A Single-Station Passive Localization Method Based on an Improved Genetic-Particle Swarm Optimization Algorithm

Abstract

This invention specifically relates to a single-station passive localization method based on an improved genetic-particle swarm optimization algorithm, comprising: measuring the phase difference using an interferometer; obtaining an estimate of the phase difference change rate using a Kalman filter algorithm; further obtaining a coarse estimate of the target radiation source position using the improved genetic-particle swarm optimization algorithm based on the estimated phase difference change rate; and using the coarse estimate of the target radiation source position as prior information, performing iterative calculations using a nonlinear least squares method to obtain a fine estimate of the target radiation source position. This method does not require phase difference de-ambiguity resolution or azimuth angle prior information, and can be implemented using only a single-baseline interferometer formed by two array elements. It has advantages such as no need to measure angles, no need for phase difference de-ambiguity resolution, fast convergence speed, and high localization performance.

Description

Technical Field

[0001] This invention relates to the field of passive positioning in electronic warfare, specifically to a single-station passive positioning method based on an improved genetic-particle swarm optimization algorithm, which is used for real-time positioning of fixed or slow-moving targets by a single station of motion observation in ground or low-altitude scenarios. Background Technology

[0002] In increasingly complex electromagnetic environments, electronic countermeasures technology plays an increasingly important role. Among these, high-precision passive positioning technology, as an effective means of silent reconnaissance, occupies a crucial position in the field of electronic reconnaissance due to its characteristic of not requiring the active emission of electromagnetic signals. Single-station passive positioning technology is an important branch of passive positioning technology categorized by the number of observation stations. Its principle lies in receiving the electromagnetic wave signals radiated by the target through a single observation platform, and then calculating the target's position by combining kinematic equations and signal processing algorithms. Compared to multi-station passive positioning technology, this method has advantages such as strong concealment and flexible deployment, and has significant research value in scenarios such as spectrum monitoring and UAV collaboration.

[0003] In recent years, there has been much research in the field of passive radar localization. For example, the angle-based cross-location method works by calculating the direction of arrival (DOA) at different times corresponding to azimuth curves in space when there is relative motion between the target radiation source and the observation platform. Multiple curves intersect at the target's true position, allowing for the calculation of coordinates. However, this method requires accumulating sufficient azimuth changes, has a long convergence time, and suffers from small azimuth changes when the target moves radially relative to the radar, leading to inaccurate localization. The time-of-arrival (TOA) method utilizes the time delay difference between the signal received by the observation platform from the target radiation source and the propagation path changes caused by the relative motion between the target and the radar. By establishing a spatial relationship between the time difference sequence and the target's kinematic model, the target's position and velocity can be calculated. This method requires high-precision time measurement equipment and, in scenarios involving a moving observation platform, needs to compensate for the impact of its own motion on the time delay difference, making calibration challenging. Doppler frequency localization is based on the Doppler frequency shift effect caused by the relative motion between the target radiation source and the radar. During this process, the radial velocity between the observation platform and the target dynamically changes, resulting in a continuous frequency shift in the signal. The amount of this shift is related to the instantaneous motion parameters of the observation platform and the target position. By recording the Doppler frequency shift sequence, a set of nonlinear observation equations can be constructed, and the target position can then be calculated. When the observation platform's speed is slow or when the observation platform is moving tangentially relative to the target, the change in Doppler frequency shift is not significant enough, which will greatly affect the localization effectiveness. Furthermore, this method has high requirements for the received signal; it needs to be a continuous signal or a long-duration pulse signal.

[0004] Compared to the observation parameters used in the methods mentioned above, phase difference data is more sensitive to the relative displacement of the target, achieving higher positioning accuracy. Furthermore, phase difference has the advantage of strong anti-interference capability in complex electromagnetic environments. Therefore, this invention uses this parameter for target positioning. Existing technologies propose a direction-finding-phase difference change rate positioning method, which calculates distance by fusing the target's angle of arrival (AOA) with the phase difference change characteristics. However, this method requires an additional baseline on the hardware, increasing hardware costs and signal processing complexity. Moreover, this method requires prior azimuth information, which is obtained by de-ambiguing the phase difference using an interferometer, introducing errors. When calculating the target position, both the phase difference change rate error and the direction-finding error will simultaneously affect its positioning performance. Therefore, to address the problems in existing technologies and reduce hardware costs, it is necessary to improve the original algorithm structure and incorporate intelligent optimization algorithms to improve its positioning accuracy. Simultaneously, the improved algorithm needs to maintain good real-time performance.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] Existing passive radar localization methods that utilize the phase difference change rate require measuring angle information or solving for the azimuth angle using two baselines before locating the target. To address these issues, this invention provides a passive radar localization method based on an improved genetic-particle swarm optimization algorithm, enabling high-precision passive radar localization.

[0007] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0008] According to a first aspect of the present invention, a single-station passive localization method based on an improved genetic-particle swarm optimization algorithm is provided, the method comprising:

[0009] The phase difference is measured using an interferometer, and the phase difference is then used to obtain an estimate of the rate of change of the phase difference through a Kalman filter algorithm.

[0010] Based on the estimated value of the phase difference change rate, a coarse estimate of the target radiation source location is obtained by using an improved genetic-particle swarm optimization algorithm.

[0011] Using the coarse estimate of the target radiation source location as prior information, the nonlinear least squares method is used for iterative calculation to obtain the fine estimate of the target radiation source location.

[0012] In some exemplary embodiments, obtaining an estimate of the phase difference change rate using a Kalman filter algorithm specifically involves:

[0013] The rate of change of phase difference was calculated using the finite difference method.

[0014] The phase difference and the rate of change of the phase difference are used as state variables, and the phase difference is used as the observation variable.

[0015] Based on the state variables and observation variables, state equations and observation equations are established respectively, and Kalman filter equations are established.

[0016] The estimated value of the phase difference change rate is obtained through the Kalman filter equation.

[0017] In some exemplary embodiments, the calculation of the phase difference change rate using the differential method includes two cases:

[0018] First scenario:

[0019] When the sampling time interval is much shorter than the phase difference change period, the phase difference change rate is calculated using the differential method, and the formula used is as follows:

[0020]

[0021] in, Let nT be the rate of change of the phase difference. This represents the true phase difference at time nT. Let be the measurement ambiguity phase difference at time nT, where T is the sampling time interval;

[0022] The second scenario:

[0023] When the sampling time interval is greater than the phase difference change period, the phase difference change rate is calculated using the differential method, and the formula used is as follows:

[0024]

[0025] In some exemplary embodiments, the estimated value based on the phase difference change rate is further used to obtain a coarse estimate of the target radiation source location using an improved genetic-particle swarm optimization algorithm, including:

[0026] The observation area of ​​the observation platform is selected based on the prior fuzzy distance;

[0027] Based on the observation area of ​​the observation platform, chaotic sequences are used to initialize the position and velocity of particles;

[0028] The particle fitness value is constructed based on the estimated value of the phase difference change rate, and the individual optimal position and the population optimal position are updated based on the particle fitness value;

[0029] Based on the optimal position explored by the entire particle swarm from beginning to end, a corresponding chaotic sequence is generated;

[0030] The particle in the optimal position in the chaotic sequence is used to replace the position of one particle in the current particle swarm.

[0031] Repeat the iteration until the stopping condition is met, that is, the maximum number of generations or the specified fitness value is reached.

[0032] In some exemplary embodiments, the particle swarm optimization algorithm employs an embedded hybrid approach, incorporating crossover and mutation operations from the genetic algorithm into the particle swarm algorithm to increase the diversity of the particle population and expand the search range of the algorithm.

[0033] In some exemplary embodiments, the step of using a coarse estimate of the target radiation source location as prior information and performing iterative calculations using a nonlinear least squares method to obtain a fine estimate of the target radiation source location includes:

[0034] The average of the optimal positions of the population after the improved genetic-particle swarm optimization algorithm at each sampling time is selected as the initial value for locating the target radiation source;

[0035] The position of the target at time t can be obtained by iterative recursion using the nonlinear least squares method.

[0036] In some exemplary embodiments, the method further includes:

[0037] Monte Carlo simulation tests were conducted, and the process of accurately estimating the location of the target radiation source was repeated several times. The final location result was obtained by averaging the results of each accurate estimation of the target radiation source location.

[0038] According to a second aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the single-station passive localization method based on the improved genetic-particle swarm optimization algorithm described in the first aspect.

[0039] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the single-station passive localization method based on the improved genetic-particle swarm optimization algorithm described in the first aspect above.

[0040] According to a fourth aspect of the present invention, an electronic device is provided, comprising:

[0041] Processor; and

[0042] Memory for storing the executable instructions of the processor;

[0043] The processor is configured to implement the single-station passive localization method based on the improved genetic-particle swarm optimization algorithm described in the first aspect above by executing the executable instructions.

[0044] The single-station passive localization method based on an improved genetic-particle swarm optimization algorithm provided in this invention does not require phase difference de-ambiguity resolution or azimuth prior information. It can be implemented using only a single-baseline interferometer formed by two array elements, resulting in a relatively simple system structure. The acquisition of the phase difference change rate and the localization model are derived in detail. The phase difference change rate is obtained using the finite difference method and Kalman filtering. A nonlinear relationship is directly established using the target radiation source position and the phase difference change rate. The improved genetic-particle swarm optimization algorithm is used to obtain the initial localization solution, and then nonlinear least squares method is applied for localization. Compared with traditional methods, it has the advantage of not requiring additional angle information. Localization performance curves were plotted through simulation. Simulation results show that this method has good localization performance for fixed targets, exhibiting advantages under different phase difference change rate errors and at different frequencies.

[0045] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0047] Figure 1 A schematic diagram of a fuzzy phase difference as an exemplary embodiment of the present invention;

[0048] Figure 2 Another schematic diagram of the fuzzy phase difference as an exemplary embodiment of the present invention;

[0049] Figure 3 A schematic diagram of a positioning model as an exemplary embodiment of the present invention;

[0050] Figure 4 A flowchart illustrating an exemplary embodiment of the present invention;

[0051] Figure 5 This is a comparison chart of positioning performance under different phase difference change rate errors, which is an exemplary embodiment of the present invention.

[0052] Figure 6 A comparison of positioning performance at different frequencies, which is an exemplary embodiment of the present invention. Detailed Implementation

[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0054] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0055] In complex electromagnetic environments, radar monostation passive positioning offers advantages such as no signal transmission, strong concealment, simple equipment, and low cost, thus showing promising application prospects. Existing radar monostation passive positioning methods utilizing the phase difference change rate require measuring angle information or using two baselines to solve for the azimuth angle before locating the target.

[0056] To address the shortcomings and deficiencies of existing technologies, this example embodiment provides a single-station passive localization method based on an improved genetic-particle swarm optimization algorithm for target location. First, the phase difference is estimated using a Kalman filter algorithm to obtain the rate of change of phase difference. Then, based on this estimate, the improved genetic-particle swarm optimization algorithm is used to further obtain a coarse estimate of the target radiation source's location. Finally, the coarse location estimate is used as prior information, and a nonlinear least squares method is used for iterative calculation to obtain a precise estimate of the target radiation source's location. While the nonlinear least squares algorithm is simple and has high positioning accuracy, it is greatly affected by the selection of initial values. The proposed method utilizes the prior information carried by the coarse estimation result, significantly improving the impact of initial values ​​on the nonlinear least squares method, eliminating the need for highly accurate initial values ​​and enabling precise positioning over a wider ambiguity range. This method offers advantages such as no need for angle measurement, no need for phase difference de-ambiguity, fast convergence speed, and high positioning performance.

[0057] refer to Figure 1 As shown, the specific steps may include:

[0058] Step 1: Use the differential method and Kalman filtering to obtain the phase difference rate of change at each sampling time;

[0059] Step 2: Select the observation area X of the observation platform based on the prior fuzzy distance.max ,X min The values ​​are set as follows: maximum number of generations N, population size M, and fitness threshold Q.

[0060] Step 3: Calculate the phase difference rate of change at each moment to generate initial particle positions and initial velocities within the observation area. Calculate the fitness value and update the individual optimal position and the population optimal position.

[0061] Step 4: Update particle position and velocity;

[0062] Step 5: Perform crossover and mutation operations;

[0063] Step 6: Perform chaos optimization at the optimal position;

[0064] Step 7: Repeat steps 4 to 6 until the stopping condition is met, that is, the maximum number of generations or the specified fitness value is reached;

[0065] Step 8: Select the average of the optimal positions at each sampling time as the initial value for locating the target radiation source, and perform nonlinear least squares iteration to obtain the target location;

[0066] Step 9: To verify the repeatability of the algorithm, repeat the above steps to perform a Monte Carlo simulation test, setting the number of trials to 100. Average the results of each trial to obtain the final localization result.

[0067] The steps in this exemplary embodiment will now be described in more detail with reference to the accompanying drawings and embodiments.

[0068] Step 1: Use the differential method and Kalman filtering to obtain the phase difference rate of change at each sampling time.

[0069] When measuring phase difference using an interferometer, the measured phase difference is affected because the baseline distance exceeds half a wavelength. Phase blurring can occur. One possibility is that, within a certain time period, T is much smaller than the phase difference change period T. C ,like Figure 2 As shown, the phase difference obtained by observing and sampling the interferometer at the same time interval T can be considered to have a constant degree of change. Therefore, the phase ambiguity problem can be eliminated by differential processing. The principle is to calculate the phase difference between two adjacent sampling points and then use the ratio of this difference to the observation time interval as an approximate estimate of the rate of change of the phase difference. If the obtained result shows a large fluctuation, it is determined that a 2π jump has occurred in the phase difference.

[0070] For example, the rate of change of phase difference at time nT can be obtained from the above-mentioned difference method idea:

[0071]

[0072] in, Let nT be the rate of change of the phase difference. This represents the true phase difference at time nT. Let n be the measurement ambiguity phase difference at time nT; T is the sampling time interval.

[0073] Another scenario considers T to be greater than the phase difference change period T. C ,like Figure 3 As shown. At this point, discussion and calculation are needed. Based on the idea of ​​the finite difference method, we can obtain:

[0074]

[0075] When processing the phase difference rate of change data obtained by the differential method using Kalman filtering, the state variable is set as: The observed variables are: Where i = nT. Establish the state equation and observation equation as shown below.

[0076] Equations of state:

[0077]

[0078] Observation equation:

[0079]

[0080] Matrix A represents the state transition matrix, and matrix H represents the observation matrix. Both system noise w(i) and observation noise v(i) satisfy the zero-mean Gaussian white noise characteristic. Let Q(i) be the covariance matrix of w(i), and R(i) be the covariance matrix of v(i). The phase difference rate of change can be obtained using the Kalman filter equation shown below.

[0081]

[0082] The discussion will focus on the positioning model, as illustrated in the diagram below. Figure 4 As shown, the localization scenario involves a single observation platform performing passive localization of a fixed target. Assume the target radiation source is located in the far field, the interferometer baseline length is d, the speed of light is c, and the frequency of the incoming wave is f. C The azimuth angle of the incoming wave is β(t). The phase difference of the target radiation source is obtained. for:

[0083]

[0084] Assuming d is much smaller than the distance between the target and the observation platform, the derivative is:

[0085]

[0086] In the formula: These are the rate of change of phase difference and the rate of change of azimuth angle of arrival, respectively. The coordinates of the fixed target radiation source are set as (x...). T ,y T The coordinates of the observation platform at time t are (x, y). O (t),y O (t)), then based on the geometric relationship of the azimuth angle, the following mathematical model can be established:

[0087]

[0088] The derivative is shown below. Wherein, These represent the velocity of the observation platform along the x-axis and y-axis, respectively.

[0089]

[0090] Therefore, we can conclude that:

[0091]

[0092] Step 2: Select the observation area X of the observation platform based on the prior fuzzy distance. max ,X min The maximum number of generations is set to N, the population size to M, and the fitness threshold to Q, laying the foundation for stopping subsequent algorithm iterations.

[0093] Step 3: Calculate the phase difference rate of change at each moment. A chaotic sequence is used to initialize the particle's position and velocity; and a corresponding chaotic sequence is generated based on the optimal position found by the entire particle swarm throughout the process. Subsequently, the particle at the optimal position in this chaotic sequence replaces the position of one particle in the current particle swarm. In this invention, a representative Logistic chaotic system was selected. When initializing the particles, X is set... min ≤X i (t)≤X max V min ≤V i (t)≤V max The initial particle position and initial velocity are generated using the following formula:

[0094]

[0095] With a control parameter μ = 4, and a random variable z0 uniformly distributed in the interval 0 to 1, the system will be in a completely chaotic state. Given any two different initial values ​​z0, iterative calculations can yield a distinct set of chaotic sequences.

[0096] Next, the fitness value f is calculated according to the following formula, where This estimates the location of the target radiation source. Simultaneously, it updates the optimal location for individuals and the optimal location for the entire population.

[0097]

[0098] Step 4: In the particle swarm optimization algorithm, each particle represents the estimated location of the target radiation source. i is the particle index; the optimal position that particle i has experienced is P. i (t)=[p i,1 (t),p i,2 [(t)]; where the flight speed is V. i (t)=[v i,1 (t),v i,2 The optimal positions experienced by the entire particle swarm are G(t) = [g1(t), g2(t)]. The particle position update formula within time t is:

[0099]

[0100] Where w is the inertia weight; c1 and c2 are acceleration factors; and r1 and r2 are random numbers uniformly distributed from 0 to 1. In the method of this invention, both the inertia weight w and the acceleration factors c1 and c2 are adaptively adjusted. i It is the fitness value of the particle at the current iteration number; f avg f is the fitness value of the particle at its optimal position. g f′ is the fitness value of the particle at its optimal position. avg To be better than f avg The average of the fitness values, i.e., taking f i <f avg Each f that meets the conditions i Let f be the value of f. i ′(a), the number of which is n, a-1,2,...,n, w max To find the maximum value of w, this method takes 1.1; w min The minimum value of w is 0.5 in this method. n is the number of generations in the iteration, and N is the maximum allowed number of generations. The specific settings are as follows:

[0101]

[0102] Step 5: Using an embedded hybrid approach, the crossover and mutation operations from the genetic algorithm are embedded into the particle swarm optimization algorithm to increase the diversity of the particle population and expand the search range of the algorithm, which plays an important role in selecting the maximum distance of the population in the method of this invention.

[0103]

[0104] In the formula, CX i (t),MX i P(t) represents the crossover and mutation results, PX1(t), PX1(t), and X(t) represent the crossover and mutation results, respectively, and rand is a random number uniformly distributed from 0 to 1.

[0105] Step 6: Perform chaotic optimization on the optimal position, z0 is calculated as follows:

[0106]

[0107] Then, the chaotic sequence is generated by iteratively using the above Logistic equation.

[0108] z i+1 =μz i (1-z i ), i = 0, 1, 2, ..., M-1

[0109] Replace and retain the optimal particle positions obtained from the generated chaotic sequence.

[0110] X i (t)=X min +(X max -X min )·z i

[0111] V i (t)=(V max -V min )·z i +V min

[0112] Step 7: Repeat steps 4 to 6 until the stopping condition is met, that is, the maximum number of generations or the specified fitness value is reached.

[0113] Step 8: Select the average of the optimal positions of the population after the improved genetic-particle swarm optimization algorithm at each sampling time as the initial value for locating the target radiation source, denoted as . The position of the target at time t can be obtained through iterative recursion using the nonlinear least squares method.

[0114]

[0115] In the formula,

[0116]

[0117] in, It can be obtained from the following formula:

[0118]

[0119] Step 9: Repeat the above steps to perform Monte Carlo simulation tests, setting the number of trials to 100. Average the results of each trial to obtain the final positioning result.

[0120] The Cramer-Rhodes lower bound is a meaningful and practical evaluation for localization and tracking problems. It reveals the statistically averaged lower bound of the state estimation error for the model under discussion; as the algorithm approaches optimality, the localization accuracy will tend towards the Cramer-Rhodes lower bound. Through derivation, we can obtain:

[0121]

[0122] in,

[0123]

[0124] Generally, the relationship between positioning error and geometry is described using the geometric precision factor (GDOP). Based on the CRLB equation above, GDOP is obtained as:

[0125]

[0126] To further illustrate the performance of the algorithm in estimating target parameter values, the following simulation experiment was conducted. Assuming the target radiation source frequency is 10 GHz, the baseline length on the moving observation platform is 2.5 m, the observer's speed is 100 km / h, and the initial position is [0,0] m, moving in uniform linear motion. The position and attitude information of the observation platform itself can be obtained through the inertial navigation system. The total signal observation time is 20 s, with 100 points sampled uniformly during the observation time, i.e., sampling once every 0.2 s. To select the most suitable population size and generation number under this condition, a comparative analysis experiment was conducted. The table below compares the fitness values ​​under different population sizes and generation numbers.

[0127] Table 1 Comparison of different population sizes and different generations

[0128]

[0129] To improve positioning accuracy while reducing algorithm runtime, data analysis in the table shows that a population size of 110 and an optimal number of generations of 80 are selected. To evaluate positioning performance, 100 Monte Carlo simulations were conducted. X represents the estimated location of the target radiation source obtained from a single simulation. T Let M be the actual location of the target radiation source, and M be the number of Monte Carlo trials. The root mean square error obtained from the simulation is:

[0130]

[0131] When performing real-time positioning of a fixed target, the positioning performance of traditional methods and the method of this invention is analyzed and compared under different phase difference change rates and different frequencies. Figure 5 and Figure 6 These reflect the curve distribution trends in these two cases, respectively.

[0132] In the simulation, it can be observed from the images that the positioning performance of traditional methods is affected by the phase difference rate of change error and frequency changes. When the phase difference rate of change error gradually increases, the root mean square error (RMSE) of traditional methods gradually increases, and the positioning performance gradually decreases; conversely, when the frequency gradually increases, the RMSSE gradually decreases, and the positioning performance gradually improves. However, the method of this invention, due to the use of an improved genetic-particle swarm optimization algorithm, shows less impact on positioning performance within the respective ranges of phase difference rate of change error and frequency. The RMSSE of this method shows a stable upward trend, but the increase is small. Regarding the variation in phase difference rate of change error, this is attributed to the intelligent optimization algorithm, namely the improved genetic-particle swarm optimization algorithm, which averages the phase difference rate of change error through multiple evolutionary iterations, thereby reducing the impact of the error. Regarding the variation in frequency, this is attributed to two factors: firstly, the positioning model uses frequency as a known parameter when calculating the target position, eliminating error; secondly, the improved genetic-particle swarm optimization algorithm itself has global search capabilities, maintaining the same performance even with frequency changes. Simulation results show that, under different phase difference change rate errors and different frequencies, the positioning performance of the method of the present invention has certain advantages over the traditional method, and it has better robustness.

[0133] In summary, this invention proposes a single-baseline positioning method utilizing only the phase difference change rate. This method does not require phase difference deambiguity resolution or prior azimuth information, and can be implemented using only a single-baseline interferometer formed by two array elements, resulting in a relatively simple system structure. The acquisition of the phase difference change rate and the positioning model are derived in detail. The phase difference change rate is obtained using the finite difference method and Kalman filtering. A nonlinear relationship is directly established using the target radiation source position and the phase difference change rate. An improved genetic-particle swarm optimization algorithm is used to obtain the initial positioning solution, and then a nonlinear least squares method is applied for positioning. Simulation results show that, under different phase difference change rate errors and different frequencies, the positioning performance of the proposed method is superior to traditional methods, exhibiting good robustness.

[0134] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments.

[0135] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0136] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0137] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0138] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is defined only by the appended claims.

Claims

1. A single-station passive localization method based on an improved genetic-particle swarm optimization algorithm, characterized in that, The method includes: The phase difference is measured using an interferometer, and the rate of change of the phase difference is estimated using a Kalman filter algorithm; specifically: The rate of change of phase difference is calculated using the finite difference method, including two cases: First scenario: When the sampling time interval is much shorter than the phase difference change period, the phase difference change rate is calculated using the differential method, and the formula used is as follows: in, for The rate of change of phase difference at time , for The true phase difference at any given moment for The measurement of the ambiguity phase difference at time, The sampling time interval; The second scenario: When the sampling time interval is greater than the phase difference change period, the phase difference change rate is calculated using the differential method, and the formula used is as follows: The phase difference and the rate of change of the phase difference are used as state variables, and the phase difference is used as the observation variable. Based on the state variables and observation variables, state equations and observation equations are established respectively, and Kalman filter equations are established. The estimated value of the phase difference change rate is obtained through the Kalman filter equation; Based on the estimated phase difference change rate, a coarse estimate of the target radiation source location is obtained using an improved genetic-particle swarm optimization algorithm; including: The observation area of ​​the observation platform is selected based on the prior fuzzy distance; Based on the observation area of ​​the observation platform, chaotic sequences are used to initialize the position and velocity of particles; The particle fitness value is constructed based on the estimated value of the phase difference change rate, and the individual optimal position and the population optimal position are updated based on the particle fitness value; Based on the optimal position explored by the entire particle swarm from beginning to end, a corresponding chaotic sequence is generated; The particle in the optimal position in the chaotic sequence is used to replace the position of one particle in the current particle swarm. Repeat the iteration until the stopping condition is met, that is, the maximum number of generations or the specified fitness value is reached; Using the coarse estimate of the target radiation source location as prior information, the nonlinear least squares method is used for iterative calculation to obtain the fine estimate of the target radiation source location.

2. The method according to claim 1, characterized in that, The particle swarm optimization algorithm employs an embedded hybrid approach, incorporating crossover and mutation operations from genetic algorithms into the particle swarm algorithm. This increases the diversity of the particle population and expands the algorithm's search range.

3. The method according to claim 1, characterized in that, The process of using a coarse estimate of the target radiation source's location as prior information and iteratively calculating using a nonlinear least squares method to obtain a fine estimate of the target radiation source's location includes: The average of the optimal positions of the population after the improved genetic-particle swarm optimization algorithm at each sampling time is selected as the initial value for locating the target radiation source; Through iterative recursion using the nonlinear least squares method, we can obtain... The location of the target at any given time.

4. The method according to claim 1, characterized in that, The method further includes: Monte Carlo simulation tests were conducted, and the process of accurately estimating the location of the target radiation source was repeated several times. The final location result was obtained by averaging the results of each accurate estimation of the target radiation source location.

5. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the single-station passive localization method based on the improved genetic-particle swarm optimization algorithm as described in any one of claims 1 to 4.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the single-station passive localization method based on the improved genetic-particle swarm optimization algorithm as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the single-station passive localization method based on the improved genetic-particle swarm optimization algorithm according to any one of claims 1 to 4 by executing the executable instructions.

Images