Method, device and equipment for quickly determining mutual fuzzy function for multi-target optimization

Abstract

The invention provides a method, a device and equipment for quickly determining a cross-ambiguity function for multi-target optimization. The cross-ambiguity function rapid determination method for multi-target optimization is applied to target determination of an external radiation source radar, and comprises the following steps: firstly, determining particles with relatively high fitness by adopting a particle swarm optimization algorithm, and thus obtaining target particles; then, the target particle is used as the optimization direction of the grey wolf optimization algorithm, so that calculation resource consumption caused by searching and traversing the particles one by one is avoided, and a fuzzy result can be quickly and accurately obtained; and finally, taking a fuzzy result as a final target searched by the external radiation source radar. Through the combination of the particle swarm optimization algorithm and the grey wolf optimization algorithm, the problem that the existing particle swarm optimization algorithm is easy to fall into a global optimal solution is avoided, and the real-time performance and the accuracy of multi-target optimization of the external radiation source radar are improved.

Description

Technical Field

[0001] The present invention relates to the field of radar data processing technology, and in particular to a method, a device and equipment for quickly determining mutual fuzzy functions for multi-target optimization. Background Art

[0002] In an exo-radiation radar system, the energy of the echo signal of a target at a long distance is extremely weak and is usually submerged in the noise signal. It is necessary to improve the accumulation gain of the target echo signal through coherent accumulation, so as to achieve subsequent functions such as target detection, positioning and tracking. The mutual ambiguity function is a typical coherent accumulation method and is widely used in exo-radiation radar systems. For example, in the field of radar, calculating the mutual ambiguity function between the reference signal and the monitoring signal is an important way to improve the signal-to-noise ratio. A higher accumulation gain can be obtained by performing long-term mutual ambiguity calculations on the sampled data. However, as the accumulation time increases, the amount of calculation of the mutual ambiguity function increases exponentially. If a two-dimensional traversal search method is directly adopted to complete the calculation of the mutual ambiguity function, the amount of calculation is huge, so it is difficult to meet the real-time processing requirements of the radar in practical applications.

[0003] Existing methods for calculating mutual fuzzy functions include: cross-correlation FFT method, variable frequency search step method and particle swarm optimization method. Among them, the cross-correlation FFT method still uses a traversal search method, which has high computational complexity and will produce a large number of redundant results, seriously wasting computing resources; although the variable frequency search step method considers using a non-traversal search method to improve computational efficiency, the search strategy is fixed and still generates a lot of redundant data; the particle swarm optimization method has become a new research hotspot because it uses the characteristics of fuzzy functions to design intelligent search strategies, but because the particle swarm optimization method relies on the particle position update mechanism to explore the solution space, if the update rules are not set properly, the particles may converge to the local optimal solution too early and cannot continue to find the global optimal solution.

[0004] Therefore, the target determination method of the existing external radiation source radar system has the problems of low computational efficiency and falling into the local optimal solution. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method, device and equipment for quickly determining mutual fuzzy functions for multi-objective optimization.

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

[0007] In a first aspect, the present invention provides a method for quickly determining a mutual fuzzy function for multi-target optimization, which is applied to target determination of an external radiation source radar, comprising:

[0008] S101, initializing the historical particle set and the optimal particle set;

[0009] S102, obtaining a position to be searched and a size of a range to be searched, and obtaining an empty Doppler frequency set according to the position to be searched and the size of the range to be searched; the empty Doppler frequency set is a set including multiple frequency intervals;

[0010] S103, storing the multiple targets searched by the external radiation source radar as particles in a Doppler frequency empty set to obtain a Doppler frequency set;

[0011] S104, based on the Doppler frequency set and the first preset iteration stop 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 is the particles in the Doppler frequency set that have been searched based on the particle swarm optimization algorithm; the first optimal particle set is the optimal fitness value of all small populations 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;

[0012] S105. Based on the target particles, the first historical particle set, the first optimal particle set and the second preset iteration stop condition, the particles in the Doppler frequency set are updated using the Grey Wolf Optimization Algorithm to obtain an updated result, and a fuzzy result is obtained based on the updated result; the fuzzy result is the final target of the external radiation source radar search.

[0013] Optionally, the range to be searched includes: a time range to be searched and a frequency range to be searched, obtaining a position to be searched and a size of the range to be searched, and obtaining an empty set of Doppler frequencies according to the position to be searched and the size of the range to be searched, including:

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

[0015] The frequency range to be searched is divided into a plurality of frequency intervals according to a preset division interval within the position to be searched, so as to obtain a plurality of Doppler frequency intervals; wherein each Doppler frequency interval is regarded as a small population;

[0016] A plurality of Doppler frequency intervals constitute a Doppler frequency empty set.

[0017] Optionally, based on the Doppler frequency set and the first preset iteration stop condition, the historical particle set and the optimal particle set are updated using a particle swarm optimization algorithm to obtain a first historical particle set, a first optimal particle set and a target particle, including:

[0018] S201, within the time range to be searched, 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 first particle fitness;

[0019] S202, determining the individual optimal value of each particle, the global optimal value of the small population, and the global optimal position of the small population according to the fitness of the first particle;

[0020] S203, updating the positions of all particles according to the global optimal value and the global optimal position of the small population to obtain updated particle positions;

[0021] S204, taking the particle update position as the particle current position, and repeatedly executing S201-S203 until a first preset iteration stop condition is reached;

[0022] S205, adding all particles processed before reaching the first preset iteration stop condition to the historical particle set to obtain a first historical particle set; adding the global optimal values ​​of all small populations to the optimal particle set to obtain a first optimal particle set; and forming the target particles from the first optimal particle set with the top three particles in fitness ranking.

[0023] Optionally, the first preset iteration stop 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.

[0024] Optionally, based on the target particles, the first historical particle set, the first optimal particle set and the second preset iteration stop condition, the particles in the Doppler frequency set are processed for position update using the Grey Wolf Optimization Algorithm to obtain an update result, and a fuzzy result is obtained according to the update result; and the fuzzy result is used as the final goal of the external radiation source radar search, including:

[0025] S301, obtaining a target distance between a target particle and a current position of remaining particles; the remaining particles are all particles in a Doppler frequency set except the target particle;

[0026] S302, updating the positions of the remaining particles according to the position of the target particle and the target distance, to obtain the updated positions of the remaining particles;

[0027] S303, obtaining the fitness of the remaining particles according to the updated positions of the remaining particles and the first optimal particle set, and obtaining a first result;

[0028] S304, obtaining a global optimal value and a global optimal position according to the first result;

[0029] S305, taking the updated positions of the remaining particles in S302 as the current positions of the remaining particles in S301, repeatedly executing S301-S304 until a second preset iteration stop condition is satisfied, and taking the global optimal value corresponding to the most recent execution of S304 as the fuzzy result;

[0030] S305. Use the fuzzy result as the final target of the external radiation source radar search.

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

[0032] Based on the position and distance of the target particle, the positions of the remaining particles 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) indicates the updated position of the remaining particles at the t+1th iteration, x ij (t) represents the position of the remaining particles at the tth iteration, v ij (t+1) represents the update speed of the remaining particles at the t+1th iteration, represents the contraction factor, X(t) represents the position of the remaining particles in the gray wolf algorithm, ω represents the weight coefficient, r1(t) represents the first random number corresponding to the tth iteration, r2(t) represents the second random number corresponding to the tth iteration, c1 represents the evolutionary attribute learning factor, c2 represents the social attribute learning factor, p gj (t) represents the global optimal position of the small population, p ij (t) represents the individual optimal value of each particle.

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

[0037] Optionally, obtaining a global optimal value and a global optimal position according to the first result includes:

[0038] 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.

[0039] In a second aspect, the present invention provides a device for quickly determining a mutual fuzzy function for multi-objective optimization, the device for quickly determining a mutual fuzzy function for multi-objective optimization comprising: an initialization unit, an acquisition unit, a particle access 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 position to be searched and the size of the range to be searched, and obtain a Doppler frequency empty set according to the position to be searched and the size of the range to be searched; the Doppler frequency empty set is a set containing multiple frequency intervals;

[0042] The particle access unit is used to: store multiple targets searched by the external radiation source radar as particles in the Doppler frequency empty set to obtain the Doppler frequency set;

[0043] The first iteration unit is used to: based on the Doppler frequency set and the first preset iteration stop condition, use the particle swarm optimization algorithm 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; the first historical particle set is the particles searched by the particle swarm optimization algorithm in the Doppler frequency set; the first optimal particle set is the optimal fitness value of all small populations 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;

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

[0045] In a third aspect, the present invention provides a device for quickly determining mutual fuzzy functions for multi-objective optimization, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, when the device for quickly determining mutual fuzzy functions for multi-objective optimization is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for quickly determining mutual fuzzy functions for multi-objective optimization as described in the first aspect above.

[0046] The present invention provides a method, device and equipment for quickly determining mutual fuzzy functions for multi-target optimization. The method for quickly determining mutual fuzzy functions for multi-target optimization is applied to target determination of external radiation source radar, including: S101, initializing a historical particle set and an optimal particle set; S102, obtaining a position to be searched and a size of a range to be searched, and obtaining a Doppler frequency empty set according to the position to be searched and the size of the range to be searched; the Doppler frequency empty set is a set containing multiple frequency intervals; S103, taking multiple targets searched by the external radiation source radar as particles, storing them in the Doppler frequency empty set, and obtaining a Doppler frequency set; S104, based on the Doppler frequency set and a first preset iteration stop condition, using a particle swarm optimization algorithm to optimize the historical particle set and the optimal particle set; The first historical particle set is updated to obtain the first historical particle set, the first optimal particle set and the target particle; the first historical particle set is the particles searched based on the particle swarm optimization algorithm in the Doppler frequency set; the first optimal particle set is the optimal fitness value of all small populations 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, according to the target particle, the first historical particle set, the first optimal particle set and the second preset iteration stop condition, the position of the particles in the Doppler frequency set is updated by using the gray wolf optimization algorithm to obtain an update result, and a fuzzy result is obtained according to the update result; the fuzzy result is the final target of the external radiation source radar search. In the present invention, a particle swarm optimization algorithm is adopted to first determine particles with a larger fitness, and thereby obtain target particles; then the target particles are used as the optimization direction of the gray wolf optimization algorithm, thereby avoiding the consumption of computing resources caused by searching and traversing particles one by one, and the fuzzy results can be obtained quickly and accurately; finally, the fuzzy results are used as the final goal of the external radiation source radar search; that is, by adopting the combination of the particle swarm optimization algorithm and the gray wolf optimization algorithm, the problem that the existing particle swarm optimization algorithm is easy to fall into the global optimal solution is avoided, and the real-time performance and accuracy of the multi-target optimization of the external radiation source radar are improved.

[0047] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic flow chart of a method for quickly determining a mutual fuzzy function for multi-objective optimization provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of the accumulation results of the mutual fuzzy function obtained by using the cross-correlation FFT method provided by an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of the accumulation results of mutual fuzzy functions obtained by using a variable frequency search step size method provided by an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of the accumulation results of mutual fuzzy functions obtained by using a particle swarm optimization method provided by an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the accumulation results of mutual fuzzy functions obtained by the method of the present invention provided in an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of the structure of a device for quickly determining mutual fuzzy functions for multi-objective optimization provided by an embodiment of the present invention;

[0054] Figure 7 A schematic diagram of the structure of a device for quickly determining mutual fuzzy functions for multi-objective optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

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

[0056] In order to avoid the problem that the existing particle swarm optimization algorithm is prone to fall into the global optimal solution and to improve the real-time performance and accuracy of multi-target optimization of external radiation source radar, an embodiment of the present invention provides a method for quickly determining mutual fuzzy functions for multi-target optimization. Figure 1 A schematic diagram of a flow chart of a method for quickly determining a mutual fuzzy function for multi-objective optimization provided by an embodiment of the present invention. Figure 1 As shown, including:

[0057] S101, initializing the historical particle set and the optimal particle set.

[0058] S102: Obtain a position to be searched and a size of a range to be searched, and obtain an empty set of Doppler frequencies according to the position to be searched and the size of the range to be searched.

[0059] The Doppler frequency empty set is a set including multiple frequency intervals.

[0060] It should be noted that, in the embodiment of the present invention, the range to be searched includes: a time range to be searched and a frequency range to be searched.

[0061] S102 may specifically include:

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

[0063] The frequency range to be searched is divided into a plurality of frequency intervals according to a preset division interval within the position to be searched, so as to obtain a plurality of Doppler frequency intervals; wherein each Doppler frequency interval is regarded as a small population;

[0064] A plurality of Doppler frequency intervals constitute a Doppler frequency empty set.

[0065] In the embodiment of the present invention, the time range to be searched is expressed as [τ min ,τ max ], the frequency range to be searched is expressed as [f min ,f max ], divide the frequency range to be searched into equal intervals according to the preset division intervals, and obtain multiple Doppler frequency intervals, each Doppler frequency interval as a small population; initialize the historical particle set Optimal particle set

[0066] S103. Storing multiple targets found by the external radiation source radar as particles in a Doppler frequency empty set to obtain a Doppler frequency set.

[0067] In another possible implementation, in order to reduce the amount of calculation, the targets that meet the preset fitness screening threshold among the multiple targets searched by the external radiation source radar can be taken as particles and stored 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 stop condition, using the particle swarm optimization algorithm to update the historical particle set and the optimal particle set, to obtain a first historical particle set, a first optimal particle set and a target particle.

[0069] The first historical particle set is the particles searched in the Doppler frequency set based on the particle swarm optimization algorithm; the first optimal particle set is the optimal fitness value of all small populations 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 the embodiment of the present invention, the fitness values ​​in the first historical particle set are sorted in descending order, and the particles with the top three fitness values ​​are taken as target particles.

[0071] Optionally, S104 may specifically include:

[0072] S201, within the time range to be searched, 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 first particle fitness;

[0073] S202, determining the individual optimal value of each particle, the global optimal value of the small population, and the global optimal position of the small population according to the fitness of the first particle;

[0074] S203, updating the positions of all particles according to the global optimal value and the global optimal position of the small population to obtain updated particle positions;

[0075] S204, taking the particle update position as the particle current position, and repeatedly executing S201-S203 until a first preset iteration stop condition is reached;

[0076] S205, adding all particles processed before reaching the first preset iteration stop condition to the historical particle set to obtain a first historical particle set; adding the global optimal values ​​of all small populations to the optimal particle set to obtain a first optimal particle set; and forming the target particles from the first optimal particle set with the top three particles in fitness ranking.

[0077] In this embodiment of the present invention, the fitness values ​​of particles that have no intersection with the historical particle set are calculated, and the fitness values ​​of particles that have an intersection with the optimal particle set are set to 0. The remaining particles directly obtain fitness values ​​based on the historical particle set, and the individual optimal value p of each particle is updated. ij , the global optimal value gbest of each small population h (h=1,2,...,H) and the global optimal position p of each small population gj , where h represents the number of small populations.

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

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

[0080]

[0081] Among them, ω represents the weight coefficient, v ij '(t) is the velocity of the particle in the tth iteration of the particle swarm optimization algorithm, v ij '(t+1) represents the velocity of the particle at the t+1th iteration of the particle swarm optimization algorithm, x ij '(t) is the time-frequency point position of the particle, p ij '(t) represents the individual optimal value of each particle in the particle swarm optimization algorithm, p gj (t) represents the global optimal position of the small population, r1'(t) and r2'(t) are random numbers between [0,1], c1 and c2 are learning factors, where c1 has its own evolutionary attributes and c2 has social attributes. is the shrinkage factor, the purpose is to control the search ability of the particle swarm algorithm, select the appropriate It can increase the convergence speed of the particle swarm algorithm and enhance the search capability of the algorithm.

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

[0083] Among them, x ij '(t+1) indicates the particle update position at the t+1th iteration in the particle swarm optimization algorithm, x ij '(t) represents the particle position at the tth iteration in the particle swarm optimization algorithm.

[0084] Optionally, the first preset iteration stop 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.

[0085] S105. Based on the target particles, the first historical particle set, the first optimal particle set and the second preset iteration stop condition, the particles in the Doppler frequency set are updated using the Grey Wolf Optimization Algorithm to obtain an updated result, and a fuzzy result is obtained based on the updated result; the fuzzy result is the final target of the external radiation source radar search.

[0086] The embodiment of the present invention provides a method for quickly determining a mutual fuzzy function for multi-objective optimization, comprising: firstly determining particles with a larger fitness by adopting a particle swarm optimization algorithm, and obtaining target particles thereby; then taking the target particles as the optimization direction of a gray wolf optimization algorithm, avoiding the consumption of computing resources caused by searching and traversing the particles one by one, and obtaining fuzzy results quickly and accurately; finally taking the fuzzy results as the final goal of the external radiation source radar search; that is, by adopting a combination of the particle swarm optimization algorithm and the gray wolf optimization algorithm, the problem that the existing particle swarm optimization algorithm is easy to fall into the global optimal solution is avoided, and the real-time performance and accuracy of the multi-objective optimization of the external radiation source radar are improved.

[0087] Optionally, S105 may specifically include:

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

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

[0090] S302. Update the positions of the remaining particles according to the position of the target particle and the target distance to obtain the updated positions of the remaining particles.

[0091] S303: Obtain the fitness of the remaining particles according to the updated positions of the remaining particles and the first optimal particle set, and obtain a first result.

[0092] S304. Obtain a global optimal value and a global optimal position according to the first result.

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

[0094] S305. Use the fuzzy result as the final target of the external radiation source radar search.

[0095] It should be noted that, in the embodiment of the present invention, in order to reduce calculation, the fitness values ​​of particles that have no intersection with the first particle set can also be directly calculated, and the remaining particles directly obtain fitness values ​​from the first particle set.

[0096] In addition, in the embodiment of the present invention, all particles are simultaneously used as the gray wolf population, and the three particles with the best fitness are used 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 grey 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 represents the distance between α, β and δ and the remaining particles, C1, C2 and C3 represent the first random vector, the second random vector and the third random vector, respectively. α (t), X β (t) and X δ (t) represent the positions of α, β and δ respectively, and X(t) represents the position of the remaining particles in the grey wolf algorithm.

[0100]

[0101] Among them, A1, A2, A3 represent the first coefficient factor, the second coefficient factor and the third coefficient factor respectively, and X1(t), X2(t), X3(t) represent the positions of the remaining particles after being updated according to the positions of the gray wolves α, β, δ respectively.

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

[0103] Optionally, S302 may specifically include:

[0104] Based on the position and distance of the target particle, the positions of the remaining particles 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) indicates the updated position of the remaining particles at the t+1th iteration, x ij (t) represents the position of the remaining particles at the tth iteration, v ij (t+1) represents the update speed of the remaining particles at the t+1th iteration, represents the contraction factor, X(t) represents the position of the remaining particles in the gray wolf algorithm, ω represents the weight coefficient, r1(t) represents the first random number corresponding to the tth iteration, r2(t) represents the second random number corresponding to the tth iteration, c1 represents the evolutionary attribute learning factor, c2 represents the social attribute learning factor, p gj (t) represents the global optimal position of the small population, p ij (t) represents the individual optimal value of each particle.

[0108] It should be noted that, in the embodiment of the present invention, the update positions of the remaining particles are obtained based on the position of the target particle and the target distance, mainly with the target particle as the optimization update direction to perform the final target optimization selection.

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

[0110] Optionally, obtaining a global optimal value and a global optimal position according to the first result includes:

[0111] 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.

[0112] In order to verify the effectiveness of the method for quickly determining mutual fuzzy functions for multi-objective optimization provided by the embodiment of the present invention, a simulation experiment was also conducted in the embodiment of the present invention.

[0113] The simulation experiment assumes that the maximum detection distance and speed range of interest of the actual external radiation source radar are 90km and 360m / s, so the delay range that needs to be processed to achieve target detection is [0,300]μs and the Doppler frequency range is [-4,4]kHz. The accumulation time T = 0.1s, if the search is traversed in the Doppler frequency range, the ambiguity function at 800 frequency points needs to be calculated. In addition, the simulation parameters of the moving target are shown in Table 1.

[0114] Table 1 Target simulation parameters

[0115] Parameter Type Target A Target B Latency 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 schematic diagrams of the accumulation results of the mutual fuzzy functions obtained by the cross-correlation FFT method, the variable frequency search step method, the particle swarm optimization method and the method of the present invention are shown in order; Figure 2 Figure (a), Figure 3 Figure (a), Figure 4 Figure (a), Figure 5 Figure (a) shows the mutual fuzzy function accumulation result of the corresponding method. Figure 2 Figure (b), Figure 3 Figure (b), Figure 4 Figure (b), Figure 5 Figure (b) shows the delay-Doppler plane accumulation results of the corresponding method, where each line represents the total ambiguity function results within the delay search range at a certain frequency point, and the number of lines represents the number of calculated frequency points. Figure 2 Figure (b), Figure 3 Figure (b), Figure 4 Figure (b), Figure 5 The frequency points calculated in Figure (b) are 800, 410, 82, and 132 respectively.

[0117] Furthermore, the peak intersection of the two mutual fuzzy function results is taken as the searched target peak. It can be seen that the cross-correlation FFT method, the variable frequency search step method and the method of the present invention can search for all targets, and the method of the present invention has the least search frequency points, and the calculation amount is about 1 / 8 of the cross-correlation FFT method and 1 / 4 of the variable frequency search step method; the particle swarm optimization method has the least calculation amount, but only searches for target A, and the target is missed, which verifies the effectiveness of the method of the present invention.

[0118] The method provided in the embodiment of the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc., which is not limited in the embodiment of the present invention.

[0119] Based on the same inventive concept, an embodiment of the present invention further provides a device for quickly determining mutual fuzzy functions for multi-objective optimization. Figure 6 A schematic diagram of a structure of a device for quickly determining mutual fuzzy functions for multi-objective optimization provided by an embodiment of the present invention. Figure 6 As shown, it includes: an initialization unit 601, an acquisition unit 602, a particle access unit 603, a first iteration unit 604 and a second iteration unit 605;

[0120] The 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 position to be searched and the size of the range to be searched, and obtain a Doppler frequency empty set according to the position to be searched and the size of the range to be searched; the Doppler frequency empty set is a set containing multiple frequency intervals;

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

[0123] The first iteration unit 604 is used to: based on the Doppler frequency set and the first preset iteration stop condition, use the particle swarm optimization algorithm 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; the first historical particle set is the particles searched by the particle swarm optimization algorithm in the Doppler frequency set; the first optimal particle set is the optimal fitness value of all small populations 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;

[0124] The second iteration unit 605 is used to: based on the target particles, the first historical particle set, the first optimal particle set and the second preset iteration stop condition, use the Gray Wolf optimization algorithm to update the positions of the particles in the Doppler frequency set to obtain an update result, and obtain a fuzzy result based on the update result; the fuzzy result is the final target of the external radiation source radar search.

[0125] Figure 7 A schematic diagram of the structure of a device for quickly determining a mutual fuzzy function for multi-objective optimization provided by an embodiment of the present invention includes: a processor 710, a storage medium 720 and a bus 730, wherein the storage medium 720 stores machine-readable instructions executable by the processor 710, and when the device for quickly determining a mutual fuzzy function for multi-objective optimization is running, the processor 710 communicates with the storage medium 720 via the bus 730, and the processor 710 executes the machine-readable instructions to execute the steps of the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

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

[0127] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, 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 the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order 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. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0129] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations 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 one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0130] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other changes of the above disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the term "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and the meaning of "multiple" is two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0131] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for quickly determining mutual fuzzy functions for multi-target optimization, applied to target determination of external radiation source radar, characterized in that: include: S101, initializing the historical particle set and the optimal particle set; S102, obtaining a position to be searched and a size of a range to be searched, and obtaining an empty Doppler frequency set according to the position to be searched and the size of the range to be searched; the empty Doppler frequency set is a set including multiple frequency intervals; S103, taking the multiple targets searched by the external radiation source radar as particles, storing them in the Doppler frequency empty set, and obtaining a Doppler frequency set; S104, based on the Doppler frequency set and the first preset iteration stop condition, the historical particle set and the optimal particle set are updated by 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 is the particles in the Doppler frequency set that have been searched by the particle swarm optimization algorithm; the first optimal particle set is the optimal fitness value of all small populations 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; S105, performing position update processing on particles in the Doppler frequency set using a Grey Wolf Optimization Algorithm according to the target particle, the first historical particle set, the first optimal particle set, and a second preset iteration stop condition, obtaining an update result, and obtaining a fuzzy result according to the update result; The fuzzy result is the final target of the external radiation source radar search.

2. The method for quickly determining mutual fuzzy functions for multi-objective optimization according to claim 1, characterized in that: The range to be searched includes: a time range to be searched and a frequency range to be searched, and the acquiring of the position to be searched and the size of the range to be searched, and obtaining the empty set of Doppler frequencies according to the position to be searched and the size of the range to be searched, includes: Obtaining a location to be searched, a time range to be searched, a frequency range to be searched, and a preset division interval; Dividing the frequency range to be searched into a plurality of frequency intervals according to the preset division interval within the position to be searched, to obtain a plurality of Doppler frequency intervals; wherein each of the Doppler frequency intervals is regarded as a small population; The multiple Doppler frequency intervals constitute the Doppler frequency empty set.

3. The method for quickly determining mutual fuzzy functions for multi-objective optimization according to claim 2, characterized in that: The method of updating the historical particle set and the optimal particle set by using a particle swarm optimization algorithm based on the Doppler frequency set and the first preset iteration stop condition to obtain a first historical particle set, a first optimal particle set and a target particle includes: S201, within the time range to be searched, based on the mutual fuzzy function algorithm and the current position of the particle, calculating the fitness value of the particle in the Doppler frequency set that has no intersection with the historical particle set, to obtain a first particle fitness; S202, determining the individual optimal value of each particle, the global optimal value of the small population, and the global optimal position of the small population according to the first particle fitness; S203, updating the positions of all particles according to the global optimal value of the small population and the global optimal position of the small population to obtain updated particle positions; S204, taking the particle update position as the particle current position, and repeatedly executing S201-S203 until the first preset iteration stop condition is reached; S205, adding all particles processed before reaching the first preset iteration stop condition to the historical particle set to obtain the first historical particle set; adding the global optimal values ​​of all the small populations to the optimal particle set to obtain the first optimal particle set; and forming the target particles from the first optimal particle set with the top three particles in fitness ranking.

4. The method for quickly determining mutual fuzzy functions for multi-objective optimization according to claim 1 or 3, characterized in that: The first preset iteration stop 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 quickly determining mutual fuzzy functions for multi-objective optimization according to claim 1, characterized in that: According to the target particle, the first historical particle set, the first optimal particle set and the second preset iteration stop condition, the particles in the Doppler frequency set are updated using the Grey Wolf Optimization Algorithm to obtain an update result, and a fuzzy result is obtained according to the update result; The method further comprises: taking the fuzzy result as the final target of the external radiation source radar search, comprising: S301, obtaining a target distance between the target particle and the current positions of the remaining particles; The remaining particles are all particles in the Doppler frequency set except the target particle; S302, updating the positions of the remaining particles according to the position of the target particle and the target distance to obtain updated positions of the remaining particles; S303, acquiring the fitness of the remaining particles according to the updated positions of the remaining particles and the first optimal particle set, and obtaining a first result; S304. Obtain a global optimal value and a global optimal position according to the first result; S305, taking the updated positions of the remaining particles in S302 as the current positions of the remaining particles in S301, repeatedly executing S301-S304 until the second preset iteration stop condition is satisfied, and taking the global optimal value corresponding to the most recent execution of S304 as the fuzzy result; S305. Use the fuzzy result as the final target of the external radiation source radar search.

6. The method for quickly determining mutual fuzzy functions for multi-objective optimization according to claim 5, characterized in that: The updating of the positions of the remaining particles according to the position of the target particle and the target distance to obtain the updated positions of the remaining particles comprises: Based on the position of the target particle and the target distance, the positions of the remaining particles are updated using the following formula to obtain the updated positions of the remaining particles: x ij (t+1)=x ij (t)+v ij (t+1); x ij (t+1) indicates the updated position of the remaining particles at the t+1th iteration, x ij (t) represents the position of the remaining particles at the tth iteration, v ij (t+1) represents the update speed of the remaining particles at the t+1th iteration, represents the contraction factor, X(t) represents the position of the remaining particles in the gray wolf algorithm, ω represents the weight coefficient, r1(t) represents the first random number corresponding to the tth iteration, r2(t) represents the second random number corresponding to the tth iteration, c1 represents the evolutionary attribute learning factor, c2 represents the social attribute learning factor, p gj (t) represents the global optimal position of the small population, p ij (t) represents the individual optimal value of each particle.

7. The method for quickly determining mutual 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 a second preset iteration threshold or the global optimal value reaches a preset target screening threshold.

8. The method for quickly determining mutual fuzzy functions for multi-objective optimization according to claim 5, characterized in that: The obtaining of the global optimal value and the global optimal position according to the first result includes: The global optimal value of all small populations included in the first result is used as the global optimal value, and the position corresponding to the global optimal value is used as the global optimal position.

9. A device for quickly determining mutual fuzzy functions for multi-objective optimization, characterized in that: The mutual fuzzy function fast determination device 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 a position to be searched and a size of a range to be searched, and obtain a Doppler frequency empty set according to the position to be searched and the size of the range to be searched; the Doppler frequency empty set is a set including multiple frequency intervals; The particle access unit is used to: store the multiple targets searched by the external radiation source radar as particles in the Doppler frequency empty set to obtain a Doppler frequency set; The first iteration unit is used to: based on the Doppler frequency set and the first preset iteration stop condition, use the particle swarm optimization algorithm 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; the first historical particle set is the particles in the Doppler frequency set that have been searched based on the particle swarm optimization algorithm; the first optimal particle set is the optimal fitness value of all small populations included in the first historical particle set; the target particle is the first 3 particles in the first historical particle set ranked by fitness; The second iteration unit is used to: perform position update processing on the particles in the Doppler frequency set using the Grey Wolf Optimization Algorithm according to the target particles, the first historical particle set, the first optimal particle set and a second preset iteration stop condition to obtain an update result, and obtain a fuzzy result based on the update result; the fuzzy result is the final target of the external radiation source radar search.

10. A device for quickly determining mutual fuzzy functions for multi-objective optimization, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the device for quickly determining mutual fuzzy functions 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 quickly determining mutual fuzzy functions for multi-objective optimization as described in any one of claims 1-8.

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