# Research on Data Fusion of Photoelectric Measuring Instrument Based on UKF Algorithm

Authors: Tang Yikang
Journal: Security and Communication Networks
 (2022)

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## Abstract

The UKF algorithm-based data fusion technique was researched in this work in order to tackle the issues of conventional data fusion methods such as lengthy fusion processes, high recall rates of findings, and big errors. First, the photoelectric measuring principle is examined in this procedure. On the basis of UKF filtering operation on the data sample set, the fusion processing of photoelectric measurement instrument is completed through the steps of data pretreatment, vertical and horizontal fusion, and fusion quality optimization. The experimental results show that the fusion process time of this method is between 0.27 min and 0.40 min, the recall rate of the fusion result is always lower than 6%, and the fusion deviation is small, indicating that the design expectation is achieved.


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## Body

## 1. Introduction

In recent years, with the continuous optimization of information technology, related optical fiber communication materials and optoelectronic devices have also been optimized, so that the quality and measurement effect of photoelectric measurement technology have been continuously improved [1]. Compared with other detection methods, photoelectric measurement has gained more applications in some high-voltage environments because of its advantages of high precision and strong anti-interference.Photographic instruments, sometimes known as photoelectric measuring instruments, are often utilised in hazardous or difficult-to-reach locations for other reasons [2]. Although photoelectric measuring devices include sensors that are subject to interference from network environments, the natural environment, and other sounds, the divergence of measurement data grows to some degree [3]. As a result, the data from the photoelectric measuring device must be combined.Therefore, referring to the multi-source data point matching process [4], a method of deep data fusion for photoelectric measurement is designed. In this method, the collected photoelectric measurement data are denoised and preprocessed, and then the features of the obtained photoelectric measurement data are presented in the format of reference time series by using kernel principal component analysis (PCA). Then, on the basis of determining the correlation of different sequence data, the time point features of the data are matched so as to complete the deep fusion of the data. However, in practical application, it is found that the fusion method based on time point matching of multi-source data has the problem of high recall rate of fusion results, which also reflects the low fusion accuracy of the method to a certain extent. Zhang et al. [5] designed a data fusion method based on the division of independent regions and compressed sensing. This method firstly collects photoelectric measurement data by using compressed sensing theory, then divides independent data regions, and builds data union regions by means of load balancing. Then, on the basis of calculating the weight coefficient of data fusion, the data fusion is completed by combining the reconstruction process of compressed sensing coefficient. However, the fusion process of this method takes a long time, which proves that its timeliness is poor. Yao et al. [6] designed a data fusion method based on probabilistic routing. In this method, the data transmission process is guided by the optimization of the chaotic ant colony algorithm. While the communication load is reduced, the similar fusion structure is obtained by gradually converging the routing structure, and the fusion process is implemented. However, this method has the problem of large fusion error and low practicability.Aiming at the problems of the above traditional data fusion methods, such as long time in the fusion process, high recall rate of fusion results, and large error, this study designed a new data fusion method for photoelectric measuring instruments based on the analysis of UKF filtering process.


## 2. Analysis of Photoelectric Measurement Principle and Application of UKF Algorithm

### 2.1. Analysis of Photoelectric Measurement Principle

The hardware base of photoelectric measurement is optical fiber device. Photoelectric measuring instruments first transform the observed item into an electrical signal, which is then processed by laser amplification and statistical computation to provide the final measurement result. Because the optical fiber material is used as a signal transmission medium, photoelectric measurement has the advantages of high accuracy, fast speed, and long-distance operation; especially in the operating environment with extremely high voltage, the photoelectric measurement technology has been widely used [7, 8]. The operating principle of the photoelectric measuring instrument is shown in Figure 1.Figure 1
Operation principle diagram of the photoelectric measuring instrument.In order to effectively improve the validity and reliability of photoelectric measurement results, this study designed a data fusion method using UKF algorithm in the process of electrical signal processing.


### 2.2. Application of UKF Algorithm

The unscented Kalman filter (UKF) is obtained by adding lossless transformation on the basis of the classical Kalman filter, which solves the problems of low precision and poor stability of the classical Kalman filter [9, 10].The central idea of UKF is that there is no trace change, and it can carry out effective sampling according to the original data state, so as to ensure that the eigenvalues of the collected data sample set, especially the data mean and covariance, are the same as the original data state [11]. On this basis, the eigenvalues of the collected data are calculated by nonlinear functions. This process is not only to perform Taylor expansion operation on nonlinear functions but also to obtain accurate function values by using statistical characteristics, so as to effectively avoid truncation errors in the operation of the classical Kalman filter.Like the classical Kalman filter, UKF algorithm also needs to estimate the minimum variance of data sample set. Different from the classical Kalman filter algorithm, UKF algorithm calculates the data relation matrix by predicting the mean square error and prior information of the data sample set, so as to complete iteration and recursion in the operation process. The specific flow of UKF algorithm is as follows.Step 1.
Collect a group of data samples with weights through untraced transformation, in which the amount of data is2n+1, that is, the generated sigma points are as follows:(1)Xi2n+1k=xi+n+∂Pkxi+n+∂Pk,where ∂ represents the scale ratio column, whose function is to reduce the data collection and observation error, P represents the prior state estimation value of the data, and k represents the sampling time.Step 2.
Calculate the predicted value of each data sample point, and the process is as follows:(2)Xi2n+1k+1=xin+∂Pkxi2n+1,where k+1 represents the next sampling moment of k.Step 3.
Calculate the state value and covariance matrix of data atk+1, and the process is as follows:(3)fk+1=∑i=12n+1ωixi,Covi,i+1=∑i=12n+1ωixik+1−xik,where ωi represents the weight coefficient corresponding to the data.Step 4.
On the basis of untraced transformation, new sigma points are generated according to the data state values atk+1, and the process is as follows:(4)Xi2n+1k+1=xi+n+∂Pk+1xi+n+∂Pk+1.Step 5.
Substitute the results of formula (4) into the nonlinear equation, and the predicted values of the data are as follows:(5)Zi2n+1k+1=hXi2n+1k+1,where h stands for the nonlinear function.Step 6.
Calculate the Kalman filter gain, and the process is as follows:(6)κ=Zi2n+1k+1−Xi2n+1k+1.Step 7.
Update the state quantity and covariance of data iteratively, and the process is as follows:(7)fk+1′=κ∑i=12n+1ωixi,Covi,i+1′=κ∑i=12n+1ωixik+1−xik.
In summary, the UKF filtering operation on the data sample set is completed, and its tracking and state prediction effect on the data target is effectively optimized. The validity and reliability of the data sample set of the photoelectric measuring instrument obtained by UKF filtering algorithm are also greatly improved.


## 2.1. Analysis of Photoelectric Measurement Principle

The hardware base of photoelectric measurement is optical fiber device. Photoelectric measuring instruments first transform the observed item into an electrical signal, which is then processed by laser amplification and statistical computation to provide the final measurement result. Because the optical fiber material is used as a signal transmission medium, photoelectric measurement has the advantages of high accuracy, fast speed, and long-distance operation; especially in the operating environment with extremely high voltage, the photoelectric measurement technology has been widely used [7, 8]. The operating principle of the photoelectric measuring instrument is shown in Figure 1.Figure 1
Operation principle diagram of the photoelectric measuring instrument.In order to effectively improve the validity and reliability of photoelectric measurement results, this study designed a data fusion method using UKF algorithm in the process of electrical signal processing.


## 2.2. Application of UKF Algorithm

The unscented Kalman filter (UKF) is obtained by adding lossless transformation on the basis of the classical Kalman filter, which solves the problems of low precision and poor stability of the classical Kalman filter [9, 10].The central idea of UKF is that there is no trace change, and it can carry out effective sampling according to the original data state, so as to ensure that the eigenvalues of the collected data sample set, especially the data mean and covariance, are the same as the original data state [11]. On this basis, the eigenvalues of the collected data are calculated by nonlinear functions. This process is not only to perform Taylor expansion operation on nonlinear functions but also to obtain accurate function values by using statistical characteristics, so as to effectively avoid truncation errors in the operation of the classical Kalman filter.Like the classical Kalman filter, UKF algorithm also needs to estimate the minimum variance of data sample set. Different from the classical Kalman filter algorithm, UKF algorithm calculates the data relation matrix by predicting the mean square error and prior information of the data sample set, so as to complete iteration and recursion in the operation process. The specific flow of UKF algorithm is as follows.Step 1.
Collect a group of data samples with weights through untraced transformation, in which the amount of data is2n+1, that is, the generated sigma points are as follows:(1)Xi2n+1k=xi+n+∂Pkxi+n+∂Pk,where ∂ represents the scale ratio column, whose function is to reduce the data collection and observation error, P represents the prior state estimation value of the data, and k represents the sampling time.Step 2.
Calculate the predicted value of each data sample point, and the process is as follows:(2)Xi2n+1k+1=xin+∂Pkxi2n+1,where k+1 represents the next sampling moment of k.Step 3.
Calculate the state value and covariance matrix of data atk+1, and the process is as follows:(3)fk+1=∑i=12n+1ωixi,Covi,i+1=∑i=12n+1ωixik+1−xik,where ωi represents the weight coefficient corresponding to the data.Step 4.
On the basis of untraced transformation, new sigma points are generated according to the data state values atk+1, and the process is as follows:(4)Xi2n+1k+1=xi+n+∂Pk+1xi+n+∂Pk+1.Step 5.
Substitute the results of formula (4) into the nonlinear equation, and the predicted values of the data are as follows:(5)Zi2n+1k+1=hXi2n+1k+1,where h stands for the nonlinear function.Step 6.
Calculate the Kalman filter gain, and the process is as follows:(6)κ=Zi2n+1k+1−Xi2n+1k+1.Step 7.
Update the state quantity and covariance of data iteratively, and the process is as follows:(7)fk+1′=κ∑i=12n+1ωixi,Covi,i+1′=κ∑i=12n+1ωixik+1−xik.
In summary, the UKF filtering operation on the data sample set is completed, and its tracking and state prediction effect on the data target is effectively optimized. The validity and reliability of the data sample set of the photoelectric measuring instrument obtained by UKF filtering algorithm are also greatly improved.


## 3. Data Fusion of Photoelectric Measuring Instrument

Based on data pretreatment, the UKF filtering method is used to get a trustworthy data sample set from a photoelectric measuring equipment.


### 3.1. Data Preprocessing of Photoelectric Measuring Instrument

Photoelectric measuring tools are equipped with several small sensors that can capture a great quantity of data on a variety of objects [12]. Diverse types of data significantly increase the challenge of data fusion. As a result, prior to doing the fusion processing, this work performs preprocessing on the photoelectric measuring instrument data.The preprocessing process of photoelectric measuring instrument data mainly includes data cache, processing rule configuration, and data processing.(i)
Data cache: this step creates a temporary table matching different types of optoelectronic measuring instrument data. The sampling frequency of the data in the temporary table is the same as that of the sensor that collects this kind of data.(ii)
Processing rule configuration: the main function of this step is to configure rectifying rules and compile the rectifying rules into the running program of the database, so as to avoid affecting the subsequent fusion quality due to data deviation.(iii)
Data processing: in this step, the data of different types of photoelectric measuring instruments are processed one by one according to the data correction rules. The specific data preprocessing process of the photoelectric measuring instrument is shown in Figure2.Figure 2
Schematic diagram of data preprocessing of the photoelectric measuring instrument.As shown in Figure2, the original data samples of photoelectric measuring instrument are stored in the temporary table at first, then the data samples of photoelectric measuring instrument are extracted, and the state of data is judged according to the data correction rules on the basis of data traversal. If there are abnormal data, the data are processed and the correction amount is recorded.


### 3.2. Vertical and Horizontal Data Fusion Processing

In this paper, the data fusion of the photoelectric measuring instrument is realized from two perspectives: vertical fusion and horizontal fusion. It can save processing time and improve fusion effect through multi-angle fusion. The data fusion process is shown in Figure3.Figure 3
Schematic diagram of data fusion process of the photoelectric measuring instrument.The main function of the longitudinal data fusion process is to reduce the differences between different types of data, so as to realize the unification of data in the longitudinal perspective [13]. The longitudinal fusion procedure requires the collection of photoelectric measuring instrument data parameters through a Web Service interface, classification, and storage. At the conclusion of data collection, the parameters of photoelectric measuring instruments are matched to the data collected from various sensors. It is important to detect the parameter states in the data source used for matching and to assess the differences between various kinds of data by adjusting the difference degree of longitudinal data fusion throughout the matching process.Supposeδj,i represents the integration degree of the i-th data parameter from the j-th sensor in the photoelectric measuring instrument:(8)δj,i=δi,max−δi,minδ¯×100%,where δi,min and δi,max represent the minimum and maximum values of data parameters, respectively, and δ¯ represents the mean value of data parameters. At the same time, due to the complexity of photoelectric measurement data, the parameters of photoelectric measurement instrument data have different fusion forms in different data scheduling centers. Therefore, specific data parameter thresholds should be set according to relevant fusion rules to reduce the negative influence of data fusion form on vertical fusion.Horizontal data fusion can break through the limitations of vertical data fusion in data source processing, so as to realize the fusion of data from the same source. Different from the vertical fusion process, the horizontal data fusion process has a stronger ability to contain data differences [14, 15]. Therefore, it can use SNMP interface to collect data parameters of the photoelectric measuring instrument, which is precisely because Web Service interface cannot meet the requirements of horizontal data collection and cannot eliminate the difference between data source and data scheduling center. After the data parameters of the photoelectric measuring instrument are collected, the horizontal data parameters are matched to complete the horizontal data fusion.


### 3.3. Data Fusion Quality Optimization

In order to further reduce the deviation between vertical data fusion and horizontal data fusion, a deviation functionEp is specified in this study, which is used to calculate the deviation of the whole fusion process. The calculation process is as follows:(9)Ep=1−φjPj+Tj,where φj represent the fusion weights of different types of data, Pj represent the longitudinal data fusion processing results, and Tj represent the horizontal data fusion processing results. After data fusion bias is eliminated, a decision matrix is constructed and expressed as follows:(10)Ri,j=1,Ep>0.3,0,otherwise,where R represents the fusion rule of homologous data. When the fusion deviation is greater than 0, the degree of data fusion of the photoelectric measuring instrument is calculated by referring to different fusion deviation values and expressed in percentage form, as shown in Table 1.Table 1
The percentage of fusion degree corresponding to different fusion deviations.
No.Fusion deviationDegree of integration/%10.186.620.277.330.373.740.465.450.560.860.655.770.750.280.841.690.939.4101.026.5According to the percentage value for the degree of fusion displayed in Table1, the greater the deviation value is, the harsher the impact of data fusion will be. In order to enhance the robustness of the data fusion process, this study uses principal component analysis to reduce the dimension of the fusion deviation and control the value of the fusion deviation. The principal component analysis process can be expressed as follows:(11)A=minγη−γF,where η represents the fusion deviation matrix, γ represents the low-order matrix, and F represents the norm of the low-order matrix. Assuming that the fusion deviation data matrix is low-rank, the matrix information is decomposed into low-rank component and sparse component within the given deviation range [16], and the low-rank component and sparse component are restored using the norm optimization problem, so as to further eliminate the influence of the fusion result deviation, forming the optimized fusion process as follows:(12)D=minΔAη−Ri,j2γF2,where Δ represents the updating coefficient of fusion deviation.According to the above optimized fusion calculation formula, the effective fusion processing of photoelectric measuring instrument data is realized.


## 3.1. Data Preprocessing of Photoelectric Measuring Instrument

Photoelectric measuring tools are equipped with several small sensors that can capture a great quantity of data on a variety of objects [12]. Diverse types of data significantly increase the challenge of data fusion. As a result, prior to doing the fusion processing, this work performs preprocessing on the photoelectric measuring instrument data.The preprocessing process of photoelectric measuring instrument data mainly includes data cache, processing rule configuration, and data processing.(i)
Data cache: this step creates a temporary table matching different types of optoelectronic measuring instrument data. The sampling frequency of the data in the temporary table is the same as that of the sensor that collects this kind of data.(ii)
Processing rule configuration: the main function of this step is to configure rectifying rules and compile the rectifying rules into the running program of the database, so as to avoid affecting the subsequent fusion quality due to data deviation.(iii)
Data processing: in this step, the data of different types of photoelectric measuring instruments are processed one by one according to the data correction rules. The specific data preprocessing process of the photoelectric measuring instrument is shown in Figure2.Figure 2
Schematic diagram of data preprocessing of the photoelectric measuring instrument.As shown in Figure2, the original data samples of photoelectric measuring instrument are stored in the temporary table at first, then the data samples of photoelectric measuring instrument are extracted, and the state of data is judged according to the data correction rules on the basis of data traversal. If there are abnormal data, the data are processed and the correction amount is recorded.


## 3.2. Vertical and Horizontal Data Fusion Processing

In this paper, the data fusion of the photoelectric measuring instrument is realized from two perspectives: vertical fusion and horizontal fusion. It can save processing time and improve fusion effect through multi-angle fusion. The data fusion process is shown in Figure3.Figure 3
Schematic diagram of data fusion process of the photoelectric measuring instrument.The main function of the longitudinal data fusion process is to reduce the differences between different types of data, so as to realize the unification of data in the longitudinal perspective [13]. The longitudinal fusion procedure requires the collection of photoelectric measuring instrument data parameters through a Web Service interface, classification, and storage. At the conclusion of data collection, the parameters of photoelectric measuring instruments are matched to the data collected from various sensors. It is important to detect the parameter states in the data source used for matching and to assess the differences between various kinds of data by adjusting the difference degree of longitudinal data fusion throughout the matching process.Supposeδj,i represents the integration degree of the i-th data parameter from the j-th sensor in the photoelectric measuring instrument:(8)δj,i=δi,max−δi,minδ¯×100%,where δi,min and δi,max represent the minimum and maximum values of data parameters, respectively, and δ¯ represents the mean value of data parameters. At the same time, due to the complexity of photoelectric measurement data, the parameters of photoelectric measurement instrument data have different fusion forms in different data scheduling centers. Therefore, specific data parameter thresholds should be set according to relevant fusion rules to reduce the negative influence of data fusion form on vertical fusion.Horizontal data fusion can break through the limitations of vertical data fusion in data source processing, so as to realize the fusion of data from the same source. Different from the vertical fusion process, the horizontal data fusion process has a stronger ability to contain data differences [14, 15]. Therefore, it can use SNMP interface to collect data parameters of the photoelectric measuring instrument, which is precisely because Web Service interface cannot meet the requirements of horizontal data collection and cannot eliminate the difference between data source and data scheduling center. After the data parameters of the photoelectric measuring instrument are collected, the horizontal data parameters are matched to complete the horizontal data fusion.


## 3.3. Data Fusion Quality Optimization

In order to further reduce the deviation between vertical data fusion and horizontal data fusion, a deviation functionEp is specified in this study, which is used to calculate the deviation of the whole fusion process. The calculation process is as follows:(9)Ep=1−φjPj+Tj,where φj represent the fusion weights of different types of data, Pj represent the longitudinal data fusion processing results, and Tj represent the horizontal data fusion processing results. After data fusion bias is eliminated, a decision matrix is constructed and expressed as follows:(10)Ri,j=1,Ep>0.3,0,otherwise,where R represents the fusion rule of homologous data. When the fusion deviation is greater than 0, the degree of data fusion of the photoelectric measuring instrument is calculated by referring to different fusion deviation values and expressed in percentage form, as shown in Table 1.Table 1
The percentage of fusion degree corresponding to different fusion deviations.
No.Fusion deviationDegree of integration/%10.186.620.277.330.373.740.465.450.560.860.655.770.750.280.841.690.939.4101.026.5According to the percentage value for the degree of fusion displayed in Table1, the greater the deviation value is, the harsher the impact of data fusion will be. In order to enhance the robustness of the data fusion process, this study uses principal component analysis to reduce the dimension of the fusion deviation and control the value of the fusion deviation. The principal component analysis process can be expressed as follows:(11)A=minγη−γF,where η represents the fusion deviation matrix, γ represents the low-order matrix, and F represents the norm of the low-order matrix. Assuming that the fusion deviation data matrix is low-rank, the matrix information is decomposed into low-rank component and sparse component within the given deviation range [16], and the low-rank component and sparse component are restored using the norm optimization problem, so as to further eliminate the influence of the fusion result deviation, forming the optimized fusion process as follows:(12)D=minΔAη−Ri,j2γF2,where Δ represents the updating coefficient of fusion deviation.According to the above optimized fusion calculation formula, the effective fusion processing of photoelectric measuring instrument data is realized.


## 4. Simulation Experiment and Result Analysis

In order to verify the practical application performance of the data fusion method of the photoelectric measuring instrument designed above based on UKF filtering algorithm, the following simulation experiment process is designed.


### 4.1. Scheme Design

Set the frequency of the photoelectric measuring instrument to 5000 Hz and the measuring time to 1000 s. The photoelectric measuring instrument has a measuring accuracy of ±1”. Quick measurement can be started with a button trigger in the state of measurement or lofting measurement. Its data interface is Mini USB interface.In order to avoid convincing experimental results due to being too single, the fusion method based on time point matching of multi-source data in reference [4] and the fusion method based on independent region division and compressed sensing in reference [5] were used as comparison methods to complete performance verification together with the method in this paper.The application performance of the three fusion techniques was evaluated in the experimental process using the time required for fusion, the fusion deviation, and the recall rate of fusion findings as indicators. The shorter the fusion procedure, the more timely the fusion technique. The smaller the fusion deviation is, the more effective the fusion method is. The lower the recall rate of fusion results, the higher the accuracy of fusion results.


### 4.2. Analysis of Experimental Results

Firstly, the application performance of the proposed method, the fusion method based on time point matching of multi-source data, and the fusion method based on independent region division and compressed sensing is verified by taking the time of the fusion process as the test index. The time results of the fusion process of different methods are shown in Table2.Table 2
Time results of fusion process using different methods.
Number of experiments/timeFusion process time/minFusion method based on time point matching of multi-source dataFusion method based on independent region division and compressed sensingMethod in this paper100.560.890.27200.680.880.34300.700.850.36400.720.900.37500.731.030.40By analyzing the results shown in Table2, it can be seen that with the increase of the number of experiments, the fusion process takes between 0.56 min and 0.73 min after the fusion method based on time point matching of multi-source data is applied, and the fusion process takes between 0.27 min and 0.40 min after the method in this paper is applied. The time required to fuse these two approaches is constantly growing. However, when the fusion approach based on independent area division and compressed sensing is used, the fusion time varies between 0.89 and 1.03 minutes and exhibits a tendency of reducing initially and then rising. Through numerical comparison, it is clear that the approach described in this paper’s fusion process takes less time, suggesting that the method is more timely. Then, the application performance of the proposed method, the fusion method based on multi-source data time point matching, and the fusion method based on independent region partition and compressed sensing is verified with the fusion bias as the test index. The fusion deviations of different methods are shown in Figure 4.Figure 4
Statistical graph of fusion deviation of different methods.By analyzing the results shown in Figure4, it can be seen that the fusion deviations of the proposed method, the fusion method based on time point matching of multi-source data, and the fusion method based on independent region division and compressed sensing will all change with the increase of the number of experiments. When the number of experiments is 10, the fusion deviations of the fusion method based on time point matching of multi-source data and the fusion method based on independent region division and compressed sensing reach their minimum values, respectively. When the number of experiments is 30, the fusion deviation of the proposed method reaches its minimum value. By observing the curve changes in the figure, it can be seen that the fusion deviation curve of the proposed method is always below that of the two comparison methods, indicating that the fusion deviation of the proposed method is always lower than that of the two comparison methods. It can be seen from the class that the fusion deviation of the method in this paper is smaller, indicating that the method is more effective.Finally, the application performance of the proposed method, the fusion method based on multi-source data time point matching, and the fusion method based on independent region partition and compressed sensing is verified by the recall rate of fusion results. The fusion deviations of different methods are shown in Figure5.Figure 5
Statistical graph of recall rate of fusion results of different methods.According to the results shown in Figure5, with the increase of the number of experiments, the recall rate of fusion results of the proposed method, the fusion method based on time point matching of multi-source data, and the fusion method based on independent region division and compressed sensing also changed significantly. At first, the recall rate for fusion results based on multi-source data fusion point matching method was lower than the recall rate for fusion results based on independent division and compression perception, but as the number of experiments increased, the recall rate for fusion results based on multi-source data fusion point matching method increased rapidly, exceeding the recall rate for fusion results based on independent division and compression perception. However, by comparison, the recall rate of the fusion results of the proposed method is always lower than 6%, which proves that the fusion results of the proposed method are more accurate than those of the two comparison methods.


## 4.1. Scheme Design

Set the frequency of the photoelectric measuring instrument to 5000 Hz and the measuring time to 1000 s. The photoelectric measuring instrument has a measuring accuracy of ±1”. Quick measurement can be started with a button trigger in the state of measurement or lofting measurement. Its data interface is Mini USB interface.In order to avoid convincing experimental results due to being too single, the fusion method based on time point matching of multi-source data in reference [4] and the fusion method based on independent region division and compressed sensing in reference [5] were used as comparison methods to complete performance verification together with the method in this paper.The application performance of the three fusion techniques was evaluated in the experimental process using the time required for fusion, the fusion deviation, and the recall rate of fusion findings as indicators. The shorter the fusion procedure, the more timely the fusion technique. The smaller the fusion deviation is, the more effective the fusion method is. The lower the recall rate of fusion results, the higher the accuracy of fusion results.


## 4.2. Analysis of Experimental Results

Firstly, the application performance of the proposed method, the fusion method based on time point matching of multi-source data, and the fusion method based on independent region division and compressed sensing is verified by taking the time of the fusion process as the test index. The time results of the fusion process of different methods are shown in Table2.Table 2
Time results of fusion process using different methods.
Number of experiments/timeFusion process time/minFusion method based on time point matching of multi-source dataFusion method based on independent region division and compressed sensingMethod in this paper100.560.890.27200.680.880.34300.700.850.36400.720.900.37500.731.030.40By analyzing the results shown in Table2, it can be seen that with the increase of the number of experiments, the fusion process takes between 0.56 min and 0.73 min after the fusion method based on time point matching of multi-source data is applied, and the fusion process takes between 0.27 min and 0.40 min after the method in this paper is applied. The time required to fuse these two approaches is constantly growing. However, when the fusion approach based on independent area division and compressed sensing is used, the fusion time varies between 0.89 and 1.03 minutes and exhibits a tendency of reducing initially and then rising. Through numerical comparison, it is clear that the approach described in this paper’s fusion process takes less time, suggesting that the method is more timely. Then, the application performance of the proposed method, the fusion method based on multi-source data time point matching, and the fusion method based on independent region partition and compressed sensing is verified with the fusion bias as the test index. The fusion deviations of different methods are shown in Figure 4.Figure 4
Statistical graph of fusion deviation of different methods.By analyzing the results shown in Figure4, it can be seen that the fusion deviations of the proposed method, the fusion method based on time point matching of multi-source data, and the fusion method based on independent region division and compressed sensing will all change with the increase of the number of experiments. When the number of experiments is 10, the fusion deviations of the fusion method based on time point matching of multi-source data and the fusion method based on independent region division and compressed sensing reach their minimum values, respectively. When the number of experiments is 30, the fusion deviation of the proposed method reaches its minimum value. By observing the curve changes in the figure, it can be seen that the fusion deviation curve of the proposed method is always below that of the two comparison methods, indicating that the fusion deviation of the proposed method is always lower than that of the two comparison methods. It can be seen from the class that the fusion deviation of the method in this paper is smaller, indicating that the method is more effective.Finally, the application performance of the proposed method, the fusion method based on multi-source data time point matching, and the fusion method based on independent region partition and compressed sensing is verified by the recall rate of fusion results. The fusion deviations of different methods are shown in Figure5.Figure 5
Statistical graph of recall rate of fusion results of different methods.According to the results shown in Figure5, with the increase of the number of experiments, the recall rate of fusion results of the proposed method, the fusion method based on time point matching of multi-source data, and the fusion method based on independent region division and compressed sensing also changed significantly. At first, the recall rate for fusion results based on multi-source data fusion point matching method was lower than the recall rate for fusion results based on independent division and compression perception, but as the number of experiments increased, the recall rate for fusion results based on multi-source data fusion point matching method increased rapidly, exceeding the recall rate for fusion results based on independent division and compression perception. However, by comparison, the recall rate of the fusion results of the proposed method is always lower than 6%, which proves that the fusion results of the proposed method are more accurate than those of the two comparison methods.


## 5. Conclusion

To address some of the shortcomings of the traditional data fusion method, this study utilised an analysis of the photoelectric measuring principle, signal processing processes, and data sample set to implement the UKF operation, data preprocessing design, longitudinal and transverse fusion, and fusion quality optimization steps to complete the effective fusion of the photoelectric measuring instrument. The experimental findings indicate that this approach has a high timeliness of fusion, a low recall rate, and a low fusion deviation, suggesting that it has a wide range of application benefits.



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