Multi-station cooperative passive positioning method and device and readable storage medium

Abstract

The invention provides a multi-station cooperative passive positioning method and device and a readable storage medium, and the method comprises the steps: building a signal model according to radiation source signal data received by a plurality of receivers; according to the signal model and the radiation source signal data, constructing a cost function of moving multi-station direct positioning; according to the cost function, roughly estimating the position of a radiation source through an improved particle swarm algorithm to obtain a first positioning result; according to the first positioning result, high-precision estimation is carried out on the position of the radiation source through a quasi-Newton method, a second positioning result is obtained, and the second positioning result is a passive positioning result of the radiation source. According to the multi-station cooperative passive positioning method provided by the invention, rapid and accurate radiation source positioning can be realized in scenes of emergency communication, disaster rescue large-scale activity guarantee and the like, and the intelligent level and the response efficiency of the system are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of passive positioning technology, and in particular to a multi-station collaborative passive positioning method, device and readable storage medium. Background Technology

[0002] With the exponential increase in the complexity of the electromagnetic environment, passive positioning technology, due to its covert detection characteristics, has become one of the core technologies of modern electronic reconnaissance systems. This technology overcomes the limitations of traditional active radar, which easily exposes its own position, by passively receiving radiation signals from target sources or environmental reflections, demonstrating unique advantages in fields such as UAV navigation and disaster relief.

[0003] Depending on the number of observation stations, positioning methods in passive positioning systems can be divided into single-station positioning and multi-station cooperative positioning. From a system architecture perspective, single-station positioning relies on a single node for target detection. Its advantages lie in deployment flexibility and hardware cost control, and it still holds a place in small-scale monitoring scenarios. However, when tracking high-speed maneuvering targets, its single observation node suffers from insufficient dimensionality in Doppler parameter calculation and is susceptible to multipath interference in complex electromagnetic environments, leading to significant parameter calculation errors. In contrast, multi-station cooperative positioning, by constructing a distributed observation network and utilizing spatial geometric constraints, significantly improves the completeness of parameter estimation and has become the mainstream solution for improving positioning accuracy in complex environments.

[0004] Therefore, how to improve the accuracy of multi-station collaborative positioning has become a problem that needs to be solved. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a multi-station collaborative passive positioning method, device and readable storage medium to address the above-mentioned shortcomings of the prior art and solve the problems existing in the prior art.

[0006] Firstly, this application provides a multi-station cooperative passive positioning method, the method comprising:

[0007] S1. Establish a signal model based on the radiation source signal data received by multiple receivers;

[0008] S2. Based on the signal model and the radiation source signal data, construct the cost function for direct positioning of multiple motion stations;

[0009] S3. Based on the cost function, the location of the radiation source is roughly estimated by the improved particle swarm optimization algorithm to obtain the first localization result;

[0010] S4. Based on the first positioning result, the position of the radiation source is estimated with high precision using the quasi-Newton method to obtain a second positioning result, which is the passive positioning result of the radiation source.

[0011] In some embodiments, S1 includes:

[0012] Based on the radiation source signal data received by multiple receivers, the Doppler frequency shift caused by the relative motion of the multiple receivers is determined, and a signal model based on the Doppler frequency difference is established.

[0013] In some embodiments, the signal model based on the Doppler frequency difference is as follows:

[0014] ;

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] in, K represents the number of signal interceptions. L represents the number of receiving stations. In order to receive signals, As the attenuation factor, It is white noise. The signal is emitted by the radiation source, and N is the number of sampling points. For the phase delay matrix, It is a frequency difference matrix. For unknown transmission frequency shift, This is due to the Doppler frequency shift.

[0022] In some embodiments, S2 includes:

[0023] S21. Construct the log-likelihood function of the observation vector using the maximum likelihood estimation method;

[0024] S22. Simplify the log-likelihood function into a cost function;

[0025] S23. Simplify the cost function into matrix form to obtain the modified covariance matrix;

[0026] S24. Obtain the location cost function for locating the radiation source based on the modified covariance matrix.

[0027] In some embodiments, S3 includes:

[0028] S31. Initialize the basic parameters of the particle swarm algorithm;

[0029] S32. Call the objective function to calculate the fitness value of each particle, record the current position of the particle as the individual optimal position, and select the position with the smallest fitness from all particles as the global optimal position.

[0030] S33. In each iteration, the velocity is adjusted based on the individual optimal position, the global optimal position, and the random perturbation, and the position is updated according to the new velocity;

[0031] S34. If a particle flies out of the preset range, it is corrected to the nearest boundary value, and the current fitness is recalculated. If it is better than the individual optimal value, the individual optimal position is updated; if it is better than the global optimal value, the global optimal position is updated. The speed and position are updated repeatedly until the maximum number of iterations is reached or convergence is achieved.

[0032] S35. After the iteration is completed, output the global optimal position as the first localization result.

[0033] In some embodiments, in S33, the speed update formula is:

[0034] ;

[0035] The position update formula is:

[0036] ;

[0037] Where pbest is the individual optimal position, gbest is the global optimal position, w is the inertia weight, c1 is the individual learning factor, and c2 is the social learning factor.

[0038] In some embodiments, S4 includes:

[0039] S41. Initialize the Hessian inverse matrix to the identity matrix, and calculate the initial gradient based on the first localization result and the identity matrix;

[0040] S42. In each iteration, the search direction is calculated based on the current Hessian inverse matrix and the current gradient. After determining the optimal step size through line search, the position estimate is updated, the gradient of the new position is calculated, and the Hessian inverse matrix is ​​updated.

[0041] S43. Determine whether the gradient change satisfies the convergence condition. If it does, stop iterating; otherwise, continue iterating.

[0042] S44. By dynamically adjusting the Hessian inverse matrix and combining gradient information, fast convergence is achieved, and the final position is obtained as the second localization result.

[0043] Secondly, this application provides a multi-station cooperative passive positioning device, the device comprising:

[0044] The signal model building module is configured to build a signal model based on radiation source signal data received by multiple receivers.

[0045] The cost function construction module is configured to construct a cost function for direct positioning of multiple motion stations based on the signal model and the radiation source signal data.

[0046] The coarse estimation module is configured to perform a coarse estimation of the radiation source location based on the cost function using an improved particle swarm optimization algorithm to obtain a first location result;

[0047] The high-precision estimation module is configured to perform high-precision estimation of the radiation source position based on the first positioning result using the quasi-Newton method to obtain a second positioning result, wherein the second positioning result is the passive positioning result of the radiation source.

[0048] Thirdly, this application provides a multi-station cooperative passive positioning device, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to implement the multi-station cooperative passive positioning method described in the first aspect.

[0049] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the multi-station cooperative passive positioning method described in the first aspect.

[0050] This application provides a multi-station collaborative passive positioning method, apparatus, and readable storage medium. The method includes: establishing a signal model based on radiation source signal data received by multiple receivers; constructing a cost function for direct positioning of moving multi-stations based on the signal model and the radiation source signal data; performing a coarse estimation of the radiation source location using an improved particle swarm optimization algorithm based on the cost function to obtain a first positioning result; and performing a high-precision estimation of the radiation source location using a quasi-Newton method based on the first positioning result to obtain a second positioning result, wherein the second positioning result is the passive positioning result of the radiation source. This application provides a multi-station collaborative passive positioning method that can achieve rapid and accurate radiation source positioning in scenarios such as emergency communication and disaster relief support for large-scale events, significantly improving the system's intelligence level and response efficiency. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0052] Figure 1A flowchart illustrating a multi-station collaborative passive localization method provided in this application embodiment;

[0053] Figure 2 This is a schematic diagram of the structure of a multi-station collaborative passive positioning device provided in an embodiment of this application;

[0054] Figure 3 This is a schematic diagram of another multi-station collaborative passive positioning device provided in an embodiment of this application.

[0055] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0056] To enable those skilled in the art to better understand the technical solution of this application, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0057] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining this application and are not intended to limit this application.

[0058] It is understood that, without conflict, the various embodiments and features in the embodiments of this application can be combined with each other.

[0059] It is understood that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, while parts unrelated to this application are not shown in the drawings.

[0060] It is understood that each unit or module involved in the embodiments of this application may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure.

[0061] It is understood that the terms "first," "second," etc., used in the embodiments of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0062] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this application may occur in a different order than those marked in the accompanying drawings.

[0063] It is understood that the flowcharts and block diagrams of this application illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagrams and flowcharts may be implemented using a hardware-based system to implement the specified function, or using a combination of hardware and computer instructions.

[0064] It is understood that the units and modules involved in the embodiments of this application can be implemented by software or by hardware. For example, the units and modules can be located in the processor.

[0065] It is understood that the specific values ​​of each parameter in this application are merely illustrative examples, and in practical applications, the parameters can be optimized and adjusted based on specific requirements.

[0066] Multi-station cooperative positioning systems can be classified according to different physical characteristics of electromagnetic signals, including Angle of Arrival (AOA) based on angle information, Time Difference of Arrival (TDOA) based on time information, and Frequency Difference of Arrival (FDOA) based on frequency information.

[0067] Among them, the angle of arrival (AOA) positioning technology based on angle information achieves positioning by measuring the azimuth angle of the target signal arriving at the observation station. However, its accuracy is quite sensitive to distance, and even a small deviation in direction finding can lead to significant positioning errors.

[0068] In contrast, Time Difference of Arrival (TDOA) positioning technology, which uses time information, constructs observation equations by calculating the time difference of electromagnetic waves arriving at different observation stations. It has the advantages of simple algorithms and rapid response. However, the applicability of this method is limited when there is relative motion between the observation station and the target radiation source.

[0069] To address positioning needs in motion scenarios, Frequency Difference Positioning (FDOA) based on frequency information analyzes signal frequency domain characteristics and combines them with narrowband filtering technology to effectively suppress multipath effects and noise interference, making it particularly suitable for complex electromagnetic environments such as urban canyons and underwater environments.

[0070] From the perspective of algorithm principles, passive positioning technology can be divided into two main methods: two-step positioning and direct positioning.

[0071] Two-step localization requires extracting intermediate parameters such as signal arrival time difference and frequency shift before calculating target coordinates based on geometric equations. While this method is relatively mature, its core flaw lies in the error propagation chain; the estimation error of intermediate parameters is amplified step-by-step through nonlinear equations to the final localization result. Furthermore, in multi-target scenarios, complex data association and matching are required, easily leading to signal-target mismatch. Direct localization, on the other hand, directly calculates target spatial coordinates from the original signal using a joint optimization model. By eliminating the intermediate parameter estimation step, signal energy utilization is significantly improved. Due to the enhanced weak signal detection capability caused by spatial coherence accumulation, effective localization can still be achieved even in ultra-low signal-to-noise ratio environments. Moreover, the multi-source separation mechanism based on signal subspace decomposition naturally decouples aliased signals, eliminating the need for manual data association processes and maintaining high association accuracy in multi-target dense scenarios.

[0072] Compared to the two-step method, the direct positioning method integrates the spatiotemporal correlation of signals through a global optimization framework, eliminates information loss caused by parameter segmentation, and reconstructs the technical boundaries of positioning accuracy and anti-interference capability from the principle level, providing a more adaptable solution for high-precision passive positioning in complex dynamic scenarios.

[0073] However, existing technologies that use a two-step positioning method to locate radiation sources require the collection of various data and the calculation of the target's position through positioning equations. This separates parameter estimation from position calculation, resulting in a large error between the calculation results and the actual position of the radiation source. Consequently, the accuracy of passive positioning is not high enough, or it may even fail.

[0074] Particle swarm optimization (PSO) is an optimization method based on swarm intelligence. Its core mechanism is to explore the solution space by simulating the social behavior of flocks of birds or schools of fish, utilizing information interaction and position updates among particles. However, this algorithm exhibits two significant characteristics during its iterative process: First, when particles search the multidimensional space by tracking individual and swarm optimal solutions, the random factor in its velocity update formula, while maintaining population diversity, can easily lead to oscillations in flat regions, affecting convergence accuracy. Second, the learning factor, as a key parameter controlling individual cognition and social experience, directly affects the balance between the algorithm's development and exploration capabilities. When the cognitive factor is too large, it may fall into local optima, while an excessively high social factor can easily lead to premature convergence.

[0075] To improve the accuracy of passive positioning, this application provides a multi-station cooperative passive positioning method based on particle swarm optimization (PSO). First, a radiation source signal data receiving model incorporating Doppler frequency shift is established, and data is collected from the radiation source. Then, based on the radiation source data receiving model and the data collected from multiple stations, combined with the log-likelihood function of the radiation source location observation, a cost function for direct positioning by multiple moving stations is constructed. Next, based on the established cost function, an improved PSO algorithm is used to roughly estimate the radiation source location. Finally, a quasi-Newton method is used to perform a high-precision estimation of the radiation source location, and the estimation result is output. This application can improve positioning performance; compared to the ordinary grid search method, the combination of PSO and the quasi-Newton method can improve positioning accuracy.

[0076] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0077] This application provides a multi-station collaborative passive positioning method. The working process of this method can be implemented by electronic devices, such as computers, handheld smart terminals, etc. For ease of explanation, the implementation subject of the method in each embodiment of this application is described as a computer.

[0078] Figure 1 This is a schematic diagram of the multi-station cooperative passive positioning method provided in the embodiments of this application, as shown below. Figure 1 As shown, this application provides a multi-station cooperative passive localization method, which includes S1-S4, as follows:

[0079] S1. Establish a signal model based on the radiation source signal data received by multiple receivers;

[0080] The signal emitted by the radiation source will be subjected to a Doppler frequency shift due to the relative motion of the receiver (also known as the receiving station) moving at a constant speed in different directions. This application describes this frequency shift change by collecting actual signal data and establishing a signal model.

[0081] In some embodiments, S1 includes:

[0082] Based on the radiation source signal data received by multiple receivers, the Doppler frequency shift caused by the relative motion of the multiple receivers is determined, and a signal model based on the Doppler frequency difference is established.

[0083] In some embodiments, a passive localization model based on Doppler frequency difference is established, specifically including the following:

[0084] Consider a fixed radio radiation source and a mobile one. There are 1 receiver. Assume the receivers are synchronized in frequency and time. The location of the radiation source is determined by coordinate vectors. Indicated. Each receiver travels along its trajectory with A short-interval interception of transmitted signals. Assume... and ,in and They represent the first The receiver in the first The position and velocity vectors of each interception interval. The receiver in the first The complex signal observed at each intercept interval is:

[0085] ;

[0086] The above formula can be written in vector form as follows:

[0087] ;

[0088] in:

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] in, K represents the number of signal interceptions, i.e., the number of times the receiving station intercepts transmitted signals during its movement. L represents the number of receiving stations, i.e., the number of receivers participating in the positioning process. The received signal at the receiving station is represented as the superposition of the transmitted signal and ambient noise. The attenuation factor during propagation represents the decrease in signal energy with distance. The white noise inherent in the environment, The signal emitted by the radiation source is generally an unknown signal, and N is the number of sampling points for each interception. The phase delay matrix is ​​the matrix. It is a function matrix representing the location of an unknown radiation source. The frequency difference matrix, the matrix It is a function matrix representing an unknown transmission frequency. This is due to an unknown emission frequency shift caused by source instability. This is due to the Doppler frequency shift caused by the relative motion between the receiving station and the radiation source.

[0095] S2. Based on the signal model and the radiation source signal data, construct the cost function for direct positioning of multiple motion stations;

[0096] In this step, based on the radiation source data receiving signal model and the signal data collected by multiple stations from the radiation source, combined with the log-likelihood function of the radiation source location observation, a cost function for direct positioning by multiple moving stations is constructed, and the most likely location of the radiation source is deduced.

[0097] In some embodiments, S2 includes S21-S24, as follows:

[0098] S21. Construct the log-likelihood function of the observation vector using the maximum likelihood estimation method;

[0099] Specifically, locate the radiation source. This ensures that the signal model best matches the actual received signal. Using the maximum likelihood estimation method, the log-likelihood function of the observation vector is given by the following equation:

[0100] ;

[0101] in, The noise variance is the inherent white noise of the environment.

[0102] S22. Simplify the log-likelihood function into a cost function. ;

[0103] Specifically, without loss of generality, we assume... ,because Since it is independent of parameters, the cost function can be maximized. To estimate the location:

[0104] .

[0105] S23. Simplify the cost function into matrix form to obtain the modified covariance matrix;

[0106] Specifically, the cost function Further simplifying to matrix form, where, is defined vector as well as Hermitian matrix :

[0107] ;

[0108] ;

[0109] ;

[0110] Due to the matrix The dimension is Therefore, it increases with the number of data samples, leading to an increase in computational cost. To reduce computational cost, this application will... matrix Replace with matrix :

[0111] ;

[0112] in, The corrected covariance matrix is ​​a correction matrix made to reduce computational cost while maintaining performance, when it satisfies... This will greatly reduce the computational load.

[0113] S24. Obtain the location cost function for locating the radiation source based on the modified covariance matrix.

[0114] Specifically, by seeking Maximum eigenvalue Construct an optimization function for the location space, in which the matrix is... Replace with matrix :

[0115] ;

[0116] in, The location cost function exhibits As the location changes, the location is determined by finding the maximum or minimum value of the function. The estimated location of the radiation source is given by the following formula:

[0117] ;

[0118] in, The location of the radiation source, i.e. the object to be located, is estimated using the above equation, which is more accurate than the two-step location method.

[0119] S3. Based on the cost function, the location of the radiation source is roughly estimated by the improved particle swarm optimization algorithm to obtain the first localization result;

[0120] In some embodiments, S3 includes S31-S35, specifically as follows:

[0121] S31. Initialization parameters: Initialize the basic parameters of the particle swarm algorithm;

[0122] Specifically, the basic parameters of the particle swarm optimization algorithm are set, including the number of particles, the number of variables, and the individual learning factor. Social learning factors Etc. Define the lower and upper bounds of the search space. During initialization, the position of each particle is randomly generated within the defined domain, and its velocity is randomly assigned within a certain range.

[0123] S32. Calculate fitness: Call the objective function to calculate the fitness value of each particle, record the current position of the particle as the individual optimal position, and select the position with the smallest fitness from all particles as the global optimal position.

[0124] S33. Iteratively update particle state: In each iteration, adjust the velocity based on the individual optimal position, the global optimal position, and random perturbation, and update the position according to the new velocity;

[0125] In some embodiments, in S33, the speed update formula is:

[0126] ;

[0127] Then, the position is updated based on the new speed. The position update formula is:

[0128] ;

[0129] Where pbest is the individual optimal position, which is the best fitness position reached by a particle in the entire search process so far in the improved particle swarm algorithm; gbest is the global optimal position, which is the best fitness position found by all particles in the entire particle swarm in the search process so far in the improved particle swarm algorithm; w is the inertia weight, which is a weighting coefficient for updating particle velocity and is used to adjust the overall convergence speed of particles; c1 is the individual learning factor, which is used to control the degree to which a particle is affected by its individual optimal position (pbest); and c2 is the social learning factor, which is used to control the degree to which a particle is affected by the group's historical best position (gbest).

[0130] S34. Boundary handling and iteration: If a particle flies out of the preset range, it is corrected to the nearest boundary value, and the current fitness is recalculated. If it is better than the individual optimal value, the individual optimal position is updated; if it is better than the global optimal value, the global optimal position is updated. The speed and position are updated repeatedly until the maximum number of iterations is reached or convergence is achieved.

[0131] Specifically, if a particle flies out of the preset range, i.e., its position exceeds the defined domain, it is corrected to the nearest boundary value. Then, the current fitness is recalculated. If it is better than the individual optimal value, the individual optimal position is updated; if it is better than the global optimal value, the global optimal position is updated. The speed and position are updated repeatedly until the maximum number of iterations is reached or convergence occurs, i.e., the particle swarm gathers into a small range.

[0132] S35. Output results: After the iteration is completed, the global optimal position is output as the first localization result.

[0133] Specifically, after the iteration is complete, the global optimal position is output, which is the position found among all particles that is closest to the radiation source, and its corresponding minimum fitness value.

[0134] For example, the number of particles in the particle swarm optimization algorithm can be set to 20, the number of variables to 2, the inertia weight to 0.7298, and the individual learning factor and social learning factor to 2.05. The particle swarm optimization algorithm can be iterated 30 times to obtain a rough localization estimate.

[0135] S4. Based on the first positioning result, the position of the radiation source is estimated with high precision using the quasi-Newton method to obtain a second positioning result, which is the passive positioning result of the radiation source.

[0136] In some embodiments, S4 includes S41-S44, specifically as follows:

[0137] S41. Initialization: Initialize the Hessian inverse matrix to the identity matrix, and calculate the initial gradient based on the first localization result and the identity matrix.

[0138] Specifically, the initial position estimate x0 is obtained from the particle swarm optimization algorithm, the Hessian inverse matrix is ​​initialized to the identity matrix H0, and the initial gradient g0 is calculated.

[0139] S42. Iterative optimization: In each iteration, the search direction is calculated based on the current Hessian inverse matrix and the current gradient. After determining the optimal step size through line search, the position estimate is updated, the gradient of the new position is calculated, and the Hessian inverse matrix is ​​updated.

[0140] Specifically, in each iteration, the search direction is first calculated based on the current Hessian inverse matrix and gradient. After determining the optimal step size through line search, the position estimate is updated. Then, the gradient at the new position is calculated, and finally, the Hessian inverse matrix is ​​updated using the BFGS formula.

[0141] ;

[0142] in:

[0143] ;

[0144] in, The approximation for the Hessian inverse matrix is ​​the th... The inverse of the Hessian matrix of the objective function during each iteration. An approximation, This is an auxiliary matrix term used in the BFGS algorithm to update the Hessian inverse matrix. It contains information about the changes in variables and gradients before and after the current iteration.

[0145] S43. Convergence Judgment: Determine whether the gradient change satisfies the convergence condition. If it does, stop iterating; otherwise, continue iterating.

[0146] Specifically, check if the gradient change satisfies the convergence condition; if the gradient g i+1 If the value is close to zero (or changes very little), it indicates that an extreme point has been found, and the iteration should be stopped; otherwise, the iteration should continue.

[0147] S44. Output result: By dynamically adjusting the Hessian inverse matrix and combining gradient information, fast convergence is achieved, and the final position is the second localization result.

[0148] Specifically, by dynamically adjusting the Hessian inverse matrix and combining it with gradient information, fast convergence is achieved, and the final position x is obtained. i+1 That is, the high-precision location estimation result of the radiation source.

[0149] For example, set the maximum number of iterations to 100, and the gradient tolerance and function value tolerance to 10. -5 The high-precision position estimate is obtained by iterative operation.

[0150] This application provides a multi-station collaborative passive positioning method, which can achieve rapid and accurate radiation source positioning in scenarios such as emergency communication, disaster relief, and large-scale event support, significantly improving the system's intelligence level and response efficiency. In large-scale event support, by real-time monitoring of illegal drone signals or interference sources, combined with the passive positioning technology of this application, rapid source tracing can be achieved, ensuring communication security. By combining the passive positioning algorithm of this application with distributed networking capabilities, a low-cost, high-precision, and rapidly deployable emergency support solution can be created, promoting the development of emergency communication technology towards intelligence and collaboration.

[0151] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0152] Figure 2 This is a schematic diagram of the multi-station collaborative passive positioning device provided in the embodiments of this application, as shown below. Figure 2 As shown, this application provides a multi-station cooperative passive positioning device, the device comprising:

[0153] The signal model building module 11 is configured to build a signal model based on radiation source signal data received by multiple receivers.

[0154] The cost function construction module 12 is configured to construct a cost function for motion multi-station direct positioning based on the signal model and the radiation source signal data.

[0155] The coarse estimation module 13 is configured to perform a coarse estimation of the radiation source location based on the cost function using an improved particle swarm optimization algorithm to obtain a first positioning result.

[0156] The high-precision estimation module 14 is configured to perform high-precision estimation of the radiation source position based on the first positioning result using the quasi-Newton method to obtain a second positioning result, wherein the second positioning result is the passive positioning result of the radiation source.

[0157] Regarding the limitations on the multi-station collaborative passive positioning device, please refer to the limitations on the multi-station collaborative passive positioning method in the above embodiments of this application, which will not be repeated here.

[0158] Figure 3 Another schematic diagram of the multi-station cooperative passive positioning device provided in the embodiments of this application is shown below. Figure 3 As shown, the device includes a memory 22 and a processor 21. The memory stores a computer program, and the processor is configured to run the computer program to perform the methods described in the above embodiments of this application.

[0159] The memory is connected to the processor. The memory can be flash memory, read-only memory or other types of memory. The processor can be a central processing unit or a microcontroller.

[0160] In some embodiments, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described in the above embodiments of this application.

[0161] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, computer program modules or other data. Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0162] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A multi-station cooperative passive localization method, characterized in that, The method comprises: S1, establishing a signal model according to radiation source signal data received by multiple receivers; S2, constructing a cost function of motion multi-station direct positioning according to the signal model and the radiation source signal data; S3, coarsely estimating the position of the radiation source by using an improved particle swarm algorithm according to the cost function to obtain a first positioning result; S4, highly accurately estimating the position of the radiation source by using a quasi-Newton method according to the first positioning result to obtain a second positioning result, wherein the second positioning result is a passive positioning result of the radiation source.

2. The multi-station cooperative passive localization method of claim 1, wherein, S1, comprising: determining Doppler frequency shifts of the multiple receivers caused by relative motion according to radiation source signal data received by the multiple receivers, and establishing a signal model based on the Doppler frequency shifts.

3. The multi-station cooperative passive localization method of claim 2, wherein, The signal model based on the Doppler frequency shifts is: ； ； ； ； ； ； ； wherein, K is the number of intercepted signals, L is the number of receiving stations, is the received signal, is the attenuation factor, is the white noise, is the radiation source emission signal, N is the number of sampling points, is the phase delay matrix, is the frequency difference matrix, is the unknown emission frequency shift, is the Doppler shift.

4. The multi-station cooperative passive localization method of claim 1, wherein, S2, comprising: S21, constructing a log-likelihood function of an observation vector by using a maximum likelihood estimation method; S22, simplifying the log-likelihood function into a cost function; S23, simplifying the cost function into a matrix form to obtain a modified covariance matrix; S24, obtaining a positioning cost function for positioning the position of the radiation source according to the modified covariance matrix.

5. The multi-station cooperative passive localization method of claim 1, wherein, S3, comprising: S31, initializing basic parameters of the particle swarm algorithm; S32, calling a target function to calculate fitness values of each particle, recording a current position of the particle as an individual optimal position, and selecting a position with the minimum fitness from all the particles as a global optimal position; S33, in each iteration, adjusting a speed based on the individual optimal position, the global optimal position and a random disturbance, and updating the position according to the new speed; S34, if a particle flies out of a preset range, correcting the particle to the nearest boundary value, and recalculating a current fitness, if the current fitness is better than an individual optimal value, updating the individual optimal position, if the current fitness is better than a global optimal value, updating the global optimal position, repeatedly updating the speed and the position until a maximum iteration number is reached or convergence is achieved; S35, outputting the global optimal position as the first positioning result after the iteration is completed.

6. The multi-station cooperative passive localization method of claim 5, wherein, In S33, the speed updating formula is: ； The position updating formula is: ； wherein pbest is the individual optimal position, gbest is the global optimal position, w is an inertia weight, c1 is an individual learning factor, and c2 is a social learning factor.

7. The multi-station cooperative passive localization method of claim 1, wherein, S4, comprising: S41, initializing a Hessian inverse matrix as a unit matrix, and calculating an initial gradient according to the first positioning result and the unit matrix; S42, in each iteration, calculating a search direction based on a current Hessian inverse matrix and a current gradient, updating a position estimate after determining an optimal step length by line search, calculating a gradient of the new position, and updating the Hessian inverse matrix; S43, judging whether a gradient change satisfies a convergence condition, if yes, stopping the iteration, otherwise, continuing the iteration; S44, achieving fast convergence by dynamically adjusting the Hessian inverse matrix in combination with gradient information to obtain a final position as the second positioning result.

8. A multi-station cooperative passive location device, characterized by, The device comprises: a signal model establishing module configured to establish a signal model according to radiation source signal data received by multiple receivers; a cost function construction module configured to construct a cost function of multi-station direct localization according to the signal model and the emitter signal data; a coarse estimation module configured to coarsely estimate the emitter position by using an improved particle swarm optimization algorithm according to the cost function, and obtain a first positioning result; a high-precision estimation module configured to accurately estimate the emitter position by using a quasi-Newton method according to the first positioning result, and obtain a second positioning result, which is a passive positioning result of the emitter.

9. A multi-station cooperative passive location device, characterized by, A computer readable storage medium having a computer program stored therein, wherein the computer program is executed by a processor to implement the multi-station cooperative passive positioning method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer readable storage medium having a computer program stored therein, wherein the computer program is executed by a processor to implement the multi-station cooperative passive positioning method according to any one of claims 1-7.

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