Mutual fuzzy function fast determination method, device and equipment for multi-objective optimization

Abstract

This invention provides a method, apparatus, and device for rapidly determining mutually fuzzy functions for multi-target optimization. The method for rapidly determining mutually fuzzy functions for multi-target optimization, applied to target determination in external radiation source radar, includes: firstly, using a particle swarm optimization algorithm to determine particles with high fitness, and then using these to obtain target particles; then, using the target particles as the optimization direction of a gray wolf optimization algorithm, avoiding the computational resource consumption caused by searching and traversing each particle one by one, and quickly and accurately obtaining fuzzy results; finally, using the fuzzy results as the final target for external radiation source radar search; that is, by combining particle swarm optimization and gray wolf optimization algorithms, the problem of existing particle swarm optimization algorithms easily getting trapped in the global optimum is avoided, improving the real-time performance and accuracy of multi-target optimization in external radiation source radar.

Description

Technical Field

[0001] This invention relates to the field of radar data processing technology, and specifically to a method, apparatus, and device for rapidly determining mutually fuzzy functions for multi-target optimization. Background Technology

[0002] In external radiation source radar systems, the echo signal energy of distant targets is extremely weak and is usually submerged in noise. Coherent accumulation is needed to increase the accumulation gain of the target echo signal, thereby enabling subsequent functions such as target detection, localization, and tracking. The mutual ambiguity function is a typical coherent accumulation method with wide applications in external radiation source radar systems. For example, in the radar field, calculating the mutual ambiguity function between the reference signal and the monitoring signal is an important way to improve the signal-to-noise ratio. High accumulation gain can be obtained through long-term mutual ambiguity calculations of sampled data. However, the computational complexity of the mutual ambiguity function increases exponentially with the accumulation time. Directly using a two-dimensional traversal search to calculate the mutual ambiguity function results in a huge computational burden, making it difficult to meet the real-time processing requirements of radar in practical applications.

[0003] Existing methods for calculating mutually fuzzy functions include cross-correlation FFT, variable frequency search step size method, and particle swarm optimization. Cross-correlation FFT still employs an ergonomic search approach, resulting in high computational complexity and generating a large amount of redundant results, severely wasting computational resources. While the variable frequency search step size method considers using a non-ergonomic search approach to improve computational efficiency, its fixed search strategy still produces considerable redundant data. Particle swarm optimization has become a new research hotspot due to its use of the characteristics of fuzzy functions to design intelligent search strategies. However, because Particle swarm optimization relies on particle position update mechanisms to explore the solution space, improper update rules can cause particles to converge prematurely to local optima, preventing them from continuing to search for the global optimum.

[0004] Therefore, existing target determination methods for external radiation source radar systems suffer from low computational efficiency and the problem of getting trapped in local optima. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a method, apparatus, and device for rapidly determining mutually fuzzy functions for multi-objective optimization.

[0006] The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] In a first aspect, the present invention provides a method for rapidly determining a mutually fuzzy function for multi-target optimization, applied to target determination in external radiation source radar, including:

[0008] S101. Initialize the historical particle set and the optimal particle set;

[0009] S102. Obtain the search position and the size of the search range, and obtain the empty set of Doppler frequencies based on the search position and the size of the search range; the empty set of Doppler frequencies is a set containing multiple frequency intervals;

[0010] S103. Multiple targets detected by the external radiation source radar are treated as particles and stored in the Doppler frequency empty set to obtain the Doppler frequency set.

[0011] S104. Based on the Doppler frequency set and the first preset iteration stopping condition, the historical particle set and the optimal particle set are updated using the particle swarm optimization algorithm to obtain the first historical particle set, the first optimal particle set, and the target particle; the first historical particle set consists of particles in the Doppler frequency set that have been searched using the particle swarm optimization algorithm; the first optimal particle set consists of the optimal fitness value of all subpopulations contained in the first historical particle set; the target particle is the top 3 particles in the fitness ranking of the first historical particle set.

[0012] S105. Based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, the position of the particles in the Doppler frequency set is updated using the Grey Wolf optimization algorithm to obtain the update result. The fuzzy result is obtained based on the update result; the fuzzy result is the final target searched by the external radiation source radar.

[0013] Optionally, the search range includes: a search time range and a search frequency range; the search location and the size of the search range are obtained; and based on the search location and the size of the search range, an empty set of Doppler frequencies is obtained, including:

[0014] Obtain the location to be searched, the time range to be searched, the frequency range to be searched, and the preset division interval;

[0015] Within the search location, the search frequency range is divided into multiple frequency intervals according to a preset interval, resulting in multiple Doppler frequency intervals; each Doppler frequency interval is treated as a small population.

[0016] Multiple Doppler frequency ranges are used to form an empty set of Doppler frequencies.

[0017] Optionally, based on the Doppler frequency set and the first preset iteration stopping condition, the particle swarm optimization algorithm is used to update the historical particle set and the optimal particle set to obtain the first historical particle set, the first optimal particle set, and the target particle, including:

[0018] S201. Within the search time range, based on the mutual fuzzy function algorithm and the current position of the particle, calculate the fitness value of the particle in the Doppler frequency set that has no intersection with the historical particle set, and obtain the fitness of the first particle.

[0019] S202. Determine the individual optimal value of each particle, the global optimal value of the small population, and the global optimal position of the small population based on the fitness of the first particle.

[0020] S203. Based on the global optimum value and the global optimum position of the small population, update the positions of all particles to obtain the updated particle positions.

[0021] S204. Use the particle's updated position as the particle's current position, and repeat S201-S203 until the first preset iteration stop condition is met;

[0022] S205. Add all the particles processed before reaching the first preset iteration stopping condition to the historical particle set to obtain the first historical particle set; add the global optimal values ​​of all small populations to the optimal particle set to obtain the first optimal particle set; and select the top 3 particles in fitness ranking from the first optimal particle set as the target particles.

[0023] Optionally, the first preset iteration stopping condition includes: the number of iterations reaches the first preset iteration threshold or the global optimum of the small population reaches the preset target screening threshold.

[0024] Optionally, based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, the position of the particles in the Doppler frequency set is updated using the Grey Wolf optimization algorithm to obtain the update result. The fuzzy result is then obtained based on the update result. This fuzzy result is used as the final target for the external radiation source radar search, including:

[0025] S301. Obtain the target distance between the current positions of the target particle and the remaining particles; the remaining particles are all particles in the Doppler frequency set except for the target particle.

[0026] S302. Based on the position of the target particle and the distance to the target, update the position of the remaining particles to obtain the updated position of the remaining particles;

[0027] S303. Based on the updated positions of the remaining particles and the first optimal particle set, obtain the fitness of the remaining particles and get the first result;

[0028] S304. Based on the first result, obtain the global optimal value and the global optimal position;

[0029] S305. Use the updated position of the remaining particles in S302 as the current position of the remaining particles in S301, and repeat S301-S304 until the second preset iteration stop condition is met. Use the global optimal value corresponding to the most recent execution of S304 as the fuzzy result.

[0030] S305. Use the ambiguous results as the final target for radar search of external radiation sources.

[0031] Optionally, based on the position of the target particle and its distance from the target, the positions of the remaining particles are updated to obtain the updated positions of the remaining particles, including:

[0032] Based on the target particle's position and distance, the remaining particle positions are updated using the following formula to obtain the updated positions of the remaining particles:

[0033] x ij (t+1)=x ij (t)+v ij (t+1);

[0034]

[0035] x ij (t+1) represents the updated position of the remaining particles in the (t+1)th iteration, x ij (t) represents the position of the remaining particles in the t-th iteration, v ij (t+1) represents the update rate of the remaining particles in the (t+1)th iteration. Let X(t) represent the shrinkage factor, X(t) represent the position of the remaining particle in the Gray Wolf algorithm, ω represent the weight coefficient, r1(t) represent the first random number corresponding to the t-th iteration, r2(t) represent the second random number corresponding to the t-th iteration, c1 represent the evolutionary attribute learning factor, c2 represent the social attribute learning factor, and p represent the shrinkage factor. gj (t) represents the global optimal position of the small population, p ij (t) represents the individual optimal value for each particle.

[0036] Optionally, the second preset iteration stopping condition includes: the number of iterations reaches the second preset iteration threshold or the global optimal value reaches the preset target screening threshold.

[0037] Optionally, based on the first result, the global optimal value and the global optimal position are obtained, including:

[0038] The global optimum value of all small populations included in the first result is taken as the global optimum value, and the position corresponding to the global optimum value is taken as the global optimum position.

[0039] In a second aspect, the present invention provides a device for rapidly determining a mutually fuzzy function for multi-objective optimization. The device for rapidly determining a mutually fuzzy function for multi-objective optimization includes: an initialization unit, an acquisition unit, a particle storage and retrieval unit, a first iteration unit, and a second iteration unit.

[0040] The initialization unit is used to: initialize the historical particle set and the optimal particle set;

[0041] The acquisition unit is used to: acquire the search position and the size of the search range, and obtain the empty set of Doppler frequencies based on the search position and the size of the search range; the empty set of Doppler frequencies is a set containing multiple frequency intervals;

[0042] The particle storage unit is used to: store multiple targets detected by the external radiation source radar as particles into the Doppler frequency empty set to obtain the Doppler frequency set.

[0043] The first iteration unit is used to: update the historical particle set and the optimal particle set based on the Doppler frequency set and the first preset iteration stopping condition using the particle swarm optimization algorithm to obtain the first historical particle set, the first optimal particle set, and the target particle; the first historical particle set consists of particles in the Doppler frequency set that have been searched using the particle swarm optimization algorithm; the first optimal particle set consists of the optimal fitness value of all subpopulations contained in the first historical particle set; the target particle is the top 3 particles in the fitness ranking of the first historical particle set.

[0044] The second iteration unit is used to: update the positions of particles in the Doppler frequency set using the Grey Wolf optimization algorithm based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, to obtain the update result, and to obtain the fuzzy result based on the update result; the fuzzy result is the final target searched by the external radiation source radar.

[0045] Thirdly, the present invention provides a device for rapidly determining a mutually fuzzy function for multi-objective optimization, comprising: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the device for rapidly determining a mutually fuzzy function for multi-objective optimization is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for rapidly determining a mutually fuzzy function for multi-objective optimization as described in the first aspect above.

[0046] This invention provides a method, apparatus, and device for rapidly determining mutually fuzzy functions for multi-target optimization. The method for rapidly determining mutually fuzzy functions for multi-target optimization, applied to target determination using an external radiation source radar, includes: S101, initializing a historical particle set and an optimal particle set; S102, obtaining the search position and search range size, and obtaining a Doppler frequency empty set based on the search position and search range size; the Doppler frequency empty set is a set containing multiple frequency intervals; S103, storing multiple targets searched by the external radiation source radar as particles into the Doppler frequency empty set, obtaining a Doppler frequency set; S104, based on the Doppler frequency set and a first preset iteration stopping condition, using a particle swarm optimization algorithm to process the historical particle set and the optimal particle set... The algorithm updates the first historical particle set, the first optimal particle set, and the target particle. The first historical particle set consists of particles searched using the particle swarm optimization algorithm within the Doppler frequency set. The first optimal particle set is the fitness value of all subpopulations included in the first historical particle set. The target particle is the top 3 particles in the first historical particle set in terms of fitness. In step S105, based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, the position of the particles in the Doppler frequency set is updated using the gray wolf optimization algorithm to obtain the update result. The fuzzy result is obtained based on the update result. The fuzzy result is the final target searched by the external radiation source radar. In this invention, a particle swarm optimization algorithm is first used to determine particles with high fitness, and these particles are then used to obtain target particles. The target particles are then used as the optimization direction for the gray wolf optimization algorithm, avoiding the computational resource consumption caused by searching and traversing each particle one by one, and allowing for fast and accurate fuzzy results. Finally, the fuzzy results are used as the final target for external radiation source radar search. In other words, by combining the particle swarm optimization algorithm with the gray wolf optimization algorithm, the problem of existing particle swarm optimization algorithms easily getting trapped in the global optimum is avoided, improving the real-time performance and accuracy of multi-target optimization for external radiation source radar.

[0047] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a method for rapidly determining a mutually fuzzy function for multi-objective optimization, provided in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the accumulation result of the cross-correlation FFT method provided in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the accumulation result of the mutually fuzzy function obtained by the variable frequency search step size method provided in an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of the accumulation result of the mutually fuzzy function obtained by the particle swarm optimization method in an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the accumulation result of the mutually fuzzy function obtained by the method of the present invention is provided in an embodiment of the present invention;

[0053] Figure 6 This is a schematic diagram of a device for rapidly determining a mutually fuzzy function for multi-objective optimization, provided in an embodiment of the present invention.

[0054] Figure 7 This is a schematic diagram of a device for rapidly determining a mutually fuzzy function for multi-objective optimization, provided in an embodiment of the present invention. Detailed Implementation

[0055] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0056] To avoid the problem of existing particle swarm optimization algorithms easily getting trapped in the global optimum and to improve the real-time performance and accuracy of multi-target optimization for external radiation source radar, this invention provides a method for rapidly determining a mutually fuzzy function for multi-target optimization. Figure 1 This is a flowchart illustrating a method for rapidly determining mutually fuzzy functions for multi-objective optimization, provided by an embodiment of the present invention. Figure 1 As shown, it includes:

[0057] S101. Initialize the historical particle set and the optimal particle set.

[0058] S102. Obtain the search position and the size of the search range, and obtain the empty set of Doppler frequencies based on the search position and the size of the search range.

[0059] The Doppler frequency empty set is a set containing multiple frequency ranges.

[0060] It should be noted that, in the embodiments of the present invention, the search range includes: the search time range and the search frequency range.

[0061] S102 may specifically include:

[0062] Obtain the location to be searched, the time range to be searched, the frequency range to be searched, and the preset division interval;

[0063] Within the search location, the search frequency range is divided into multiple frequency intervals according to a preset interval, resulting in multiple Doppler frequency intervals; each Doppler frequency interval is treated as a small population.

[0064] Multiple Doppler frequency ranges are used to form an empty set of Doppler frequencies.

[0065] In this embodiment of the invention, the time range to be searched is represented as [τ]. min ,τ max The search frequency range is represented as [f] min ,f max The search frequency range is divided into multiple Doppler frequency intervals at preset intervals, with each Doppler frequency interval forming a small population; the historical particle set is initialized. Optimal Particle Set

[0066] S103. Multiple targets detected by the external radiation source radar are treated as particles and stored in the Doppler frequency empty set to obtain the Doppler frequency set.

[0067] In another possible implementation, in order to reduce the amount of computation, targets that meet the preset fitness screening threshold among the multiple targets searched by the external radiation source radar can be stored as particles in the Doppler frequency empty set to obtain the Doppler frequency set.

[0068] S104. Based on the Doppler frequency set and the first preset iteration stopping condition, the historical particle set and the optimal particle set are updated using the particle swarm optimization algorithm to obtain the first historical particle set, the first optimal particle set, and the target particle.

[0069] The first historical particle set consists of particles searched using the particle swarm optimization algorithm within the Doppler frequency set; the first optimal particle set consists of the optimal fitness value of all subpopulations contained in the first historical particle set; the target particle is the top 3 particles in the first historical particle set in terms of fitness.

[0070] It should be noted that, in this embodiment of the invention, the fitness of the first historical particle set is sorted in descending order, and the top 3 particles with the highest fitness values ​​are taken as target particles.

[0071] Optionally, S104 may specifically include:

[0072] S201. Within the search time range, based on the mutual fuzzy function algorithm and the current position of the particle, calculate the fitness value of the particle in the Doppler frequency set that has no intersection with the historical particle set, and obtain the fitness of the first particle.

[0073] S202. Determine the individual optimal value of each particle, the global optimal value of the small population, and the global optimal position of the small population based on the fitness of the first particle.

[0074] S203. Based on the global optimum value and the global optimum position of the small population, update the positions of all particles to obtain the updated particle positions.

[0075] S204. Use the particle's updated position as the particle's current position, and repeat S201-S203 until the first preset iteration stop condition is met;

[0076] S205. Add all the particles processed before reaching the first preset iteration stopping condition to the historical particle set to obtain the first historical particle set; add the global optimal values ​​of all small populations to the optimal particle set to obtain the first optimal particle set; and select the top 3 particles in fitness ranking from the first optimal particle set as the target particles.

[0077] In this embodiment of the invention, the fitness values ​​of particles that have no intersection with the historical particle set are calculated, the fitness values ​​of particles that have an intersection with the optimal particle set are set to 0, and the fitness values ​​of the remaining particles are directly obtained from the historical particle set to update the individual optimal value p of each particle. ij The global optimum (gbest) for each subpopulation h (h=1,2,...,H) and the global optimal position p of each subpopulation gj , where h represents the number of subpopulations.

[0078] It should be noted that since both the historical particle set and the optimal particle set are empty sets in the steps, it is necessary to calculate the fitness value of all particles.

[0079] Specifically, the velocity and position of each particle are updated according to formulas (1) and (2).

[0080]

[0081] Where ω represents the weighting coefficient, v ij '(t) represents the velocity of the particle in the t-th iteration of the particle swarm optimization algorithm, v ij '(t+1) represents the particle velocity at the (t+1)th iteration of the particle swarm optimization algorithm, x ij '(t) is the time-frequency position of the particle, p ij '(t) represents the individual optimal value of each particle in the particle swarm optimization algorithm, p gj r1'(t) represents the global optimal position of the small population, r1'(t) and r2'(t) are random numbers between [0,1], and c1 and c2 are learning factors, where c1 has its own evolutionary properties and c2 has social properties. The contraction factor is used to control the search capability of the particle swarm optimization algorithm; selecting an appropriate factor is crucial. It can increase the convergence speed of the particle swarm optimization algorithm and enhance the algorithm's search capability.

[0082] x ij '(t+1)=x ij '(t)+v ij '(t+1) (2)

[0083] Where, x ij '(t+1) represents the particle update position in the (t+1)th iteration of the particle swarm optimization algorithm, x ij '(t) represents the particle position at the t-th iteration in the particle swarm optimization algorithm.

[0084] Optionally, the first preset iteration stopping condition includes: the number of iterations reaches the first preset iteration threshold or the global optimum of the small population reaches the preset target screening threshold.

[0085] S105. Based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, the position of the particles in the Doppler frequency set is updated using the Grey Wolf optimization algorithm to obtain the update result. The fuzzy result is obtained based on the update result; the fuzzy result is the final target searched by the external radiation source radar.

[0086] This invention provides a method for rapidly determining a mutually fuzzy function for multi-target optimization, comprising: firstly, determining particles with high fitness using a particle swarm optimization algorithm, and then obtaining target particles based on these particles; then, using the target particles as the optimization direction of a gray wolf optimization algorithm, avoiding the computational resource consumption caused by searching and traversing each particle one by one, and obtaining fuzzy results quickly and accurately; finally, using the fuzzy results as the final target for external radiation source radar search; that is, by combining the particle swarm optimization algorithm and the gray wolf optimization algorithm, the problem that existing particle swarm optimization algorithms are prone to getting trapped in the global optimum is avoided, improving the real-time performance and accuracy of multi-target optimization for external radiation source radar.

[0087] Optionally, S105 may specifically include:

[0088] S301. Obtain the target distance between the current positions of the target particle and the remaining particles.

[0089] The remaining particles are all particles in the Doppler frequency set except for the target particle.

[0090] S302. Based on the position of the target particle and the distance to the target, update the position of the remaining particles to obtain the updated position of the remaining particles.

[0091] S303. Based on the updated positions of the remaining particles and the first optimal particle set, obtain the fitness of the remaining particles and get the first result.

[0092] S304. Based on the first result, obtain the global optimal value and the global optimal position.

[0093] S305. Use the updated position of the remaining particles in S302 as the current position of the remaining particles in S301, and repeat S301-S304 until the second preset iteration stop condition is met. Use the global optimal value corresponding to the most recent execution of S304 as the fuzzy result.

[0094] S305. Use the ambiguous results as the final target for radar search of external radiation sources.

[0095] It should be noted that, in this embodiment of the invention, in order to reduce computation, the fitness values ​​of particles that do not intersect with the first particle set can be calculated directly, and the fitness values ​​of the remaining particles can be obtained directly from the first particle set.

[0096] In addition, in this embodiment of the invention, all particles are treated as a gray wolf population, and the three particles with the best fitness are treated as the three wolves α, β and δ of the gray wolf population, where α represents the first wolf, β represents the second wolf and δ represents the third wolf.

[0097] The position of the target particle under the gray wolf strategy is updated according to equations (3) and (4), and then the position and velocity of each remaining particle are updated according to equations (5) and (6).

[0098]

[0099] Among them, D α D β and D δ Let X represent the distances between α, β, and δ and the remaining particles, respectively; C1, C2, and C3 represent the first, second, and third random vectors, respectively; and X represent the distances between α, β, and δ and the remaining particles, respectively. α (t), X β (t) and X δ X(t) represents the positions of α, β and δ respectively, and X(t) represents the position of the remaining particle in the Grey Wolf algorithm.

[0100]

[0101] Where A1, A2, and A3 represent the first coefficient factor, the second coefficient factor, and the third coefficient factor, respectively, and X1(t), X2(t), and X3(t) represent the positions of the remaining particles after being updated based on the positions of the gray wolf α, β, and δ.

[0102] X(t)=[X1(t)+X2(t)+X3(t)] / 3 (5)

[0103] Optionally, S302 may specifically include:

[0104] Based on the target particle's position and distance, the remaining particle positions are updated using the following formula to obtain the updated positions of the remaining particles:

[0105] x ij (t+1)=x ij (t)+v ij (t+1);

[0106]

[0107] x ij (t+1) represents the updated position of the remaining particles in the (t+1)th iteration, x ij (t) represents the position of the remaining particles in the t-th iteration, v ij (t+1) represents the update rate of the remaining particles in the (t+1)th iteration. Let X(t) represent the shrinkage factor, X(t) represent the position of the remaining particle in the Gray Wolf algorithm, ω represent the weight coefficient, r1(t) represent the first random number corresponding to the t-th iteration, r2(t) represent the second random number corresponding to the t-th iteration, c1 represent the evolutionary attribute learning factor, c2 represent the social attribute learning factor, and p represent the shrinkage factor. gj (t) represents the global optimal position of the small population, p ij (t) represents the individual optimal value for each particle.

[0108] It should be noted that, in this embodiment of the invention, the remaining particle update position is obtained based on the position of the target particle and the target distance, mainly with the target particle as the optimization update direction, in order to make the final target optimization selection.

[0109] Optionally, the second preset iteration stopping condition includes: the number of iterations reaches the second preset iteration threshold or the global optimal value reaches the preset target screening threshold.

[0110] Optionally, based on the first result, the global optimal value and the global optimal position are obtained, including:

[0111] The global optimum value of all small populations included in the first result is taken as the global optimum value, and the position corresponding to the global optimum value is taken as the global optimum position.

[0112] To verify the effectiveness of the method for rapidly determining mutually fuzzy functions for multi-objective optimization provided in this embodiment of the invention, simulation experiments were also conducted.

[0113] The simulation experiment assumes that the maximum detection range and velocity range of the actual external radiation source radar are 90 km and 360 m / s, respectively. Therefore, the time delay range required to achieve target detection is [0, 300] μs, and the Doppler frequency range is [-4, 4] kHz. With an accumulation time T = 0.1 s, if a search is performed across the Doppler frequency range, the ambiguity function needs to be calculated at 800 frequency points. Furthermore, the simulation parameters for the moving target are shown in Table 1.

[0114] Table 1 Target Simulation Parameters

[0115] Parameter type Target A Target B Delay 30.4μs 22.2μs Doppler frequency 280Hz -290Hz Signal-to-noise ratio -15dB -12dB

[0116] The reference signal after the frame header is set to zero is correlated with the target echo signal. Figure 2 , Figure 3 , Figure 4 , Figure 5 The diagrams, in order, show the accumulation results of the cross-correlation FFT method, the variable frequency search step size method, the particle swarm optimization method, and the method of this invention. Figure 2 (a) diagram Figure 3 (a) diagram Figure 4 (a) diagram Figure 5 Figure (a) shows the result of the mutual fuzziness function accumulation for the corresponding method. Figure 2 (b) diagram Figure 3 (b) diagram Figure 4 (b) diagram Figure 5 Figure (b) shows the time-delay-Doppler plane accumulation results of the corresponding method, where each line represents all fuzzy function results within the time-delay search range at a certain frequency point, and the number of lines represents the number of frequency points calculated. Figure 2 (b) diagram Figure 3 (b) diagram Figure 4 (b) diagram Figure 5 The frequency points calculated for Figure (b) are 800, 410, 82, and 132, respectively.

[0117] Furthermore, by taking the intersection of the peak values ​​of the two cross-fuzzy function results as the searched target peak value, it can be seen that the cross-correlation FFT method, the variable frequency search step size method, and the method of the present invention can search for all targets. Moreover, the method of the present invention has the fewest search frequency points, and the computational cost is about 1 / 8 of that of the cross-correlation FFT method and 1 / 4 of that of the variable frequency search step size method. The particle swarm optimization method has the least computational cost, but it only searches for target A and produces a case of target missed detection, which verifies the effectiveness of the method of the present invention.

[0118] The method provided in this embodiment of the invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc., and this embodiment of the invention does not limit the application to such devices.

[0119] Based on the same inventive concept, embodiments of the present invention also provide a device for rapidly determining mutually fuzzy functions for multi-objective optimization. Figure 6 This is a schematic diagram of a device for rapidly determining mutually fuzzy functions for multi-objective optimization, provided in an embodiment of the present invention. Figure 6 As shown, it includes: an initialization unit 601, an acquisition unit 602, a particle storage and retrieval unit 603, a first iteration unit 604, and a second iteration unit 605;

[0120] Initialization unit 601 is used to: initialize the historical particle set and the optimal particle set;

[0121] The acquisition unit 602 is used to: acquire the search position and the size of the search range, and obtain the empty set of Doppler frequencies based on the search position and the size of the search range; the empty set of Doppler frequencies is a set containing multiple frequency intervals;

[0122] The particle storage unit 603 is used to: store multiple targets searched by the external radiation source radar as particles into the Doppler frequency empty set to obtain the Doppler frequency set.

[0123] The first iteration unit 604 is used to: update the historical particle set and the optimal particle set based on the Doppler frequency set and the first preset iteration stopping condition using the particle swarm optimization algorithm to obtain the first historical particle set, the first optimal particle set, and the target particle; the first historical particle set consists of particles in the Doppler frequency set that have been searched using the particle swarm optimization algorithm; the first optimal particle set consists of the optimal fitness value of all subpopulations contained in the first historical particle set; the target particle is the top 3 particles in the fitness ranking of the first historical particle set.

[0124] The second iteration unit 605 is used to: update the position of the particles in the Doppler frequency set using the Grey Wolf optimization algorithm based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, to obtain the update result, and to obtain the fuzzy result based on the update result; the fuzzy result is the final target searched by the external radiation source radar.

[0125] Figure 7 This invention provides a schematic diagram of a device for rapidly determining a mutually fuzzy function for multi-objective optimization, comprising: a processor 710, a storage medium 720, and a bus 730. The storage medium 720 stores machine-readable instructions executable by the processor 710. When the device for rapidly determining a mutually fuzzy function for multi-objective optimization is running, the processor 710 communicates with the storage medium 720 via the bus 730. The processor 710 executes the machine-readable instructions to perform the steps of the above-described method embodiment. Specific implementations and technical effects are similar and will not be repeated here.

[0126] The storage medium may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the storage medium may also be at least one storage device located remotely from the aforementioned processor.

[0127] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0128] It should be noted that the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention.

[0129] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0130] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0131] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the inventive concept, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for rapidly determining mutually fuzzy functions for multi-target optimization, applied to target determination in external radiation source radar, characterized in that, include: S101. Initialize the historical particle set and the optimal particle set; S102. Obtain the search location and the search range size, and obtain the Doppler frequency empty set based on the search location and the search range size; the Doppler frequency empty set is a set containing multiple frequency intervals; each Doppler frequency interval is a small population; S103. The multiple targets searched by the external radiation source radar are treated as particles and stored in the Doppler frequency empty set to obtain the Doppler frequency set. S104. Based on the Doppler frequency set and the first preset iteration stopping condition, the historical particle set and the optimal particle set are updated using the particle swarm optimization algorithm to obtain a first historical particle set, a first optimal particle set, and a target particle; the first historical particle set consists of particles in the Doppler frequency set that have been searched using the particle swarm optimization algorithm; the first optimal particle set consists of the optimal fitness value of all subpopulations contained in the first historical particle set; the target particle is the top 3 particles in the first historical particle set in terms of fitness. S105. Based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, the position of the particles in the Doppler frequency set is updated using the gray wolf optimization algorithm to obtain the update result. The target particle is used as the optimization direction of the gray wolf optimization algorithm, all particles are simultaneously considered as a gray wolf population, and the target particle is considered as the three-headed wolf of the gray wolf population. , and , Indicates the first wolf, Indicates the second wolf, The third wolf is represented, and a fuzzy result is obtained based on the updated result; the fuzzy result is the final target searched by the external radiation source radar.

2. The method for rapid determination of mutually fuzzy functions for multi-objective optimization according to claim 1, characterized in that, The search range includes a search time range and a search frequency range. The process of obtaining the search position and the size of the search range, and obtaining the empty set of Doppler frequencies based on the search position and the size of the search range, includes: Obtain the location to be searched, the time range to be searched, the frequency range to be searched, and the preset division interval; Within the location to be searched, the frequency range to be searched is divided into multiple frequency intervals according to the preset division interval, resulting in multiple Doppler frequency intervals; The multiple Doppler frequency ranges are used to form the empty set of Doppler frequencies.

3. The method for rapid determination of mutually fuzzy functions for multi-objective optimization according to claim 2, characterized in that, The step of updating the historical particle set and the optimal particle set using a particle swarm optimization algorithm based on the Doppler frequency set and a first preset iteration stopping condition to obtain a first historical particle set, a first optimal particle set, and a target particle includes: S201. Within the search time range, based on the mutual fuzzy function algorithm and the current position of the particle, calculate the fitness value of the particle in the Doppler frequency set that has no intersection with the historical particle set, and obtain the fitness of the first particle. S202. Determine the individual optimal value of each particle, the global optimal value of the small population, and the global optimal position of the small population based on the fitness of the first particle. S203. Based on the global optimal value and the global optimal position of the small population, update the positions of all particles to obtain the updated particle positions. S204. Take the updated position of the particle as the current position of the particle, and repeat S201-S203 until the first preset iteration stop condition is reached; S205. Add all particles processed before reaching the first preset iteration stop condition to the historical particle set to obtain the first historical particle set; add the global optimal values ​​of all the small populations to the optimal particle set to obtain the first optimal particle set; and make the top 3 particles in fitness ranking in the first optimal particle set the target particles.

4. The method for rapid determination of mutually fuzzy functions for multi-objective optimization according to claim 1 or 3, characterized in that, The first preset iteration stopping condition includes: the number of iterations reaches a first preset iteration threshold or the global optimal value of the small population reaches a preset target screening threshold.

5. The method for rapid determination of mutually fuzzy functions for multi-objective optimization according to claim 1, characterized in that, Based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, the position update process of the particles in the Doppler frequency set is performed using the Grey Wolf optimization algorithm to obtain the update result, and the fuzzy result is obtained based on the update result. Using the fuzzy result as the final target of the external radiation source radar search includes: S301. Obtain the target distance between the current positions of the target particle and the remaining particles; The remaining particles are all particles in the Doppler frequency set except for the target particle; S302. Update the positions of the remaining particles based on the position of the target particle and the distance to the target, to obtain the updated positions of the remaining particles; S303. Based on the updated positions of the remaining particles and the first optimal particle set, obtain the fitness of the remaining particles to get a first result; S304. Based on the first result, obtain the global optimal value and the global optimal position; S305. The updated position of the remaining particles in S302 is used as the current position of the remaining particles in S301. S301-S304 are repeated until the second preset iteration stop condition is met. The global optimal value corresponding to the most recent execution of S304 is used as the fuzzy result. S305. Use the ambiguous results as the final target for radar search of external radiation sources.

6. The method for rapid determination of mutually fuzzy functions for multi-objective optimization according to claim 5, characterized in that, The step of updating the positions of the remaining particles based on the position of the target particle and the distance to the target, to obtain the updated positions of the remaining particles, includes: Based on the position of the target particle and the distance to the target, the positions of the remaining particles are updated using the following formula to obtain the updated positions of the remaining particles: ； ； Indicates the first In the next iteration, the positions of the remaining particles are updated. Indicates the first The remaining particle positions at the next iteration Indicates the first The remaining particle update rate in the next iteration Indicates the contractility factor. This indicates the position of the remaining particles in the Grey Wolf algorithm. Indicates the weighting coefficient. Indicates the first The first random number corresponding to the next iteration. Indicates the first The second random number corresponding to the next iteration. Indicates the learning factor of evolutionary attributes. Represents social attribute learning factors. This represents the globally optimal position of a small population. This represents the individual optimal value for each particle.

7. The method for rapid determination of mutually fuzzy functions for multi-objective optimization according to claim 1, characterized in that, The second preset iteration stop condition includes: the number of iterations reaches the second preset iteration threshold or the global optimal value reaches the preset target screening threshold.

8. The method for rapid determination of mutually fuzzy functions for multi-objective optimization according to claim 5, characterized in that, The step of obtaining the global optimal value and the global optimal position based on the first result includes: The global optimal value of all small populations included in the first result is taken as the global optimal value, and the position corresponding to the global optimal value is taken as the global optimal position.

9. A device for rapidly determining mutually fuzzy functions for multi-objective optimization, characterized in that, The device for rapidly determining the mutually fuzzy function for multi-objective optimization includes: an initialization unit, an acquisition unit, a particle access unit, a first iteration unit, and a second iteration unit; The initialization unit is used to: initialize the historical particle set and the optimal particle set; The acquisition unit is used to: acquire the search location and the size of the search range, and obtain a Doppler frequency empty set based on the search location and the size of the search range; the Doppler frequency empty set is a set containing multiple frequency intervals; each Doppler frequency interval is a small population; The particle storage unit is used to: store multiple targets searched by the external radiation source radar as particles into the Doppler frequency empty set to obtain the Doppler frequency set. The first iteration unit is used to: update the historical particle set and the optimal particle set using a particle swarm optimization algorithm based on the Doppler frequency set and a first preset iteration stopping condition to obtain a first historical particle set, a first optimal particle set, and a target particle; the first historical particle set consists of particles in the Doppler frequency set that have been searched using the particle swarm optimization algorithm; the first optimal particle set consists of the optimal fitness value of all subpopulations included in the first historical particle set; the target particle is the top 3 particles in the first historical particle set in terms of fitness. The second iteration unit is used to: update the positions of particles in the Doppler frequency set using the gray wolf optimization algorithm based on the target particle, the first historical particle set, the first optimal particle set, and the second preset iteration stopping condition, to obtain an update result. The target particle is used as the optimization direction of the gray wolf optimization algorithm, all particles are simultaneously considered as a gray wolf population, and the target particle is considered as the three-headed wolf of the gray wolf population. , and , Indicates the first wolf, Indicates the second wolf, The third wolf is represented, and a fuzzy result is obtained based on the updated result; the fuzzy result is the final target searched by the external radiation source radar.

10. A device for rapidly determining mutually fuzzy functions for multi-objective optimization, characterized in that, include: The device includes a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the device for rapid determination of a mutually fuzzy function for multi-objective optimization is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for rapid determination of a mutually fuzzy function for multi-objective optimization as described in any one of claims 1-8.

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