Distributed multi-radiation source passive positioning method and system

Abstract

The embodiment of the invention relates to the technical field of passive positioning, in particular to a distributed multi-radiation source passive positioning method and system, and the method comprises the steps: carrying out the monitoring and sensing through distributed monitoring stations, carrying out the approximation of the signal intensity value of a received signal of each monitoring station, and obtaining an approximated signal intensity value obeying the logarithmic normal distribution; on the basis of the approximated signal intensity value, calculating a probability density function of each monitoring station, wherein the probability density function comprises horizontal and vertical coordinates representing a radiation source and an unknown vector of transmitting power; combining the probability density functions of all the monitoring stations to obtain a likelihood function of the whole positioning problem, and deriving a target function of maximum likelihood estimation of the whole positioning problem based on the likelihood function; a differential evolution algorithm is improved, global optimization is carried out based on the improved differential evolution algorithm and an objective function of maximum likelihood estimation of the whole positioning problem, the maximum likelihood estimator of an unknown vector is solved, and high-precision passive positioning of multiple radiation sources is effectively realized.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of passive positioning in electronic countermeasures, and in particular to a distributed multi-radiation source passive positioning method and system. Background Art

[0002] Passive positioning technology for electromagnetic radiation sources is a technology that can locate electromagnetic radiation sources without actively emitting electromagnetic detection signals. This technology realizes the concealed positioning of the located party by the locating party, and has important practical significance in the military field. As one of the core technologies of electronic countermeasures, passive positioning of electromagnetic radiation sources can obtain the position coordinates of electromagnetic radiation sources under concealed conditions. Its guiding ideology is to locate the radiation source by measuring the strength of the received signal through distributed monitoring stations. In order to ensure higher positioning accuracy, the positioning algorithm needs to be improved. Therefore, it is very necessary to design a high-precision passive positioning method.

[0003] At present, domestic and foreign research teams have proposed some passive positioning methods. The classic passive positioning methods include passive positioning methods based on angle of arrival (AOA), passive positioning methods based on time of arrival (TOA), passive positioning methods based on time difference of arrival (TDOA), passive positioning methods based on frequency of arrival (FOA), and passive positioning methods based on frequency difference of arrival (FDOA).

[0004] It is worth noting that the positioning technology based on received signal strength realizes positioning through the path loss model of electromagnetic signals, which is simpler than the positioning scheme based on other key parameters. Typical methods of single-radiator passive positioning based on received signal strength include passive positioning method using spatial correlation shadow least squares algorithm, passive positioning method based on maximum likelihood estimation, and passive positioning method based on maximum likelihood estimation method of semidefinite programming.

[0005] The above passive positioning methods are mostly for positioning scenarios with a single radiation source. However, the positioning scenario with a single radiation source cannot cover all possible real positioning scenarios. Especially in scenarios with dense electromagnetic radiation sources, signal interference is more significant, and the effectiveness of the single radiation source passive positioning method is bound to decrease. To this end, the positioning scenario with multiple radiation sources can be considered, and the synchronous positioning of multiple radiation sources can be achieved by designing a distributed multi-radiation source passive positioning system.

[0006] From the above analysis, it can be seen that the passive positioning methods currently proposed still have the following defects.

[0007] First, most of the current research focuses on single-radiator passive positioning based on received signal strength, and there is less research on multi-radiator passive positioning based on received signal strength.

[0008] Second, the several multi-radiator passive positioning methods that have been proposed have low positioning accuracy or even fail when the noise power is large. Summary of the invention

[0009] To solve the above technical problems, the embodiments of the present application propose a distributed multi-radiation source passive positioning method and system, which can effectively achieve high-precision passive positioning of multiple radiation sources in a line-of-sight propagation environment and under conditions of large noise interference.

[0010] To achieve the above purpose, the embodiment of the present application proposes a distributed multi-radiation source passive positioning method, the method comprising: monitoring and sensing through distributed monitoring stations, and calculating the signal strength value R of the received signal of the mth monitoring station. m The Fenton-Wilkinson approximation method is used to approximate the signal intensity value that obeys the log-normal distribution. Based on the approximated signal strength value Calculate the probability density function corresponding to the mth monitoring station, which contains the unknown quantity vector φ that characterizes the horizontal and vertical coordinates of the radiation source and the transmission power; combine the probability density functions corresponding to all monitoring stations to obtain the likelihood function of the entire positioning problem, and derive the objective function of the maximum likelihood estimation of the entire positioning problem based on the likelihood function; use the adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy to improve the differential evolution algorithm, and perform global optimization based on the improved differential evolution algorithm and the objective function of the maximum likelihood estimation of the entire positioning problem to solve the maximum likelihood estimator of the unknown quantity vector φ Realize passive positioning of distributed multiple radiation sources.

[0011] To achieve the above purpose, the embodiment of the present application also proposes a distributed multi-radiation source passive positioning system, the system comprising: a monitoring station sensing module, which is used to monitor and sense through distributed monitoring stations, and the signal strength value R of the received signal of the mth monitoring station m The Fenton-Wilkinson approximation method is used to approximate the signal intensity value that obeys the log-normal distribution. Probability density function calculation module, used to calculate the signal strength value based on the approximation Calculate the probability density function corresponding to the mth monitoring station, which contains the unknown quantity vector φ that characterizes the horizontal and vertical coordinates of the radiation source and the transmission power; the maximum likelihood estimation establishment module is used to combine the probability density functions corresponding to all monitoring stations to obtain the likelihood function of the entire positioning problem, and derive the objective function of the maximum likelihood estimation of the entire positioning problem based on the likelihood function; the maximum likelihood estimation solution module is used to improve the differential evolution algorithm by utilizing the fusion adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy, and perform global optimization based on the improved differential evolution algorithm and the objective function of the maximum likelihood estimation of the entire positioning problem to solve the maximum likelihood estimator of the unknown quantity vector φ Realize passive positioning of distributed multiple radiation sources.

[0012] To achieve the above-mentioned purpose, an embodiment of the present application also proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a distributed multi-radiation source passive positioning method as described above.

[0013] To achieve the above objectives, an embodiment of the present application further proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement a distributed multi-radiation source passive positioning method as described above.

[0014] The embodiment of the present application proposes a distributed multi-radiation source passive positioning method and system, which converts the signal strength value of the received signal sensed by the distributed monitoring station monitoring into a signal strength value that obeys the log-normal distribution using the Fenton-Wilkinson approximation method, thereby simplifying the mathematical model of the received signal strength of the multi-radiation source passive positioning, and facilitating the acquisition of the probability density function corresponding to the received signal strength of each monitoring station. By combining the probability density functions corresponding to the approximated received signal strengths of all monitoring stations, the likelihood function of the entire positioning problem can be constructed, and the maximum likelihood estimation problem of the entire positioning problem can be derived therefrom, that is, the objective function of the maximum likelihood estimation of the entire positioning problem, and the passive positioning problem is converted into the solution process of the maximum likelihood estimation problem. The differential evolution algorithm is improved by integrating adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy. The improved differential evolution algorithm is used to solve the objective function of the maximum likelihood estimation of the entire positioning problem, thereby improving the search ability and convergence speed of the differential evolution algorithm, and then more efficiently solving the maximum likelihood estimator of the unknown quantity vector, estimating the horizontal and vertical coordinates and the transmission power of the radiation source, and effectively achieving high-precision passive positioning of multiple radiation sources in a line-of-sight propagation environment and under conditions of large noise interference.

[0015] Optionally, the target positioning area is a rectangular area with a length of l and a width of w. There are M distributed monitoring stations and K radiation sources in the target positioning area, where M and K are both integers greater than 1. The electromagnetic signal emitted by the kth radiation source is received by the mth monitoring station, and the signal strength value R of the received signal of the mth monitoring station is m It is expressed by the following formula:

[0016]

[0017] Among them, d m，k represents the distance between the kth radiation source and the mth monitoring station, α is the path loss index, P k represents the transmission power of the kth radiation source, n m，k Represents propagation noise, with a mean of 0 and a variance of Gaussian random distribution.

[0018] Optionally, the signal strength value R of the received signal at the mth monitoring station m The Fenton-Wilkinson approximation method is used to approximate the signal intensity value that obeys the log-normal distribution. include:

[0019] The Fenton-Wilkinson approximation is used to approximate Rm as and, Subject to the mean μ m , the variance is The normal distribution of μ m and It is expressed by the formula:

[0020]

[0021] Among them, E(·) is the function for calculating expectation, and Var(·) is the function for calculating variance.

[0022] Optionally, based on the approximated signal strength value Calculate the probability density function corresponding to the mth monitoring station, which is achieved through the following formula:

[0023]

[0024] Among them, φ is the unknown quantity vector representing the horizontal and vertical coordinates of the radiation source and the transmission power, Represents the probability density function corresponding to the mth monitoring station.

[0025] Optionally, the probability density functions corresponding to all monitoring stations are combined to obtain the likelihood function of the entire positioning problem, which is implemented by the following formula:

[0026]

[0027] Among them, L(φ) represents the likelihood function of the entire positioning problem;

[0028] Based on the likelihood function, the objective function of the maximum likelihood estimation of the entire positioning problem is derived and implemented by the following formula:

[0029]

[0030] in, is the maximum likelihood estimator of the unknown vector φ that needs to be solved;

[0031] The constraints of the objective function of the maximum likelihood estimation of the entire positioning problem are expressed by the following formula:

[0032]

[0033] P low ≤P k ≤P high ;

[0034] Among them, x k represents the horizontal coordinate of the kth radiation source, y k represents the ordinate of the kth radiation source, g represents the number of iterations, P low Indicates the minimum transmit power, P high Indicates the maximum transmit power.

[0035] Optionally, a global optimization is performed based on the improved differential evolution algorithm and the objective function of the maximum likelihood estimation of the entire positioning problem to solve the maximum likelihood estimator of the unknown vector φ Realize passive positioning of distributed multiple radiation sources, including:

[0036] The improved differential evolution algorithm is used to solve the objective function of the maximum likelihood estimation of the entire positioning problem, and each element in the unknown vector φ is used as the gene of the improved differential evolution algorithm;

[0037] H initial population individuals are randomly generated to obtain a random initial population, and an opposing initial population is obtained based on the random initial population using the adversarial learning technique. The first H individuals with the minimum function value of the objective function of the maximum likelihood estimation of the entire positioning problem are selected from these two populations as the original population. The random initial population, the opposing initial population and the original population are respectively expressed as:

[0038]

[0039] Among them, D is the dimension of the individual, that is, the total number of genes;

[0040] The improved multi-mutation strategy is used to complete the mutation and crossover of the original population of the current iteration, and three test populations are obtained. The three test populations are expressed as:

[0041]

[0042] For individuals whose genes cross the boundary during the mutation crossover process, adaptive redirection is performed to obtain three redirection populations, which are expressed as:

[0043]

[0044] Apply the adversarial learning technique to each redirected population to obtain three new adversarial populations, which are expressed as:

[0045]

[0046] Perform selection operations among the original population of the current iteration, three redirected populations, and three new opposing populations to obtain the original population of the next iteration;

[0047] When the number of iterations reaches the preset maximum number of iterations G, the optimal individual I in the optimal population is obtained. great , will I great As the optimal solution of the objective function of the maximum likelihood estimation of the entire positioning problem, that is, the maximum likelihood estimator of the unknown vector φ

[0048] Optionally, the random initial population is generated by the operator It is calculated that the opposing initial population is composed of the operator Calculated, a d represents the upper bound of the dth gene, b d represents the lower bound of the dth gene, r h is a random number uniformly distributed from 0 to 1;

[0049] The improved multi-mutation strategy consists of two parts: the mutation crossover strategy candidate pool and the control parameter strategy candidate pool. In each iteration, each mutation crossover strategy selects a control parameter generation strategy from the control parameter strategy candidate pool to form a new mutation crossover strategy, thereby generating a test population. There are three operators set in the mutation strategy candidate pool, namely:

[0050]

[0051] Where o, p, q are all positive integers randomly selected from the set {1, 2, ..., H}, and they are not equal to each other and not equal to h. F is a scaling factor between (0, 1), and c his the crossover probability between (0, 1), is the best individual in the original population at the g-th iteration;

[0052] The adaptive relocation operator is:

[0053]

[0054] Among them, ω d is a random number with a uniform distribution between [0, 1]. and are the lower and upper bounds of the adaptive relocation operation, respectively;

[0055] The opposite individual generation operator is:

[0056] The selection operator is:

[0057] Where fit(·) is the fitting function. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related technologies, the drawings required for use in the embodiments of the present application or the related technical descriptions will be briefly introduced below. Obviously, the following drawings are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. The drawings described here are only used to explain the present application and are not used to limit the present application.

[0059] Figure 1 is a flow chart of a distributed multi-radiation source passive positioning method provided in one embodiment of the present application;

[0060] Figure 2 is a comparison diagram of relative positioning errors between a distributed multi-radiation source passive positioning method provided in an embodiment of the present application and a traditional passive positioning method;

[0061] Figure 3 is a comparison diagram of the relative maximum error cumulative distribution rate between the distributed multi-radiation source passive positioning method provided in one embodiment of the present application and the traditional passive positioning method;

[0062] Figure 4 is a structural schematic diagram of a distributed multi-radiation source passive positioning system provided in another embodiment of the present application;

[0063] Figure 5 It is a structural schematic diagram of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. It will be appreciated by those skilled in the art that in the embodiments of the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be limited in any way. The various embodiments can be combined and referenced with each other without contradiction.

[0065] An embodiment of the present application proposes a distributed multi-radiation source passive positioning method, which is applied to an electronic device, wherein the electronic device can be a terminal or a server. This embodiment and the following embodiments are all described by taking the server as an example. The following is a specific description of the implementation details of a distributed multi-radiation source passive positioning method proposed in this embodiment. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for the implementation of this solution.

[0066] The specific process of the distributed multi-radiation source passive positioning method proposed in this embodiment can be as follows: Figure 1 As shown, including:

[0067] S1, monitoring and sensing through distributed monitoring stations, the signal strength value R of the received signal at the mth monitoring station m The Fenton-Wilkinson approximation method is used to approximate the signal intensity value that obeys the log-normal distribution.

[0068] In the specific implementation, there are multiple radiation sources in the target positioning area. The server needs to passively locate multiple radiation sources in the target positioning area. This requirement is implemented based on distributed monitoring stations. The server monitors and senses through distributed monitoring stations. Each monitoring station can monitor (collect and receive) the signal emitted by the radiation source. The signal strength value of the received signal at the mth monitoring station is recorded as R m The server uses the Fenton-Wilkinson approximation method to obtain an approximated signal strength value that obeys the log-normal distribution.

[0069] The Fenton-Wilkinson approximation method simplifies the estimation of the distribution function by approximating the sum of multiple log-normal random variables to a single log-normal random variable. In this embodiment, the server uses the Fenton-Wilkinson approximation method to approximate the received signal strength value R that obeys the log-normal sum distribution. mApproximation to the signal strength value after approximation from the log-normal distribution

[0070] In one example, it is assumed that the target location area is a rectangular area with a length of l and a width of w. In the target location area, there are M distributed monitoring stations and K radiation sources, and M and K are both integers greater than 1. The electromagnetic signal emitted by the kth radiation source is monitored (received) by the mth monitoring station, and the signal strength value R of the received signal of the mth monitoring station is m It can be expressed by the following formula:

[0071]

[0072] Among them, d m，k represents the distance between the kth radiation source and the mth monitoring station, α is the path loss index, P k represents the transmission power of the kth radiation source, n m，k Represents propagation noise, with a mean of 0 and a variance of Gaussian random distribution (Gaussian random variable).

[0073] In one example, the server uses the Fenton-Wilkinson approximation to convert R m Approximately and, Subject to the mean μ m , the variance is Normal distribution. m and It can be expressed by the formula:

[0074]

[0075] Among them, E(·) is the function for calculating expectation, and Var(·) is the function for calculating variance.

[0076] S2, based on the approximated signal strength value Calculate the probability density function corresponding to the mth monitoring station, where the probability density function contains the horizontal and vertical coordinates representing the radiation source and the unknown quantity vector φ of the transmission power.

[0077] In the specific implementation, the server obtains the approximate signal strength value After that, we need to use the approximated signal strength value Calculate the probability density function corresponding to the mth monitoring station (i.e. the approximate signal strength value corresponding to the mth monitoring station) The probability density function contains the unknown vector φ representing the horizontal and vertical coordinates of the radiation source and the transmission power. By solving the unknown vector φ representing the horizontal and vertical coordinates of the radiation source and the transmission power, the passive positioning of multiple radiation sources can be achieved.

[0078] In one example, the server uses the approximated signal strength value Calculating the probability density function corresponding to the mth monitoring station can be achieved through the following formula:

[0079]

[0080] Among them, φ is the unknown quantity vector representing the horizontal and vertical coordinates of the radiation source and the transmission power, Represents the probability density function corresponding to the mth monitoring station.

[0081] S3, combines the probability density functions corresponding to all monitoring stations to obtain the likelihood function of the entire positioning problem, and derives the objective function of the maximum likelihood estimation of the entire positioning problem based on the likelihood function.

[0082] In the specific implementation, after obtaining the probability density function corresponding to each monitoring station, the server needs to combine the probability density functions corresponding to all monitoring stations to obtain the likelihood function of the entire positioning problem, and derive the objective function of the maximum likelihood estimation of the entire positioning problem based on the likelihood function (that is, the maximum likelihood estimation problem of the entire positioning problem).

[0083] In one example, the server combines the probability density functions corresponding to all monitoring stations to obtain the likelihood function of the entire positioning problem, which can be implemented by the following formula:

[0084]

[0085] Among them, L(φ) represents the likelihood function of the entire positioning problem.

[0086] In one example, the server derives the objective function of the maximum likelihood estimation of the entire positioning problem based on the likelihood function, which can be implemented by the following formula:

[0087]

[0088] in, is the maximum likelihood estimator of the unknown vector φ that needs to be solved. The constraint of the objective function of the maximum likelihood estimation of the entire positioning problem is expressed by the following formula:

[0089]

[0090] P low ≤P k ≤P high;

[0091] Among them, x k represents the horizontal coordinate of the kth radiation source, y k represents the ordinate of the kth radiation source, g represents the number of iterations, P low Indicates the minimum transmit power, P high Indicates the maximum transmit power.

[0092] S4, using adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy, improves the differential evolution algorithm, performs global optimization based on the improved differential evolution algorithm and the objective function of maximum likelihood estimation of the entire positioning problem, and solves the maximum likelihood estimator of the unknown vector φ Realize passive positioning of distributed multiple radiation sources.

[0093] In the specific implementation, after the server completes the construction of the objective function of the maximum likelihood estimation of the entire positioning problem, it is necessary to use adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy to improve the differential evolution algorithm, and then perform global optimization based on the improved differential evolution algorithm and the objective function of the maximum likelihood estimation of the entire positioning problem, so as to solve the maximum likelihood estimator of the unknown vector φ Realize passive positioning of distributed multiple radiation sources.

[0094] Improving the differential evolution algorithm using adversarial learning technology, adaptive redirection technology and improved multiple mutation strategy is not achieved overnight, but runs through the entire process of differential evolution.

[0095] The server performs global optimization based on the improved differential evolution algorithm and the objective function of the maximum likelihood estimation of the entire positioning problem to solve the maximum likelihood estimator of the unknown vector φ When realizing passive positioning of distributed multi-radiation sources, the improved differential evolution algorithm is first used to solve the objective function of the maximum likelihood estimation of the entire positioning problem, and each element in the unknown vector φ is used as the gene of the improved differential evolution algorithm.

[0096] Then, H initial population individuals are randomly generated to obtain a random initial population, and an opposing initial population is obtained based on the random initial population using the adversarial learning technology, and the first H individuals with the smallest function value of the objective function that makes the maximum likelihood estimate of the entire positioning problem are selected from the two populations as the original population.

[0097] The random initial population, the opposing initial population and the original population are expressed by the formulas:

[0098]

[0099] Among them, D is the dimension of the individual, that is, the total number of genes.

[0100] After that, the server uses the improved multi-mutation strategy to complete the mutation and crossover of the original population of the current iteration, and obtains three test populations. The three test populations are expressed by the formula:

[0101]

[0102] Then, the server adaptively redirects the individuals whose genes cross the boundary during the mutation crossover process, and obtains three redirection populations. The three redirection populations are expressed by the formula:

[0103]

[0104] After completing the establishment of the redirection population, the server needs to apply the adversarial learning technology to each redirection population to obtain three new adversarial populations. The three new adversarial populations are expressed by the formula:

[0105]

[0106] After all populations are established, the server selects among the original population of the current iteration, three redirected populations, and three new opposing populations to obtain the original population for the next iteration.

[0107] When the number of iterations reaches the preset maximum number of iterations G, the optimal individual I in the optimal population is obtained. great , will I great As the optimal solution of the objective function of the maximum likelihood estimation of the entire positioning problem, that is, the maximum likelihood estimator of the unknown vector φ

[0108] In one example, the random initial population is generated by the operator The opposing initial population is calculated by the operator Calculated, a d and b d Represent the upper and lower bounds of the dth gene, r h is a random number uniformly distributed between 0 and 1.

[0109] In one example, the improved multi-mutation strategy includes two parts: a mutation crossover strategy candidate pool and a control parameter strategy candidate pool. In each iteration, each mutation crossover strategy selects a control parameter generation strategy from the control parameter strategy candidate pool to form a new mutation crossover strategy, thereby generating a test population. Three operators are set in the mutation strategy candidate pool, namely:

[0110]

[0111] Where o, p, q are all positive integers randomly selected from the set {1, 2, ..., H}, and they are not equal to each other and not equal to h. F is a scaling factor between (0, 1), and c h is the crossover probability between (0, 1), is the best individual in the original population at the g-th iteration.

[0112] In one example, there are two strategies in the candidate pool of control parameter strategies, including F~N(0.9, 0.2 2 ) and P c ~N(0.1,0.2 2 ), and F~N(0.9,0.2 2 ) and P c ~N(0.9,0.2 2 ). The adaptive relocation operator is expressed by the formula:

[0113]

[0114] Among them, ω d is a random number with a uniform distribution between [0, 1]. and are the lower and upper bounds of the adaptive relocation operation, respectively.

[0115] In one example, the adversarial individual generation operator is:

[0116] The selection operator is:

[0117] Where fit(·) is the fitting function.

[0118] In this embodiment, the signal strength value of the received signal obtained by the distributed monitoring station monitoring perception is approximately converted into a signal strength value that obeys the log-normal distribution using the Fenton-Wilkinson approximation method, thereby greatly simplifying the mathematical model of the received signal strength of multi-radiator passive positioning, and facilitating the acquisition of the probability density function corresponding to the received signal strength of each monitoring station. By combining the probability density functions corresponding to the approximated received signal strength of all monitoring stations, the likelihood function of the entire positioning problem can be constructed, and the maximum likelihood estimation problem of the entire positioning problem can be derived from this, that is, the objective function of the maximum likelihood estimation of the entire positioning problem, and the passive positioning problem is converted into the solution process of the maximum likelihood estimation problem. The differential evolution algorithm is improved by integrating adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy, and the objective function of the maximum likelihood estimation of the entire positioning problem is solved by the improved differential evolution algorithm, thereby improving the search ability and convergence speed of the differential evolution algorithm, and then more efficiently solving the maximum likelihood estimator of the unknown vector, estimating the horizontal and vertical coordinates and the transmission power of the radiation source, and effectively realizing high-precision passive positioning of multiple radiation sources in a line-of-sight propagation environment and under conditions of large noise interference.

[0119] The steps of the above methods are divided only for the purpose of clear description. They can be combined into one step or some steps can be decomposed into multiple steps during implementation. As long as they include the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but not changing the core design of the algorithm and process, are all within the scope of protection of this application.

[0120] In one embodiment, in order to evaluate the performance of a distributed multi-radiation source passive positioning method (hereinafter referred to as the method) proposed in the present application, we used Matlab software to perform simulation verification.

[0121] The target positioning area is set to a rectangular area of ​​2000m×2000m, where the distributed anchor nodes (monitoring stations) are evenly distributed, the target nodes (radiation sources) are randomly distributed, and the position coordinates obey the uniform distribution. The number of anchor nodes M is set to 36, and the number of radiation sources is set to 3. The path loss index α is set to 2.5, and the shadow fading factor σ varies between [2, 12] dB. In addition, it is assumed that the target radiation power value P k It is randomly set between [2000, 3000] mW. Note that when the distance between the kth target node and the mth anchor node is less than 1m, the received signal strength value of the kth target node received by the mth anchor node can be considered as the transmission power P of the anchor node. k .

[0122] The comparison of the relative positioning error between this method and the traditional passive positioning method can be shown as follows: Figure 2 As shown in the figure, the comparison of the relative maximum error cumulative distribution rate between this method and the traditional passive positioning method can be shown as Figure 3 shown.

[0123] The simulation results show that the RMSE and RMEF of all algorithms decrease as the shadow fading intensity σ decreases, that is, the positioning accuracy improves accordingly. This method is superior to the traditional l1-MMSE algorithm and I-BLOOMP algorithm in positioning accuracy. Specifically, when the shadow fading intensity is σ=2dB, the RMSE of the l1-MMSE algorithm and the I-BLOOMP method are 139.6m and 243.3m respectively, while the RMSE of this method is only 126.3m. In addition, compared with the DE-MLE method, this method also shows a lower positioning error, thanks to the multi-strategy fusion and adaptive redirection strategy. Further analysis shows that the positioning accuracy of this method is significantly better than other methods under high shadow fading intensity conditions. When the shadow fading intensity is σ=12dB, the RMSE of this method is 263.1m, which is better than the 329.7m of the I-BLOOMP method when σ=6dB.

[0124] Another embodiment of the present application proposes a distributed multi-radiation source passive positioning system. The following is a specific description of the details of the distributed multi-radiation source passive positioning system proposed in this embodiment. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for the implementation of this example. Figure 4 It is a structural diagram of a distributed multi-radiation source passive positioning system proposed in this embodiment, including: a monitoring station sensing module M1, a probability density function calculation module M2, a maximum likelihood estimation establishment module M3 and a maximum likelihood estimation solution module M4.

[0125] The monitoring station sensing module M1 is used to monitor and sense the signal strength value R of the received signal at the mth monitoring station through distributed monitoring stations. m The Fenton-Wilkinson approximation method is used to approximate the signal intensity value that obeys the log-normal distribution.

[0126] Probability density function calculation module M2, used for signal strength value based on approximation Calculate the probability density function corresponding to the mth monitoring station, where the probability density function contains the horizontal and vertical coordinates representing the radiation source and the unknown quantity vector φ of the transmission power.

[0127] The maximum likelihood estimation module M3 is used to combine the probability density functions corresponding to all monitoring stations to obtain the likelihood function of the entire positioning problem, and derive the objective function of the maximum likelihood estimation of the entire positioning problem based on the likelihood function.

[0128] The maximum likelihood estimation solution module M4 is used to improve the differential evolution algorithm by integrating adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy. Based on the improved differential evolution algorithm and the objective function of the maximum likelihood estimation of the entire positioning problem, global optimization is performed to solve the maximum likelihood estimator of the unknown vector φ. Realize passive positioning of distributed multiple radiation sources.

[0129] It is not difficult to find that this embodiment is a system embodiment corresponding to the above method embodiment, and this embodiment can be implemented in conjunction with the above method embodiment. The relevant technical details and technical effects mentioned in the above embodiments are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiments.

[0130] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0131] Another embodiment of the present application provides an electronic device, wherein the structure of the electronic device is as follows: Figure 5 As shown, it includes: at least one processor C1; and a memory C2 that is communicatively connected to the at least one processor C1; wherein the memory C2 stores instructions that can be executed by the at least one processor C1, and the instructions are executed by the at least one processor C1 so that the at least one processor C1 can execute a distributed multi-radiation source passive positioning method as described in the above-mentioned method embodiments.

[0132] Among them, the memory and the processor are connected in a bus manner, and the bus includes any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripheral devices, voltage regulators and power management circuits together, which are all well known in the art, so this article will not describe them further. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna, and further, the antenna also receives the data and transmits the data to the processor.

[0133] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0134] Another embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement a distributed multi-radiation source passive positioning method as described in the above method embodiments.

[0135] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (such as a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. Storage media include: U disk, mobile hard disk, read-only memory, random access memory, disk or optical disk and other media that can store program codes.

[0136] It can be understood by those skilled in the art that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present application. For those skilled in the art, several improvements and modifications can be made without departing from the principles of the present application, and these improvements and modifications are also considered to be within the scope of protection of the present application.

Claims

1. A distributed multi-radiation source passive positioning method, characterized in that: include: Through distributed monitoring stations, the signal strength value R of the received signal at the mth monitoring station is m The Fenton-Wilkinson approximation method is used to approximate the signal intensity value that obeys the log-normal distribution. Based on the approximated signal strength value Calculate the probability density function corresponding to the mth monitoring station, where the probability density function contains the horizontal and vertical coordinates representing the radiation source and the unknown quantity vector φ of the transmission power; Combine the probability density functions corresponding to all monitoring stations to obtain the likelihood function of the entire positioning problem, and derive the objective function of the maximum likelihood estimation of the entire positioning problem based on the likelihood function; The differential evolution algorithm is improved by using adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy. Based on the improved differential evolution algorithm and the objective function of maximum likelihood estimation of the entire positioning problem, global optimization is performed to solve the maximum likelihood estimator of the unknown vector φ. Realize passive positioning of distributed multiple radiation sources.

2. A distributed multi-radiation source passive positioning method according to claim 1, characterized in that: The target positioning area is a rectangular area with a length of l and a width of w. There are M distributed monitoring stations and K radiation sources in the target positioning area. M and K are both integers greater than 1. The electromagnetic signal emitted by the kth radiation source is received by the mth monitoring station. The signal strength value R of the received signal at the mth monitoring station is m It is expressed by the following formula: Among them, d m,k represents the distance between the kth radiation source and the mth monitoring station, α is the path loss index, P k represents the transmission power of the kth radiation source, n m,k Represents propagation noise, with a mean of 0 and a variance of Gaussian random distribution.

3. A distributed multi-radiation source passive positioning method according to claim 2, characterized in that: The signal strength value R of the received signal at the mth monitoring station m The Fenton-Wilkinson approximation method is used to approximate the signal intensity value that obeys the log-normal distribution. include: Use the Fenton-Wilkinson approximation to convert R m Approximately and, Subject to the mean μ m , the variance is The normal distribution of m and It is expressed by the formula: Among them, E(·) is the function for calculating expectation, and Var(·) is the function for calculating variance.

4. A distributed multi-radiation source passive positioning method according to claim 3, characterized in that: Based on the approximated signal strength value Calculate the probability density function corresponding to the mth monitoring station, which is achieved through the following formula: Among them, φ is the unknown quantity vector representing the horizontal and vertical coordinates of the radiation source and the transmission power, Represents the probability density function corresponding to the mth monitoring station.

5. A distributed multi-radiation source passive positioning method according to claim 4, characterized in that: Combining the probability density functions corresponding to all monitoring stations, we can get the likelihood function of the entire positioning problem, which is implemented by the following formula: Among them, L(φ) represents the likelihood function of the entire positioning problem; Based on the likelihood function, the objective function of the maximum likelihood estimation of the entire positioning problem is derived and implemented by the following formula: in, is the maximum likelihood estimator of the unknown vector φ that needs to be solved; The constraints of the objective function of the maximum likelihood estimation of the entire positioning problem are expressed by the following formula: P low ≤P k ≤P high ; Among them, x k represents the horizontal coordinate of the kth radiation source, y k represents the ordinate of the kth radiation source, g represents the number of iterations, P low Indicates the minimum transmit power, P high Indicates the maximum transmit power.

6. A distributed multi-radiation source passive positioning method according to claim 5, characterized in that: Based on the improved differential evolution algorithm and the objective function of the maximum likelihood estimation of the entire positioning problem, global optimization is performed to solve the maximum likelihood estimator of the unknown vector φ Realize passive positioning of distributed multiple radiation sources, including: The improved differential evolution algorithm is used to solve the objective function of the maximum likelihood estimation of the entire positioning problem, and each element in the unknown vector φ is used as the gene of the improved differential evolution algorithm; H initial population individuals are randomly generated to obtain a random initial population, and an opposing initial population is obtained based on the random initial population using the adversarial learning technique. The first H individuals with the minimum function value of the objective function of the maximum likelihood estimation of the entire positioning problem are selected from these two populations as the original population. The random initial population, the opposing initial population and the original population are respectively expressed as: Among them, D is the dimension of the individual, that is, the total number of genes; The improved multi-mutation strategy is used to complete the mutation and crossover of the original population of the current iteration, and three test populations are obtained. The three test populations are expressed as: For individuals whose genes cross the boundary during the mutation crossover process, adaptive redirection is performed to obtain three redirection populations, which are expressed as: Apply the adversarial learning technique to each redirected population to obtain three new adversarial populations, which are expressed as: Perform selection operations among the original population of the current iteration, three redirected populations, and three new opposing populations to obtain the original population of the next iteration; When the number of iterations reaches the preset maximum number of iterations G, the optimal individual I in the optimal population is obtained. great , will I great As the optimal solution of the objective function of the maximum likelihood estimation of the entire positioning problem, that is, the maximum likelihood estimator of the unknown vector φ 7. A distributed multi-radiation source passive positioning method according to claim 6, characterized in that: The random initial population is composed of the operator It is calculated that the opposing initial population is composed of the operator Calculated, a d represents the upper bound of the dth gene, b d represents the lower bound of the dth gene, r h is a random number uniformly distributed from 0 to 1; The improved multi-mutation strategy consists of two parts: the mutation crossover strategy candidate pool and the control parameter strategy candidate pool. In each iteration, each mutation crossover strategy selects a control parameter generation strategy from the control parameter strategy candidate pool to form a new mutation crossover strategy, thereby generating a test population. There are three operators set in the mutation strategy candidate pool, namely: Where o, p, q are all positive integers randomly selected from the set {1, 2, …, H}, and they are not equal to each other and not equal to h. F is a scaling factor between (0, 1), and c h is the crossover probability between (0,1), is the best individual in the original population at the g-th iteration; The adaptive relocation operator is: Among them, ω d is a random number with a uniform distribution between [0,1]. and are the lower and upper bounds of the adaptive relocation operation, respectively; The opposite individual generation operator is: The selection operator is: Where fit(·) is the fitting function.

8. A distributed multi-radiation source passive positioning system, characterized in that: include; The monitoring station sensing module is used to monitor and sense the signal strength value R of the received signal at the mth monitoring station through distributed monitoring stations. m The Fenton-Wilkinson approximation method is used to approximate the signal intensity value that obeys the log-normal distribution. Probability density function calculation module, used to calculate the signal strength value based on the approximation Calculate the probability density function corresponding to the mth monitoring station, where the probability density function contains the horizontal and vertical coordinates representing the radiation source and the unknown quantity vector φ of the transmission power; The maximum likelihood estimation establishment module is used to combine the probability density functions corresponding to all monitoring stations to obtain the likelihood function of the entire positioning problem, and derive the objective function of the maximum likelihood estimation of the entire positioning problem based on the likelihood function; The maximum likelihood estimation solution module is used to improve the differential evolution algorithm by integrating adversarial learning technology, adaptive redirection technology and improved multi-mutation strategy. Based on the improved differential evolution algorithm and the objective function of the maximum likelihood estimation of the entire positioning problem, global optimization is performed to solve the maximum likelihood estimator of the unknown vector φ. Realize passive positioning of distributed multiple radiation sources.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a distributed multi-radiation source passive positioning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it can implement a distributed multi-radiation source passive positioning method as claimed in any one of claims 1 to 7.

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