking angular deviation generated by the tracking link causes the tracking beam to vary in the far-field dynamic characteristics.In the satellite laser tracking link, the satellite orbit and attitude information are inaccurate due to the long-distance and relative motion of the two terminals, and the autonomous turntable tracking method is used, where two optical terminals simultaneously track the tracking beacon beam of the other optical terminal. Therefore, tracking angle deviations are detected in the receiving detectors of both optical terminals. The tracking angle deviation affects the far-field dynamic characteristics of the beam during the transmission of the beam emitted by the optical antenna through free space [19]. Based on the numerical simulation results, the data curves are fitted, and the curves of the detected light intensity at the FO point with the tracking angle deviation A are shown in Figure 3. When the tracking angle deviation is less than 6 μrad, the normalized far-field intensity distribution shows a linearly decreasing trend as a decrease; when it is larger than 6 μrad, the normalized intensity at the FO point tends to a constant value, indicating no correlation between the far field of the two link beams when the tracking angle deviation reaches a certain angle.Figure 3
Normalized light intensity versus angular deviation with tracking.When Programming Ability Test (PAT) enters the capture state, both ends are in the open-loop scanning state, which cannot be fed back to correct the errors generated in the scanning process. In the scanning state of the signal light to the uncertain region, the real scanning trajectory will deviate from the original set scanning trajectory due to the jitter of its visual axis, resulting in the scanning trajectory not being able to completely cover the uncertain region, which eventually affects the probability of successful capture [20]. To improve the coverage of the uncertain region by the scattering angle of the scanning beam, the overlap factor between adjacent scanning beams can be increased, or the missed scanning due to the jitter of the visual axis can be reduced as much as possible. Therefore, it is important to choose the appropriate overlap factor for the scan trajectory to cover the uncertain region based on reducing the jitter of the visual axis because the composite scanning method contains two parts of the overlap factor.The working sequence of the oscillator and the turntable in the composite scan is that the oscillator starts the spiral scan from the centre of the uncertain region, and then the turntable jumps to the next scan point after the scan is completed and continues until the scan trajectory completely covers the whole uncertain region and the motion trajectory of the turntable is a rectangular spiral scan during the whole process. The spiral scan time in the subregion has been analysed in the previous subsection, and this subsection mainly analyses the time of the turntable moving in the adjacent subregion and the number of steps to jump, to deduce the scan time of the whole compound scan. Compared to the two scanning modes of continuous scanning and stepping scanning, the vibrating spiral scanning within the subregion is suitable for continuous scanning and the rectangular spiral scanning by the turntable between adjacent subregions is more suitable for stepping scanning, which can be regarded as a special case of stepping scanning. Therefore, the composite scanning uses stepping scanning, and the stepping scanning equation is(6)ΔtI=2Lc−1Fac−2tres.The cooperative process of the coarse and fine alignment system is shown in Figure4. The coarse alignment process is shown in Figure 4. The upper computer at the receiving end performs image processing on the light spot on the rear projection screen captured by the CCD camera. It uses the light spot position information obtained from the processing as the feedback quantity to drive the 2D aiming head at the transmitting end to control the antenna pointing for coarse alignment of the beam. However, due to the limited execution accuracy of the 2D aiming head, the beam cannot be precisely aligned with the viewing axis of the receiving antenna. Hence, the main purpose of the coarse alignment is to move the dynamic range of the fine 2D piezo platform to a range that can contain the target bullseye for the effective execution of the fine alignment stage. The fine alignment process is shown in Figure 4. After completing the coarse alignment work, the target bullseye is contained within the executable range of the piezoelectric stage. Then, the pointing of the beam can be controlled by the piezoelectric microdynamics stage to further eliminate the residuals left by the coarse alignment.Figure 4
Schematic diagram of the coarse and fine alignment process.Numerous problems within the optoelectronic tracking system involve optimization problems. The deep learning framework for target detection and tracking algorithm training requires the use of optimization algorithms. Online learning of target tracking requires the use of suitable optimization algorithms to reduce the loss function. Similarly, numerous optimization scenarios within the optoelectronic system require optimization, such as adaptive optics and fiber coupling. Adaptive optics requires multidimensional optimization that can enable clear imaging in optoelectronic tracking systems, making image quality much better, and reducing the difficulty of target detection and target tracking algorithms [21]. Fiber-optic coupling is a simplified version of adaptive optics, and generally, needles are optimized in only two dimensions. For the many scenarios that require optimization, many are complex problems that cannot be modelled and require high real-time algorithms and most solutions are modelled directly using model-free optimization algorithms, which construct the optimization target as a suitable objective function and optimize the objective function using the relevant model-free algorithm to make it optimal.


## 4. Analysis of Results

### 4.1. Algorithm Performance Results

Figure5 depicts the change in topology during the same update cycle. From Figure 5, it can be seen that all the algebraic connectivity in the network is greater than zero as time is delayed, and according to the previous definition of algebraic connectivity, as long as the algebraic connectivity is greater than zero, it means that the network is connected, which means that all the nodes in the network have at least one communication link to reach other nodes. There are no nodes in the network that cannot communicate with other nodes. The overall network is connected, and there is at least one communicable link between nodes. As this paper uses the Lightning Data Transport (LDT) algorithm to form a Tyson polygon, which is a topology without overlapping regions and blind areas, it can be found that the topology formed by the Transaction Certificate Machine Learning (TC-ML) algorithm is also without overlapping regions and blind areas. In other words, there are no isolated nodes and every node is connected to the network, which means that the network topology we build is fully connected. The network is more uniformly distributed because the establishment of links between nodes is based on the maximum access degree and the lower layer topology formation algorithm. In addition, it can be seen from Figure 5 that the algebraic connectivity in the same cycle is the same, which means that the algebraic connectivity of the network does not change, indicating that the topology of the network does not change due to the short movement time of the nodes in the same cycle and the topology formed by the TC-ML algorithm has high stability.Figure 5
Algebraic connectivity at different moments.However, as time passes, the topology of the nodes changes significantly in different cycles, and the algebraic connectivity of the network decreases significantly due to the long movement time of the nodes and the large changes in the relative positions of the nodes. However, the network’s connectivity is still greater than zero, indicating that the network is still connected because this paper uses a dynamic management algorithm to reconfigure the topology of the moving nodes and takes advantage of the local dynamics of the Tyson polygon, which is mainly used in this paper to reconfigure the topology locally rather than in its entirety. Therefore, the topology of the network remains stable.From the previous theoretical analysis, the increase in the number of nodes in the network due to the increase in the size of the network leads to a sharp decrease in the connectivity of the network due to the maximum access of the nodes and the energy constraints. This conclusion can also be obtained from Figure6. It is obvious that, as the number of nodes in the network increases, the algebraic connectivity of the topology formed by both MST algorithm and LDT and TC-ML algorithms decreases, indicating that the connectivity of the network is decreasing. This is because as the number of nodes increases, the nodes need to access the network, the maximum access and energy of the nodes in the network are determined, and the nodes in the network are bound to break the links with other nodes to allow the nodes to access to the network, which leads to the decrease in the connectivity of the network. However, although the TC-ML algorithm algebraic connectivity also has a decreasing trend, the algebraic connectivity is still greater than 0.05, indicating that the topology formed by the TC-ML algorithm is still connected. There are no isolated nodes, which is due to the dynamic management algorithm used by the TC-ML algorithm to adjust the network by locally reconfiguring the network, which is a huge improvement compared to other algorithms. This property of the TC-ML algorithm also makes the TC-ML algorithm much more scalable and stable than other algorithms, which is of pivotal importance for topology control of the network.Figure 6
Algorithm algebraic connectivity versus the number of nodes.Since algebraic connectivity is used as an important metric to evaluate the network, it is not only related to the number of nodes but also the maximum access degree of the nodes. For this reason, we also further simulated the variation of algebraic connectivity with the maximum access of nodes 0K in the same scenario. As analysed earlier, the main factors affecting the algebraic connectivity with the increase of the number of nodes are because of the maximum access of nodes and energy. In contrast, for the energy we discuss below, the effect of the maximum access on the algebraic connectivity is shown in Figure6. When the maximum access of nodes starting from 6 gradually increases, the trend of the algebraic connectivity of the network topology is formed by the three algorithms. As each node of the topology formed by the MST algorithm has only one optimal link, there is no redundant link, the increase in the maximum access degree 0K does not affect the topology formed by it. Figure 6 also shows that the algebraic connectivity of the network’s topology formed by the MST algorithm has little relationship with the maximum access degree 0K of the nodes. As the LDT algorithm forms a triangle, there are two edges, which means that there are two links in the topology formed by the LDT algorithm. Because 6 is greater than 2, the algebraic connectivity of the LDT algorithm does not change as the maximum access of the node 0K increases for greater than 2.


### 4.2. Experimental Results of Fast Capture Tracking of Laser Links between Nonorbiting Platforms

Experimental tests are conducted for the far-field spot canter of mass and the light intensity distribution under different link beam far-field wavefront aberrations for a certain divergence angle. The laser is required to provide a stable light source, and the phase screen is loaded into a spatial light modulator to produce a tracking beacon light with controlled wavefront aberration. The SLM is set to update the data at a rate of 50 Hz for a specific value of the RMS of the wavefront aberration, and 200 sets of data are generated. The spot canter-of-mass offset variance measurements for different RMS wavefront aberrations are shown in Figure7.Figure 7
Effect of wavefront distortion root mean square on the deviation of the canter of mass.Three wavelengths of lasers were used to generate tracking beacon light at 570 nm, 635 nm, and 808 nm. The relative position of the canter-of-mass deviation was measured and analysed by the beam far-field detection system for different degrees of wavefront aberrations. The analysis results showed that the wavelength had a significant influence on the relative position of the canter-of-mass deviation of the detected spot. The larger the wavelength, the smaller the relative position of the canter-of-mass deviation. The far-field dynamic characteristics of the beam are not significantly affected by the wavelength under the influence of a small degree of wavefront aberration RMS but more significantly affected by the wavelength when RMS/λ > 0.2.Because of the higher repetition rate of the phase in the single-layer phase screen, a comparison of the phase structure functions of the single-layer phase screen and the 10-layer phase screen reveals that the phase structure function of the 10-layer phase screen is closer to the theoretical value. Low-frequency variations in atmospheric turbulence can cause a drift in the canter of the tracking far-field spot. Experimental verification is performed for the effect of beam drift on the far-field dynamic characteristics of the link beam. The reference light is introduced during the experiment, which is emitted from the same laser and expanded, and an incident in a spatial light modulator Service Level Management (SLM) is reflected and detected in a Charge-Coupled Device (CCD) by an imaging system. The beam drift perturbation resulting from the influence of the atmospheric channel on the tracking beam is shown in Figure8.Figure 8
Beam drift.As shown in Figure8, during the experiment, a set of random phase screens was generated by the above phase screen generation method. The number was selected as 50, which were superimposed with the phase diaphragm of the tracking beam to generate different phase diaphragms for simulating the intensity distribution observed at the convergence point of the intensity distribution of the lens focus. The smallest spot canter-of-mass offset is 2.45 μm, the average drift of the beam drift is 7.12 μm, and the random distribution obeys the Rayleigh distribution, which verifies the correctness of the theoretical model. The distribution of the beam far-field characteristics is of great significance to the stable tracking of the satellite laser link, and the measurement of the beam far-field spot canter of mass is necessary. The distribution of the beam far-field spot centre of mass in the detector receiving field of view affects the calculation of the tracking angle deviation of the tracking control system and directly reflects the good or bad stability performance of the tracking system. Usually, the distance of the laser link is greater than 1000 km, so it is not practical to measure the beam far-field spot directly, and the ground far-field distance simulation experiments are usually below 50 km.Atmospheric turbulence effects lead to drift, expansion, and intensity undulations of the link beam. At the same time, wave phase differences exist in the link beam itself, making the distribution of the far-field spot of the beam more complex. Due to space limitations, the transient distribution of the far-field dynamic properties of the tracking beacon beam is simulated in a Monte Carlo method for the far-field dynamic properties of the link beam, simulating the transient spot centre-of-mass distribution of the spot.


## 4.1. Algorithm Performance Results

Figure5 depicts the change in topology during the same update cycle. From Figure 5, it can be seen that all the algebraic connectivity in the network is greater than zero as time is delayed, and according to the previous definition of algebraic connectivity, as long as the algebraic connectivity is greater than zero, it means that the network is connected, which means that all the nodes in the network have at least one communication link to reach other nodes. There are no nodes in the network that cannot communicate with other nodes. The overall network is connected, and there is at least one communicable link between nodes. As this paper uses the Lightning Data Transport (LDT) algorithm to form a Tyson polygon, which is a topology without overlapping regions and blind areas, it can be found that the topology formed by the Transaction Certificate Machine Learning (TC-ML) algorithm is also without overlapping regions and blind areas. In other words, there are no isolated nodes and every node is connected to the network, which means that the network topology we build is fully connected. The network is more uniformly distributed because the establishment of links between nodes is based on the maximum access degree and the lower layer topology formation algorithm. In addition, it can be seen from Figure 5 that the algebraic connectivity in the same cycle is the same, which means that the algebraic connectivity of the network does not change, indicating that the topology of the network does not change due to the short movement time of the nodes in the same cycle and the topology formed by the TC-ML algorithm has high stability.Figure 5
Algebraic connectivity at different moments.However, as time passes, the topology of the nodes changes significantly in different cycles, and the algebraic connectivity of the network decreases significantly due to the long movement time of the nodes and the large changes in the relative positions of the nodes. However, the network’s connectivity is still greater than zero, indicating that the network is still connected because this paper uses a dynamic management algorithm to reconfigure the topology of the moving nodes and takes advantage of the local dynamics of the Tyson polygon, which is mainly used in this paper to reconfigure the topology locally rather than in its entirety. Therefore, the topology of the network remains stable.From the previous theoretical analysis, the increase in the number of nodes in the network due to the increase in the size of the network leads to a sharp decrease in the connectivity of the network due to the maximum access of the nodes and the energy constraints. This conclusion can also be obtained from Figure6. It is obvious that, as the number of nodes in the network increases, the algebraic connectivity of the topology formed by both MST algorithm and LDT and TC-ML algorithms decreases, indicating that the connectivity of the network is decreasing. This is because as the number of nodes increases, the nodes need to access the network, the maximum access and energy of the nodes in the network are determined, and the nodes in the network are bound to break the links with other nodes to allow the nodes to access to the network, which leads to the decrease in the connectivity of the network. However, although the TC-ML algorithm algebraic connectivity also has a decreasing trend, the algebraic connectivity is still greater than 0.05, indicating that the topology formed by the TC-ML algorithm is still connected. There are no isolated nodes, which is due to the dynamic management algorithm used by the TC-ML algorithm to adjust the network by locally reconfiguring the network, which is a huge improvement compared to other algorithms. This property of the TC-ML algorithm also makes the TC-ML algorithm much more scalable and stable than other algorithms, which is of pivotal importance for topology control of the network.Figure 6
Algorithm algebraic connectivity versus the number of nodes.Since algebraic connectivity is used as an important metric to evaluate the network, it is not only related to the number of nodes but also the maximum access degree of the nodes. For this reason, we also further simulated the variation of algebraic connectivity with the maximum access of nodes 0K in the same scenario. As analysed earlier, the main factors affecting the algebraic connectivity with the increase of the number of nodes are because of the maximum access of nodes and energy. In contrast, for the energy we discuss below, the effect of the maximum access on the algebraic connectivity is shown in Figure6. When the maximum access of nodes starting from 6 gradually increases, the trend of the algebraic connectivity of the network topology is formed by the three algorithms. As each node of the topology formed by the MST algorithm has only one optimal link, there is no redundant link, the increase in the maximum access degree 0K does not affect the topology formed by it. Figure 6 also shows that the algebraic connectivity of the network’s topology formed by the MST algorithm has little relationship with the maximum access degree 0K of the nodes. As the LDT algorithm forms a triangle, there are two edges, which means that there are two links in the topology formed by the LDT algorithm. Because 6 is greater than 2, the algebraic connectivity of the LDT algorithm does not change as the maximum access of the node 0K increases for greater than 2.


## 4.2. Experimental Results of Fast Capture Tracking of Laser Links between Nonorbiting Platforms

Experimental tests are conducted for the far-field spot canter of mass and the light intensity distribution under different link beam far-field wavefront aberrations for a certain divergence angle. The laser is required to provide a stable light source, and the phase screen is loaded into a spatial light modulator to produce a tracking beacon light with controlled wavefront aberration. The SLM is set to update the data at a rate of 50 Hz for a specific value of the RMS of the wavefront aberration, and 200 sets of data are generated. The spot canter-of-mass offset variance measurements for different RMS wavefront aberrations are shown in Figure7.Figure 7
Effect of wavefront distortion root mean square on the deviation of the canter of mass.Three wavelengths of lasers were used to generate tracking beacon light at 570 nm, 635 nm, and 808 nm. The relative position of the canter-of-mass deviation was measured and analysed by the beam far-field detection system for different degrees of wavefront aberrations. The analysis results showed that the wavelength had a significant influence on the relative position of the canter-of-mass deviation of the detected spot. The larger the wavelength, the smaller the relative position of the canter-of-mass deviation. The far-field dynamic characteristics of the beam are not significantly affected by the wavelength under the influence of a small degree of wavefront aberration RMS but more significantly affected by the wavelength when RMS/λ > 0.2.Because of the higher repetition rate of the phase in the single-layer phase screen, a comparison of the phase structure functions of the single-layer phase screen and the 10-layer phase screen reveals that the phase structure function of the 10-layer phase screen is closer to the theoretical value. Low-frequency variations in atmospheric turbulence can cause a drift in the canter of the tracking far-field spot. Experimental verification is performed for the effect of beam drift on the far-field dynamic characteristics of the link beam. The reference light is introduced during the experiment, which is emitted from the same laser and expanded, and an incident in a spatial light modulator Service Level Management (SLM) is reflected and detected in a Charge-Coupled Device (CCD) by an imaging system. The beam drift perturbation resulting from the influence of the atmospheric channel on the tracking beam is shown in Figure8.Figure 8
Beam drift.As shown in Figure8, during the experiment, a set of random phase screens was generated by the above phase screen generation method. The number was selected as 50, which were superimposed with the phase diaphragm of the tracking beam to generate different phase diaphragms for simulating the intensity distribution observed at the convergence point of the intensity distribution of the lens focus. The smallest spot canter-of-mass offset is 2.45 μm, the average drift of the beam drift is 7.12 μm, and the random distribution obeys the Rayleigh distribution, which verifies the correctness of the theoretical model. The distribution of the beam far-field characteristics is of great significance to the stable tracking of the satellite laser link, and the measurement of the beam far-field spot canter of mass is necessary. The distribution of the beam far-field spot centre of mass in the detector receiving field of view affects the calculation of the tracking angle deviation of the tracking control system and directly reflects the good or bad stability performance of the tracking system. Usually, the distance of the laser link is greater than 1000 km, so it is not practical to measure the beam far-field spot directly, and the ground far-field distance simulation experiments are usually below 50 km.Atmospheric turbulence effects lead to drift, expansion, and intensity undulations of the link beam. At the same time, wave phase differences exist in the link beam itself, making the distribution of the far-field spot of the beam more complex. Due to space limitations, the transient distribution of the far-field dynamic properties of the tracking beacon beam is simulated in a Monte Carlo method for the far-field dynamic properties of the link beam, simulating the transient spot centre-of-mass distribution of the spot.


## 5. Conclusion

A quantitative analysis method for the in-orbit tracking stability of laser links is proposed, which provides an effective means for link tracking stability analysis. The effectiveness of the link tracking stability analysis method is verified through ground experiments. The ground equivalence experiments are used to verify the correctness of the established theoretical analysis model of the far-field dynamic characteristics of the beam. The semiphysical simulation experiments are used to verify the link tracking stability optimization algorithm. The correctness of the optimization algorithm is verified. The experimental results illustrate that the stable capture and tracking algorithm can effectively improve the stability of the bidirectional laser link. The effectiveness of the above terminal capture and tracking control system design is also verified by indoor and outfield laser communication experiments.



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*Source: 9296770-2022-01-21.xml*