Abstract:
Wireless data links between mobile vehicles are optimized by positioning the mobile vehicles in default locations, creating a wireless data link between the vehicles, and measuring the initial capacity of the wireless data link. An extremum-seeking algorithm is then performed, in which the mobile vehicles are moved locally around their default locations, and the resulting changes in capacity of the wireless data link are measured. The algorithm mathematically determines, based on the measured changes in capacity of the wireless data link, optimum locations at which, when the mobile vehicles are positioned there, the capacity of the wireless data link reaches a local maximum value. The mobile vehicles are then moved to the optimum locations.

Description:
TECHNICAL FIELD 
   This application relates in general to telecommunications and, more specifically, to systems and methods for improving wireless data link capacity between mobile vehicles. 
   BACKGROUND 
   Mobile vehicles, such as, for example, unmanned aerial vehicles (UAVs), are becoming more commonly used in a wide variety of applications. These vehicles are typically equipped with one or more sensors to monitor and collect data regarding the vehicle&#39;s surrounding environment. This data is often transmitted through other relay vehicles over wireless data links to a human operator or a central data gathering station. 
   In some applications, mobile vehicles can perform their desired functions by operating at high altitudes or in other free-space environments in which wireless communications between the vehicles are virtually unobstructed. In other applications, however, it may be desirable to use mobile vehicles in environments having complex terrain, such as, for example, urban environments with tall buildings or natural environments with hills, valleys, trees, or other obstructions. In such complex environments, the wireless communications between mobile vehicles are subject to very complicated electromagnetic interference effects. Even a slight displacement of a vehicle&#39;s position may result in significant changes in the transmitted/received bit rates of the vehicle. 
   Nevertheless, in conventional applications, mobile vehicles are typically placed in default locations determined in advance, and very little, if any, attempt is made to improve the signal strength of wireless communications between the mobile vehicles. Thus, a need exists for a method to improve the capacity of wireless communications between mobile vehicles operating in complex environments. 
   SUMMARY OF THE INVENTION 
   The above-mentioned drawbacks associated with existing mobile vehicle systems are addressed by embodiments of the present invention and will be understood by reading and studying the following specification. 
   In one embodiment, a method for optimizing a wireless data link between a first mobile vehicle and a second mobile vehicle comprises positioning the first mobile vehicle in a first location and the second mobile vehicle in a second location and creating a wireless data link between the first mobile vehicle and the second mobile vehicle. The method further comprises measuring the initial capacity of the wireless data link, moving the first mobile vehicle around the first location and the second mobile vehicle around the second location, and measuring the resulting changes in capacity of the wireless data link. The method further comprises mathematically determining, based on the measured changes in capacity of the wireless data link, a third location and a fourth location at which, when the first mobile vehicle and the second mobile vehicle are positioned there, respectively, the capacity of the wireless data link reaches a local maximum value, and moving the first mobile vehicle to the third location and the second mobile vehicle to the fourth location. 
   In another embodiment, a method for optimizing a wireless data link involving a first mobile vehicle comprises positioning the first mobile vehicle in a first location, creating a wireless data link between the first mobile vehicle and a telecommunications device, and measuring the initial capacity of the wireless data link. The method further comprises moving the first mobile vehicle around the first location and measuring the resulting changes in capacity of the wireless data link. The method further comprises mathematically determining, based on the measured changes in capacity of the wireless data link, a second location at which, when the first mobile vehicle is positioned there, the capacity of the wireless data link reaches a local maximum value, and moving the first mobile vehicle to the second location. 
   In another embodiment, a process for optimizing a wireless data link between a first mobile vehicle and a second mobile vehicle comprises: (a) placing the first and second mobile vehicles in default positions; (b) creating a wireless data link between the first and second mobile vehicles; and (c) measuring the initial capacity of the wireless data link and storing it as a maximum measured capacity. The process further comprises: (d) performing an extremum-seeking algorithm on the capacity of the wireless data link; (e) moving the first and second mobile vehicles to the positions determined by the extremum-seeking algorithm and measuring the new data link capacity; and (f) determining whether the new data link capacity exceeds a minimum threshold value and, if not, skipping to step (i). The process further comprises: (g) determining whether the new data link capacity exceeds the previously-stored maximum measured capacity and, if so, replacing the maximum measured capacity with the new data link capacity value and storing the corresponding positions of the mobile vehicles; (h) repeating steps (d)-(g) until the data link capacity stabilizes; and (i) moving the first and second mobile vehicles to the positions corresponding to the maximum measured capacity. 
   In another embodiment a mobile vehicle comprises a processor comprising an optimization module, a sensor coupled to the processor, and a transceiver coupled to the processor. The transceiver is capable of sending and receiving wireless data transmissions, and the optimization module is configured to mathematically determine an optimum local position of the mobile vehicle such that the capacity of a wireless data link between the transceiver and another telecommunications device reaches a local maximum value. 
   The details of one or more embodiments of the claimed invention are set forth in the accompanying drawings and the description below. Other features and advantages will become apparent from the description, the drawings, and the claims. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block diagram of one embodiment of a system having a plurality of mobile vehicles in communication with one another. 
       FIG. 2  is a flow chart illustrating one embodiment of a process for positioning mobile vehicles. 
       FIG. 3  is a signal flow diagram illustrating one loop of an embodiment of an extremum-seeking algorithm. 
   

   Like reference numbers and designations in the various drawings indicate like elements. 
   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
   In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific illustrative embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that logical, mechanical, and electrical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense. 
     FIG. 1  is a block diagram of one embodiment of a system  100  having a plurality of mobile vehicles  105  in communication with one another. The mobile vehicles  105  may comprise a wide variety of vehicles, such as, for example, hover-capable UAVs (e.g. organic air vehicles), fixed-wing UAVs, mobile ground vehicles, unmanned underwater vehicles (UUVs), or the like. In addition, the configuration of the mobile vehicles  105  may vary widely, depending on the application and the desired functions to be performed by the mobile vehicles  105 . For example, in some applications, certain mobile vehicles  105  act primarily as communication relays, whereas other mobile vehicles  105  actively monitor and collect data regarding their surrounding environment. The wireless communication system may use electromagnetic radiation or acoustic waves (especially for undersea communications) as the means of communication. 
   In the illustrated embodiment, the mobile vehicles  105  provide a communication link between two base stations  110 . The base stations  110  may comprise many possible communication devices, ranging from portable handheld devices to fixed data gathering stations. For example, in some specific exemplary embodiments, the system  100  is used by military or law enforcement personnel to facilitate communications between a central command center and one or more military or law enforcement officers operating remotely in the field. In these embodiments, the first base station  110  may comprise one or more fixed communication devices operated at the command center, and the second base station  110  may comprise one or more portable communication devices used by the military or law enforcement officers operating remotely in the field. 
   In the embodiment illustrated in  FIG. 1 , each mobile vehicle  105  comprises one or more sensors  115 , a processor  120 , an optimization module  125 , and a transceiver  130 . The sensors  115  may comprise a wide variety of devices, such as, for example, audio sensors, temperature sensors, acoustic sensors, or electromagnetic imaging sensors (e.g., optical sensors, through-wall imagers, foliage-penetration radar, etc.), to name a few. The system  100  may also comprise one or more fixed sensors (not shown), such as seismic sensors, for which the mobile vehicles  105  act primarily as communication relays. 
   In operation, the behavior of each mobile vehicle  105  is controlled by its corresponding processor  120 . Data collected by the sensors  115  is transmitted to or from a given mobile vehicle  105  through its corresponding transceiver  130 . These transmissions typically occur over wireless data links  135  between the mobile vehicles  105  and/or the base stations  110 . If the system  100  is operating in a free-space environment, these wireless data links  135  are virtually unobstructed, and the mobile vehicles  105  can be arranged in whatever configuration results in the maximum signal strength, or capacity, of each wireless data link  135  (e.g., equidistant distribution in a straight line between base stations  110 ). 
   As illustrated in  FIG. 1 , however, the mobile vehicles  105  may operate in an environment having obstacles  140 , such as, for example, buildings, cars, hills, valleys, trees, or other irregularities in terrain. These obstacles  140  can create very complicated electromagnetic interference effects in the wireless communications between the mobile vehicles  105  and/or the base stations  110 . For undersea acoustic communications, complex interference multipath propagation and interference results even without obstacles due to reflections off thermal gradient layers, and water surface reflections. In such environments, even a slight displacement in the position of a given mobile vehicle  105  may result in significant changes in the transmitted/received bit rates of the mobile vehicle  105 . Accordingly, as described in more detail below, the optimization module  125  of each mobile vehicle  105  is configured to determine an optimum local position for each mobile vehicle  105  such that the capacities of the wireless data links  135  are locally maximized to the extent possible, given the design constraints of the specific application. 
     FIG. 2  is a flow chart illustrating one embodiment of a process  200  for positioning mobile vehicles  105 . In a first step  205 , the mobile vehicles  105  are placed in default positions determined in advance. These positions are typically selected based on the given environment and the desired functions to be performed by the mobile vehicles  105 . For example, in the embodiment illustrated in  FIG. 1 , a default position for each mobile vehicle  105  is selected such that the mobile vehicles  105  can provide a stable communication link between the base stations  110 . Thus, if the mobile vehicles  105  are operating in an urban environment for example, they might be positioned on the tops of selected buildings, determined in advance, as a default placement. 
   In some embodiments, it may be desired to use the sensor(s)  115  of a given mobile vehicle  105  to monitor activity at a particular location. For example, a mobile vehicle  105  may be used to conduct video surveillance of activity on a particular street in a complex urban environment. In this example, the default position of the mobile vehicle  105  would be selected such that the optical sensor(s) of the mobile vehicle  105  have an unobstructed view of the appropriate street. This position might be on the edge of a particular rooftop or on a specific balcony or ledge. 
   Once the mobile vehicles  105  have been placed in their default positions, a series of steps  210  are repeated for each wireless data link  135 . In a step  215 , the current capacity of the wireless data link  135  is measured and stored as the maximum measured link capacity. In a next step  220 , an extremum-seeking algorithm is performed by the optimization modules  125  of the appropriate mobile vehicles  105 , as described in more detail below in connection with  FIG. 3 . 
   In relatively simple environments, the link capacity map between two mobile vehicles  105  may have only one local maximum value with respect to spatial coordinates. If this is the case, then the standard extremum-seeking algorithm described below will converge stably to the local maximum value in the link capacity map. In complex environments, however, there may be more than one local maximum value in the link capacity map between two mobile vehicles  105 . If this is the case, then the results of several iterations of the extremum-seeking algorithm may need to be compared to ensure that a true local maximum value is found. Multiple iterations of the extremum seeking algorithm can be performed by reinitializing the vehicle position randomly or out of the neighborhood previously visited local maxima. 
   After the extremum-seeking algorithm has been performed, in a step  225 , the mobile vehicles  105  are moved to the new positions determined by the extremum-seeking algorithm and the capacity of the wireless data link  135  is measured again. In a step  230 , a determination is made as to whether the current capacity of the wireless data link  135  exceeds a selected minimum threshold value. This step ensures that, as the mobile vehicles  105  attempt to optimize the capacity of the wireless data link  135  by repositioning themselves, the communication link between the mobile vehicles  105  is not lost altogether. If the link capacity drops below the minimum threshold value, then in a step  235 , the mobile vehicles  105  are returned to the positions corresponding to the best presently-known link capacity, and the process continues to the next wireless data link  135 . In performing sequential optimization of different wireless links in a relay chain, one method to monotonically improve overall performance is to optimize the capacity of a wireless data link  135  by oscillating only one of the vehicles  105  at a time. 
   If the link capacity remains above the minimum threshold value, then in a step  240 , a determination is made as to whether the new link capacity measurement exceeds the previously-stored maximum value. If so, then in a step  245 , the maximum measured link capacity is replaced with the present value and the corresponding positions of the mobile vehicles  105  are stored in memory. 
   Then, in a step  250 , a determination is made as to whether the link capacity has stabilized, or converged stably on a local maximum value. If so, then in step  235 , the mobile vehicles  105  are moved to the positions corresponding to the local maximum value in the link capacity map, and the process continues to the next wireless data link  135 . Otherwise, the process returns to step  220 , and another iteration of the extremum-seeking algorithm is performed. 
     FIG. 3  is a signal flow diagram illustrating one loop of an embodiment of an extremum-seeking algorithm. This algorithm is implemented in the optimization modules  125  of the mobile vehicles  105  between which a given wireless data link  135  exists. The algorithm involves oscillating the mobile vehicles  105  locally around their default positions, thereby causing oscillations in the capacity of the wireless data link  135  between the mobile vehicles  105 . Because it takes substantially longer for a mobile vehicle  105  to move to a new position (e.g., typically about ½ to 1 second or longer) than it takes for a wireless data link  135  to become settled (e.g., typically less than about a millisecond), the algorithm stably converges on a local maximum value in the capacity of the wireless data link  135 . 
   In the illustrated embodiment, the input dynamics  305 , labeled F i (s), represent the closed-loop position tracking dynamics of the mobile vehicles  105 . The spatial link capacity map  310 , labeled f(θ), represents the capacity of the wireless data link  135  at various spatial coordinates of the mobile vehicles  105 . The output dynamics  315 , labeled F o (s), represent the settling of the communication system components (e.g., synchronization circuits, etc.) comprising the wireless data link  135  between the mobile vehicles  105 . 
   In operation, the α p sin(ω p t) term added by adder  340  represents the perturbation in the tracking set point of the mobile vehicle  105  around its present position. This perturbation, in turn, causes oscillation of the link capacity signal. As a result of this oscillation, the gradient of the link capacity signal map can be identified and the mobile vehicle  105  can be moved in the direction of increasing link capacity. 
   The link capacity signal is noisy, as represented by noise signal, n, added to the link capacity signal by adder  320 . This noisy signal is passed through a washout filter  325 , labeled sC op (s), where C op (s) represents an output compensator. In some embodiments, for example, C op (s)=1/(s+h), where h is a selected constant. The washout filter  325  eliminates the constant part of the function. The signal is then demodulated with a sin(ω p t−φ p ) term by multiplier  330 , resulting in a number proportional to the slope of the signal. The signal is then passed through a filter  335 , labeled [C ip (s)]/s, where C ip (s) represents an input compensator, which may comprise any of a wide variety of suitably-designed proper transfer functions. In some embodiments, for example, C ip (s) is simply a constant. By multiplying the signal by a sin(ω p t−φ p ) term and integrating, the optimization module  125  identifies the gradient of the link capacity map and ensures that the mobile vehicle  105  is moving in the direction of increasing link capacity toward a local maximum value. 
   To optimize the capacity of a given wireless data link  135 , the loop of the extremum-seeking algorithm illustrated in  FIG. 3  is executed for each position coordinate available for adjustment. As an example, for a wireless data link  135  existing between two mobile vehicles  105  that are free to move in all three dimensions, the extremum-seeking algorithm would optimize the wireless data link  135  over six position coordinates, i.e., once for each position coordinate (x, y, z) for each mobile vehicle  105 . 
   As discussed above, in some applications, the movement of a given mobile vehicle  105  may be restricted due to external design constraints. For example, if a mobile vehicle  105  is being used to conduct video surveillance of activity on a street, the movement of the mobile vehicle  105  is restricted such that the optical sensor(s) of the mobile vehicle  105  always have an unobstructed view of the street. In this example, the mobile vehicle  105  may be free to move in only one dimension (e.g., along the edge of a particular rooftop or balcony), and the extremum-seeking algorithm would optimize a wireless data link  135  over only one dimension for this mobile vehicle  105 . 
   One element of extremum seeking design in the context of the systems and methods described above is knowledge of the range of values and median range of local interference map second derivatives. This is obtainable from simulation and experiment for both electromagnetic and acoustic channels. This enables extremum seeking designs that are guaranteed to converge stably to local maxima most of the time. In practice, it is virtually impossible to obtain stable convergence all of the time because the interference map second derivatives can fall outside of the median range for which extremum seeking can reasonably be designed. This is the reason for development of the resetting mechanism described above in connection with  FIG. 2 . 
   A more detailed description of extremum-seeking algorithms in general (including systematic design procedures with convergence guarantees) is available in the following publications:  Real - Time Optimization by Extremum - Seeking Control , by Kartik B. Ariyur and Miroslav Krstic, Wiley, 2003;  Multivariable Extremum Seeking Feedback: Analysis and Design , by Kartik B. Ariyur and Miroslav Krstic, Fifteenth International Symposium on Mathematical Theory of Networks and Systems, University of Notre Dame, Aug. 12-12, 2002. These publications, in their entireties, are incorporated herein by this reference. 
   The systems and methods described above provide a number of distinct advantages over conventional mobile vehicle systems. For example, the extremum-seeking algorithm enables optimization of a wireless data link between mobile vehicles operating in a complex environment having irregular terrain. Because the algorithm is designed to stably converge on a local maximum value in the spatial link capacity map, the optimization of the wireless data link is advantageously based on reliable mathematical guarantees, rather than imprecise trial-and-error methods. In addition, the optimization techniques described above can be customized based on the design constraints imposed by the desired functions to be performed by the mobile vehicles. 
   Although this invention has been described in terms of certain preferred embodiments, other embodiments that are apparent to those of ordinary skill in the art, including embodiments that do not provide all of the features and advantages set forth herein, are also within the scope of this invention. Accordingly, the scope of the present invention is defined only by reference to the appended claims and equivalents thereof.