High-speed maneuvering coherent accumulation method based on GA and GRFT

Abstract

The application discloses a high-speed maneuvering coherent accumulation method based on GA (genetic algorithm) and GRFT (generalized Radon-Fourier transform), which comprises the following steps: after the echo signal of a high-speed maneuvering target after pulse compression is subjected to symmetric windowing processing, the generalized Radon-Fourier transform is carried out after the blind speed sidelobe is suppressed, and the parameter searching process is optimized by using the genetic algorithm, and finally the target detection result is output. The GA comprises the following steps: the basic parameter range and step length are determined, the population is initialized and the fitness value is calculated, the population is updated through the genetic operator operation, and the maximum fitness value and the corresponding best individual are obtained. The application combines the GA with the GRFT, effectively reduces the algorithm complexity under the premise of guaranteeing the detection performance, and thus the coherent accumulation of the high-speed maneuvering target is completed.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing, specifically relating to a coherent accumulation method for high-speed maneuvering targets based on GA and GRFT. Background Technology

[0002] To improve the reliability of radar target detection, coherent accumulation of echo signals is usually performed to improve the signal-to-noise ratio. However, high-speed and high-maneuverability targets will produce range migration (RM) and Doppler frequency migration (DFM) phenomena between echo pulses, which seriously affect the coherent accumulation gain and make the traditional coherent accumulation method for uniform targets ineffective. Therefore, it is necessary to study new algorithms or even new radar systems to solve this problem.

[0003] Conventional high-speed target detection methods utilize the Keystone Transform in PD radar. This algorithm can correct range movement without prior knowledge, but it is unsuitable for velocity ambiguity. Another approach is the Adjacent Cross-Correlation Function (ACCF) algorithm, which avoids the computational burden of parameter search and effectively eliminates blind velocity sidelobes, but suffers from significant performance degradation. In 2011, Xu Jia proposed a coherent accumulation algorithm based on the Radon Fourier Transform (RFT). This algorithm acquires echo signals through parameter search on the range-velocity plane, constructs a Doppler matched filter, and achieves coherent accumulation of high-speed target echo signals. However, the targets to be detected are often maneuvering in real-world situations, requiring consideration of acceleration and even higher-order motion parameters. Therefore, the Generalized RFT (GRFT) for arbitrary parameterized targets was proposed. Since GRFT requires multi-dimensional traversal search, the dimensionality of the search space increases with the order of motion, leading to a significant increase in computational cost. Reducing the computational cost of GRFT remains a challenge. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention proposes a coherent accumulation method for high-speed maneuvering targets based on GA and GRFT.

[0005] The technical problem to be solved by this invention is achieved through the following technical solution:

[0006] A coherent accumulation method for high-speed maneuvering targets based on GA and GRFT includes:

[0007] Step 1: Acquire the raw echo signal of the high-speed maneuvering target;

[0008] Step 2: Perform pulse compression on the echo signal to obtain a two-dimensional data matrix;

[0009] Step 3: Suppress BSSL;

[0010] Step 4: Confirm the target's motion order, the search interval it occupies, and the search step size;

[0011] Step 5: Establish initial values ​​within the search space, substitute the randomly generated initial population into GRFT to accumulate the energy of the echo signal, and calculate the objective function value;

[0012] Step 6: The population is updated and iterated, and the new objective function value is output by substituting it into GRFT;

[0013] Step 7: GA terminates the operation, outputs the detected motion parameters, and obtains the target detection result.

[0014] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, step 1 includes:

[0015] Step 11, acquire the echo signal of the high-speed maneuvering target, represented as:

[0016]

[0017] in, Echo amplitude factor To save time, For slow pulse duration, It is a pulse sequence. The pulse repetition time, ;

[0018] Step 12, Establish the target motion model

[0019]

[0020] in, The initial distance to the target. Let the order of motion of the target be . For the first First-order motion parameters;

[0021] Step 13: Discretize the echo signal in the fast time domain according to the preset sampling frequency and number of sampling points of the radar system to obtain the discretized echo signal.

[0022] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, step 2 includes:

[0023] Step 21: Perform a Fast Fourier Transform on the discretized sampled echo signal;

[0024] Step 22: Perform frequency domain pulse compression on the echo signal after the fast Fourier transform, and then perform inverse Fourier transform to obtain a two-dimensional data matrix.

[0025] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, step 3 includes:

[0026] Step 31: Perform window function weighting on the matched filter to transform it into a mismatched filter;

[0027] Step 32: Filter the pulse compression data to suppress blind velocities and side lobes.

[0028] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, step 4 includes:

[0029] To balance computational load and detection performance, the search step size is set to 1, ignoring the influence of the fractional part of the motion parameters on the coherent accumulation results.

[0030] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, the search step size is set to 1, including:

[0031] In the genetic algorithm, an "integer constraint" is set to achieve a search step size of 1.

[0032] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, step 5 includes:

[0033] Step 51: Encode the motion parameters of the search space into binary form, and convert the binary-encoded motion parameters of the search space into chromosomes composed of genes in the genetic space.

[0034] Step 52: Substitute the individuals in the initial population randomly generated by GA into the GRFT algorithm to find their output peak value.

[0035] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, step 6 includes:

[0036] Step 61: Set the fitness function to evaluate the quality of solutions in the parameter space;

[0037] Step 62: Perform genetic operations on the initial population, applying selection, crossover, and mutation operators to the initial population with preset probabilities to update and iterate the individuals in the initial population.

[0038] Step 63: Calculate the objective function value and the corresponding fitness value;

[0039] Repeat steps 61-63 until GA finishes running.

[0040] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, step 63 includes:

[0041] The fitness is correlated with the objective function value; that is, the larger the peak value of the GRFT output, the higher the fitness of the solution. The fitness is taken as a positive value and set...

[0042]

[0043] in, For the fitness function, Let be the objective function. For positive constants .

[0044] Furthermore, in the aforementioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, the crossover probability in the crossover operator is taken as... The mutation probability of the mutation operator is taken as .

[0045] Furthermore, in the above-mentioned coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, the GA calculation terminates as follows:

[0046] Case 1: The number of iterations reaches the preset value, set to... ;

[0047] Scenario 2: The fitness of individuals in the initial population reaches a threshold.

[0048] Case 3: After a period of iteration, the number of generations in which the population fitness tends to stabilize exceeds the predetermined value.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0050] This invention provides a coherent accumulation method for high-speed maneuvering targets based on GA and GRFT optimization. Under each set of motion parameters in the search space, the number of distance units spanned by each pulse is calculated, and then the echo envelope is compensated to align it with the distance unit where the initial radial distance is located, thus completing signal extraction. At the same time, a Doppler filter bank is constructed to compensate for Doppler frequency shifts, thereby achieving coherent accumulation of the echo of the high-speed maneuvering target. On the other hand, a genetic algorithm is introduced to optimize the above process, automatically capturing and accumulating knowledge about the parameter space during the optimization process, and adaptively controlling the search process to obtain the global optimal solution. Compared with the GRFT algorithm, the advantages of this invention are: (1) the adoption of the GA intelligent optimization algorithm reduces the algorithm complexity; (2) symmetrical windowing suppresses BSSL; (3) due to the introduction of the GA termination condition, the processing process becomes adaptive and more efficient.

[0051] The present invention will now be further described with reference to the accompanying drawings and embodiments. Attached Figure Description

[0052] Figure 1 This is a flowchart of a high-speed maneuvering target coherent accumulation method based on GA and GRFT provided in an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the GRFT search process and results based on GA optimization under simulation parameters;

[0054] Figure 3 This is a schematic diagram of the actual location of the target searched using GRFT based on GA optimization under simulation parameters;

[0055] Figure 4 This is a comparison chart of the computational complexity of the GRFT algorithm based on GA optimization and the traditional GRFT algorithm. Detailed Implementation

[0056] 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.

[0057] Figure 1 This is a flowchart of a high-speed maneuvering target coherent accumulation method based on GA and GRFT provided in an embodiment of the present invention. Please refer to... Figure 1 This invention provides a method for coherent accumulation of high-speed maneuvering targets based on GA and GRFT, comprising:

[0058] Step 1: Acquire the raw echo signal of the high-speed maneuvering target;

[0059] Step 2: Perform pulse compression on the echo signal to obtain a two-dimensional data matrix;

[0060] Step 3: Suppress BSSL;

[0061] Step 4: Confirm the target's motion order, the search interval it occupies, and the search step size;

[0062] Step 5: Establish initial values ​​within the search interval, substitute the randomly generated initial population into GRFT to accumulate the energy of the echo signal, and calculate the objective function value;

[0063] Step 6: The population is updated and iterated, and the new objective function value is output by substituting it into GRFT;

[0064] Step 7: GA terminates the operation, outputs the detected motion parameters, and obtains the target detection result.

[0065] In this embodiment, the radar system first acquires the transmitted signal and the target echo signal, and then performs high-speed maneuvering target coherent accumulation on the pulse-compressed signal.

[0066] Optionally, step 1, the step of acquiring the original echo signal of a high-speed maneuvering target, includes:

[0067] Assuming the transmitted signal is a linear frequency modulation (LFM) pulse signal, its time-domain expression is:

[0068]

[0069] in, The amplitude factor of the transmitted signal. The pulse width. For carrier frequency, For frequency modulation slope, This refers to the signal bandwidth.

[0070] After the transmitted signal is reflected by a high-speed maneuvering target, the radar receives the following echo signal:

[0071]

[0072] in, Echo amplitude factor To save time, For slow pulse duration, It is a pulse sequence. The pulse repetition time, .

[0073] This can be expressed as a polynomial of the travel distance and the pulse slow time. This parameter is related to the motion model of the high-speed maneuvering target.

[0074]

[0075] in, The initial distance to the target. Let the order of motion of the target be . For the first First-order motion parameters.

[0076] The echo signal is sampled and discretized. In this embodiment, the echo signal is discretized and sampled in the fast time domain according to the sampling frequency and number of sampling points preset by the radar system.

[0077] Optionally, step 2, the step of pulse compression of the echo signal to obtain a two-dimensional data matrix, includes:

[0078] Step 21: Perform a Fast Fourier Transform on the discretized sampled echo signal;

[0079] Step 22: Perform frequency domain pulse compression on the echo signal after the fast Fourier transform, and then perform inverse Fourier transform.

[0080] After pulse compression, the echo signal becomes:

[0081]

[0082] in, The amplitude factor of the pulse compression signal. This is the pulse pressure gain.

[0083] Optionally, step 3, the step of suppressing BSSL, includes:

[0084] The matched filter is weighted using a window function to reduce sidelobes. Symmetrical windowing is applied to the matched filter; suitable window functions include the Hamming window and the Hanning window. After weighting, the matched filter becomes a mismatched filter, and its impulse response is:

[0085]

[0086] in, For the impulse response of the matched filter, The impulse response is a window function.

[0087] After windowing, the output signal is

[0088]

[0089] in, For echo signal, This is the pulse compression output signal.

[0090] Optionally, step 4, which involves confirming the target's motion order, its search interval, and search step size, includes:

[0091] Identify the parameter set of the actual problem, namely the motion order of the maneuvering target in the GRFT algorithm, as well as the search range and search step size of each motion parameter.

[0092] Optionally, in order to balance computational load and detection performance, the search step size is set to 1, the precision is set to the integer part of the motion parameters, and the influence of the decimal part on the coherent accumulation result is ignored.

[0093] Optionally, step 5, which involves establishing initial values ​​within the search interval, substituting the randomly generated initial population into GRFT to accumulate the energy of the echo signal, and calculating the objective function value, includes:

[0094] Step 51: Encode the motion parameters of the search space and convert them into chromosomes composed of genes in the genetic space;

[0095] Alternatively, the encoding method can be the commonly used binary encoding, using binary strings. This is used to represent candidate values ​​in the search space.

[0096] Step 52: Substitute the initial individuals randomly generated by GA into the GRFT algorithm to find their output peak value.

[0097] Based on existing knowledge, determine the distribution range of the population throughout the parameter space, and randomly generate an initial population within this range.

[0098] Discrete form of the GRFT algorithm:

[0099]

[0100] Optionally, step 6, the step of updating and iterating the population and substituting the values ​​into the GRFT to output new objective function values, includes:

[0101] Step 61: Set the fitness function to evaluate the quality of solutions in the parameter space;

[0102] In nature, fitness refers to an individual's ability to adapt to its environment, while in parameter space, it is used to evaluate the quality of solutions. Fitness must be positive.

[0103] Optionally, the objective function value is correlated with fitness here; that is, the larger the peak value of the GRFT output, the higher the fitness of the solution. This can be set to...

[0104]

[0105] in, For the fitness function, Let be the objective function. For positive constants .

[0106] Step 62: Perform genetic operations on the population, applying selection, crossover, and mutation operators to the population with a certain probability to update and iterate individuals, thereby generating new motion parameters.

[0107] Selection operator: Selects superior individuals from the current population to be passed on as parents to the next generation. The selection operation uses the fitness of individuals as the evaluation criterion; individuals with higher fitness have a greater probability of being passed on to the next generation.

[0108] Crossover operator: Crossover is the core operation to improve search capabilities, which involves exchanging and recombinating parts of the chromosomes of two parent individuals to generate new individuals;

[0109] Mutation operator: The individual to be mutated randomly selects gene values ​​at certain loci for mutation.

[0110] Optionally, the selection rate can be set to be proportional to the fitness value; alternatively, the crossover probability can be set to... The mutation probability is taken as .

[0111] Step 63: Calculate the objective function value and the corresponding fitness value;

[0112] Repeat steps 61-63 until GA finishes running.

[0113] Optionally, step 7, where the GA calculation is terminated and the detected motion parameters are output to obtain the target detection result, includes:

[0114] The following are the conditions under which GA ends its operation:

[0115] Case 1: The number of iterations (generation) reaches a preset value; optionally, the typical range is [range to be specified]. .

[0116] Scenario 2: The individual's fitness reaches the threshold;

[0117] Scenario 3: After a period of iteration, the number of generations in which the population fitness stabilizes exceeds a predetermined value. Optionally, the range is typically set to... .

[0118] If GA satisfies one of the above conditions, the algorithm exits the loop iteration and outputs the maximum value and the corresponding optimal motion parameters; otherwise, steps 61-63 are repeated.

[0119] After the GA-optimized GRFT algorithm completes, it outputs the optimal solution to the objective function, which is the maximum value of the GRFT output. It also outputs the optimal individual, which represents the values ​​of each motion parameter when the GRFT output reaches its maximum value.

[0120]

[0121] in, Individuals generated by the GA algorithm, This is the optimal individual value output.

[0122] After obtaining the estimated values ​​of each order of motion parameters, the distance travel and Doppler frequency shift are corrected, and coherent accumulation is performed to output the true initial position of the target.

[0123] The following simulation experiment illustrates the effectiveness of the coherent accumulation method for high-speed maneuvering targets based on GA and GRFT.

[0124] I. Experimental conditions:

[0125] The main parameters of the radar system in the simulation experiment are shown in Table 1. The target's motion parameters in the simulation experiment are of the third order. The target's actual initial distance is 10 km, its radial velocity is 2000 m / s, and its radial acceleration is 35 m / s². 2 The second radial acceleration is 30 m / s². 3 The radial velocity search range is [1500 m / s, 2500 m / s], and the radial acceleration search range is [30 m / s²]. 2 40m / s 2 The radial second acceleration search range is [25m / s²]. 3 35m / s 3 The GA parameter constraint is selected as "integer constraint".

[0126] Table 1 Main Parameters of Radar System

[0127]

[0128] II. Experimental Content and Results:

[0129] The GRFT search process was optimized using GA, with the population size set to 50, the maximum number of generations to be 300, and the number of stagnant generations to be 100.

[0130] Figure 2 This describes the iterative process of GA optimizing GRFT and the optimal individual output after GA. The fitness value of the population stabilizes after 60 generations and reaches a stagnant value after 160 generations, at which point the iteration process ends. The optimal solution corresponds to the three variable values ​​[2000, 30, 35], consistent with the target true values.

[0131] Figure 3 It is the initial distance result of the target after coherent accumulation. The signal amplitude takes the maximum value at 10km, which is consistent with the actual initial position of the target.

[0132] Figure 4 This is a computational analysis of an embodiment of the present invention, wherein the blue bars represent the computational cost required by the traditional GRFT algorithm, and the orange bars represent the computational cost required by the GA and GRFT-based algorithm proposed in this invention.

[0133] As the target motion state becomes more complex, i.e., the motion order increases, more motion parameters need to be searched, and the computational complexity of GRFT becomes very high. A coherent accumulation algorithm based on GA and GRFT can reduce the computational complexity to some extent. Figure 4 It can be seen that as the order of motion increases, the computational cost of traditional GRFT increases exponentially, while the computational cost of GA-GRFT is basically on the same order of magnitude.

[0134] This invention combines the GA and GRFT algorithms, significantly reducing the computational load of the algorithms while ensuring detection performance, which is beneficial for achieving real-time radar detection of high-speed maneuvering targets.

Claims

1. A coherent accumulation method for high-speed maneuvering targets based on GA and GRFT, characterized in that, include: Step 1: Acquire the echo signal of a high-speed maneuvering target; Step 2: Perform pulse compression on the echo signal to obtain a two-dimensional data matrix; Step 3: Suppress BSSL; Step 4: Confirm the target's motion order, its search space, and search step size; Step 5: Establish initial values ​​within the search space, substitute the randomly generated initial population into GRFT to accumulate the energy of the echo signal, and calculate the objective function value; Step 6: The population is updated and iterated, and the new objective function value is output by substituting it into GRFT; Step 7: The GA operation is terminated, and the detected motion parameters are output to obtain the target detection result; Step 5 includes: Step 51: Encode the motion parameters of the search space into binary form, and convert the binary-encoded motion parameters of the search space into chromosomes composed of genes in the genetic space. Step 52: Substitute the individuals in the initial population randomly generated by GA into the GRFT algorithm to find their output peak value; Step 6 includes: Step 61: Set the fitness function to evaluate the quality of solutions in the parameter space; Step 62: Perform genetic operations on the initial population, applying selection, crossover, and mutation operators to the initial population with preset probabilities to update and iterate the individuals in the initial population; Step 63: Calculate the objective function value and the corresponding fitness value; Repeat steps 61-63 until GA finishes running; Step 63 includes: The fitness is correlated with the objective function value; that is, the larger the peak value of the GRFT output, the higher the fitness of the solution. The fitness is taken as a positive value and set... in, For the fitness function, Let be the objective function. For positive integers, make .

2. The coherent accumulation method for high-speed maneuvering targets based on GA and GRFT according to claim 1, characterized in that, Step 1 includes: Step 11, acquire the echo signal of the high-speed maneuvering target, represented as: in, Echo amplitude factor To save time, For slow pulse duration, It is a pulse sequence. The pulse repetition time, ; It is a polynomial of motion distance and pulse slow time; For frequency modulation slope, For signal bandwidth; The pulse width. For carrier frequency; Step 12, Establish the target motion model in, The initial distance to the target. Let the order of motion of the target be . For the first First-order motion parameters; Step 13: Discretize the echo signal in the fast time domain according to the preset sampling frequency and number of sampling points of the radar system to obtain the discretized echo signal.

3. The coherent accumulation method for high-speed maneuvering targets based on GA and GRFT according to claim 2, characterized in that, Step 2 includes: Step 21: Perform a Fast Fourier Transform on the discretized sampled echo signal; Step 22: Perform frequency domain pulse compression on the echo signal after the fast Fourier transform, and then perform inverse Fourier transform to obtain a two-dimensional data matrix.

4. The coherent accumulation method for high-speed maneuvering targets based on GA and GRFT according to claim 3, characterized in that, Step 3 includes: Step 31: Perform window function weighting on the matched filter to transform it into a mismatched filter; Step 32: Filter the pulse compression data to suppress blind velocities and side lobes.

5. The coherent accumulation method for high-speed maneuvering targets based on GA and GRFT according to claim 4, characterized in that, Step 4 includes: To balance computational load and detection performance, the search step size is set to 1, ignoring the influence of the fractional part of the motion parameters on the coherent accumulation results.

6. The coherent accumulation method for high-speed maneuvering targets based on GA and GRFT according to claim 5, characterized in that, Set the search step size to 1, including: In the genetic algorithm, an "integer constraint" is set to achieve a search step size of 1.

7. The coherent accumulation method for high-speed maneuvering targets based on GA and GRFT according to claim 1, characterized in that, Crossover probability in crossover operator The mutation probability of the mutation operator is taken as .

8. The coherent accumulation method for high-speed maneuvering targets based on GA and GRFT according to claim 1, characterized in that, The following are the conditions under which GA terminates its operation: Case 1: The number of iterations reaches the preset value, set to... ; Scenario 2: The fitness of individuals in the initial population reaches a threshold. Case 3: After a period of iteration, the number of generations in which the population fitness tends to stabilize exceeds the predetermined value.

Images