Broadband distributed radar target echo coherent accumulation method based on DMSO

Abstract

The invention discloses a DMSO-based broadband distributed radar target echo coherent accumulation method, and relates to the technical field of radar signal processing, and the method comprises the steps: obtaining a target echo signal of a first receiving station, carrying out the pulse compression of the target echo signal of the first receiving station, obtaining a pulse compression result of the target echo signal of the first receiving station, and carrying out the pulse compression of the target echo signal of the first receiving station; carrying out multi-station synchronization processing to obtain synchronization pulse compression results of a plurality of receiving stations; part of parameters in the multiple receiving station synchronization pulse compression results serve as to-be-optimized parameters, a preset bimodal collaborative optimization algorithm is adopted for optimization, and a parameter optimization result is obtained; performing Doppler frequency compensation, dispersion phase compensation and range migration correction on the synchronization pulse compression results of the plurality of receiving stations by adopting the parameter optimization result to obtain a compensated signal; coherent accumulation is carried out on the compensated signals, whether a target exists or not is detected, and if yes, target information is obtained, and the target is displayed. The target detection performance can be improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar signal processing, and particularly relates to a wideband distributed radar target echo coherent accumulation method based on DMSO. BACKGROUND

[0002] The wideband distributed radar is a new system radar adopting a wideband transmitting signal. The radar reduces the power spectrum density by adopting a transmitting signal with a larger total bandwidth, thereby improving the low interception detection performance of the radar. In addition, the radar can detect the same target at different angles due to the large interval distance between the stations of the radar, thereby obtaining the spatial diversity gain. In addition, the structure of multi-station transmission and multi-station reception enables the wideband distributed radar to obtain higher processing gain relative to the single station radar, so that the radar detection distance can be increased under the condition of the same transmitting power, or the transmitting power of the single station can be reduced under the condition of ensuring the radar detection distance, thereby further reducing the interception probability of the radar signal.

[0003] However, when the wideband distributed radar detects a target, the distance resolution is too high, and the channelization processing needs to be performed first, and then the target information is extracted. When the target information is extracted, due to the large bandwidth of the wideband distributed radar, the frequency difference between the channels is large, different frequencies correspond to different phases of the target scattering cross section, that is, there is a dispersion phase. If the dispersion phase is not compensated, the channels can only be incoherently accumulated. The incoherent accumulation has a relatively low gain, and it is difficult to meet the requirements of radar target detection and parameter estimation in the low interception detection scene. In addition, there is a problem of the Doppler frequency difference between the channels in the target detection of the wideband distributed radar, which seriously restricts the coherent accumulation efficiency. Therefore, the wideband distributed radar needs to compensate the dispersion phase and the Doppler phase error between the channels at the same time, so as to realize the coherent accumulation between the channels, improve the accumulation gain of the target echo, and thereby improve the subsequent target detection performance.

[0004] Coherent accumulation and incoherent accumulation are two commonly used methods to improve the signal-to-noise ratio in radar signal processing. Since incoherent accumulation does not utilize the phase information of the radar signal, it remains applicable to broadband distributed radar. However, the existence of inter-channel phase, Doppler frequency difference, and intra-channel range migration necessitates searching for these parameters to achieve fully coherent accumulation of the broadband distributed radar echo signal. Existing search methods include ergonomic search and swarm intelligence optimization algorithms. Coherent accumulation methods require searching for these parameters; commonly used ergonomic search methods often have extremely high computational costs and suffer from performance loss due to quantization effects. While classic swarm intelligence optimization methods such as particle swarm optimization and sparrow algorithms do not have quantization errors, they are prone to getting trapped in local optima and have difficulty converging quickly, making them difficult to directly apply to high-dimensional optimization problems.

[0005] Therefore, there is an urgent need to provide a broadband distributed radar target echo coherent accumulation method to improve the problems existing in the current technology. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a broadband distributed radar target echo coherent accumulation method based on DMSO. The technical problem to be solved by this invention is achieved through the following technical solution: In a first aspect, the present invention provides a broadband distributed radar target echo coherent accumulation method based on DMSO, comprising: Get the The target echo signal of the receiving station, and the first The target echo signal from the receiving station is processed by pulse compression to obtain the first... Pulse compression results of the target echo signal from each receiving station; By synchronizing the target echo signals from multiple receiving stations, the synchronization pulse compression result of multiple receiving stations is obtained. Some parameters from the synchronization pulse compression results of multiple receiving stations are used as parameters to be optimized, and a preset dual-mode collaborative optimization algorithm is used to optimize them to obtain the parameter optimization results. The parameter optimization results were used to perform Doppler frequency compensation, dispersion phase compensation, and range migration correction on the synchronization pulse compression results of multiple receiving stations to obtain the compensated signal. The compensated signal is coherently accumulated, and the presence of a target is detected. If a target is found, the target information is obtained and the target is displayed.

[0007] The beneficial effects of this invention are: The application provides a DMSO-based wideband distributed radar target echo coherent accumulation method, proposes a bimodal collaborative optimization algorithm, and applies the algorithm to wideband distributed radar echo signal processing, the optimization method has global search capability and local search capability and can realize high-dimensional problem optimization under a large solution space condition, when the method is used for accumulating wideband distributed radar target echoes, dispersion phase and Doppler frequency difference between channels can be compensated, distance migration in the channel can be corrected, full coherent accumulation of echo signals is realized, and subsequent target detection performance is improved.

[0008] The application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 is a flowchart of the DMSO-based wideband distributed radar target echo coherent accumulation method provided by the embodiment of the application; Figure 2 is a schematic diagram of a wideband distributed radar working scene provided by the embodiment of the application; Figure 3 is a schematic diagram of a bimodal collaborative optimization algorithm model provided by the embodiment of the application; Figure 4 is a flowchart of the bimodal collaborative optimization algorithm provided by the embodiment of the application; Figure 5 is a flowchart of searching for optimal optimal parameters provided by the embodiment of the application; Figure 6 is a flowchart of algorithm initialization provided by the embodiment of the application; Figure 7 is a flowchart of algorithm initialization provided by the embodiment of the application; Figure 8 is a flowchart of accumulation detection provided by the embodiment of the application; Figure 9a is a schematic diagram of a function image of a Step function as a unimodal function provided by the embodiment of the application; Figure 9b is a schematic diagram of a function image of an Ackley function as a multimodal function provided by the embodiment of the application; Figure 10a is a schematic diagram of a coherent accumulation result provided by the embodiment of the application; Figure 10b is a schematic diagram of a non-coherent accumulation result provided by the embodiment of the application; Figure 11 is a schematic diagram of a CFAR detection result of the DMSO algorithm provided by the embodiment of the application; Figure 12 This is a schematic diagram of the CFAR detection result of the PSO algorithm provided in an embodiment of the present invention; Figure 13 This is a schematic diagram of the fitness iteration curve of the algorithm provided in an embodiment of the present invention; Figure 14 This is a schematic diagram of the accumulation result of objective 1 provided in an embodiment of the present invention; Figure 15 This is a schematic diagram of the accumulation result of objective 2 provided in an embodiment of the present invention; Figure 16 This is a schematic diagram of the accumulation result of objective 3 provided in an embodiment of the present invention. Detailed Implementation

[0010] 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.

[0011] This invention proposes a broadband distributed radar target echo coherent accumulation algorithm based on Dual-Mode Synergistic Optimizer (DMSO) to solve the problem of difficulty in coherent accumulation between broadband distributed radar target echo channels caused by dispersive phase and Doppler frequency differences between different channels, thereby improving the subsequent target detection performance.

[0012] Please see Figure 1 , Figure 1 This is a flowchart of a broadband distributed radar target echo coherent accumulation method based on DMSO provided in an embodiment of the present invention. The broadband distributed radar target echo coherent accumulation method based on DMSO provided by the present invention includes: S101, Obtain the... The target echo signal of the receiving station, and the first The target echo signal from the receiving station is processed by pulse compression to obtain the first... The pulse compression result of the target echo signal of each receiving station.

[0013] Specifically, in this embodiment, please refer to Figure 2 , Figure 2 This is a schematic diagram of a broadband distributed radar operating scenario provided in an embodiment of the present invention. The distributed transmission adopts... There are 10 launch stations, which are linearly distributed and spaced apart by 1 / 2. The receiving station has a total of Because a co-location system is used for detection, the number of receiving stations and transmitting stations is the same, i.e. If a rectangular coordinate system is established with the first launching station as the origin, then the... The coordinates of each launch station are The target's coordinates are The target's speed is , target and first The radial distance between the launch stations is The pitch angle is azimuth angle is To reduce the power spectral density of the transmitted signal, it is necessary to increase the total bandwidth of the transmitted signal. Each transmitting station transmits broadband signals in different frequency bands, and the transmitted waveform uses a linear frequency modulated signal. Then, the... The transmitted signal from each transmitting station can be represented as: ; ; in, Indicates time, Indicates the transmit pulse width. This indicates the modulation frequency of a linear frequency modulated signal. Indicates signal bandwidth. Indicates the first The center frequency of the transmitted signal from each transmitting station express The minimum carrier frequency of each transmitting station.

[0014] No. The target echo signal of the receiving station The expression is: ; ; ; in, Indicates the index of the launching station. Indicates the number of launch stations. This represents the path propagation loss of the signal. Indicates the first The complex scattering cross section of a target when a launch station conducts target detection. Indicates its amplitude, Indicates its phase, Indicates the propagation delay. Indicates noise signal, Indicates the Doppler frequency. This represents the radial velocity of the target relative to the broadband distributed radar. Indicates the first The center frequency of the transmitted signal from each transmitting station Indicates signal bandwidth. express The minimum carrier frequency of each transmitting station, Represents the speed of light. Indicates the first The transmission signals from each transmitting station Indicates time, Indicates the transmit pulse width. This indicates the modulation frequency of a linear frequency modulated signal. Represents the gate function. It represents the imaginary unit.

[0015] In the case of the Before the target echo signal from each receiving station undergoes pulse compression processing, it includes: For ease of processing, a carrier frequency is used. , for the The target echo signals from each receiving station are mixed to obtain the first mixing result. , is represented as: ; It should be noted that, for ease of representation, the noise term has been ignored in the above formula. .

[0016] Because each transmitting station transmits linear frequency modulated (LFM) signals in different frequency bands, each receiving station receives echo signals with aliased frequencies. Before further processing, it is necessary to... Different transmitted signals are separated using different filters, and each separated signal is considered as a channel, resulting in a total of [number missing] separated signals. One channel signal, The first mixing result is processed using a filter to separate the... The channel signal, of which the first The first receiving station separated the first Each channel signal is represented as: ; in, Indicates the first The receiving station separated the first The path propagation loss of each channel signal. Indicates the first The target complex scattering cross section corresponding to each channel signal Indicates its amplitude, Indicates its phase, Indicates the first The first receiving station Target echo delay of each channel signal Indicates the first The first receiving station The Doppler frequencies corresponding to each channel signal Indicates the target and the first Radial velocity of each receiving station; Using carrier frequency , for the The first receiving station separated the first The channel signals are mixed to obtain the second mixing result. , is represented as: .

[0017] In this embodiment, for the first The target echo signal from each receiving station undergoes pulse compression processing, including: The impulse response function of the matched filter, which is a mirror image function of the input signal, is used to process the second mixing result to obtain the third... The pulse compression result of the target echo signal of each receiving station is expressed as follows: .

[0018] As can be seen from the above formula, since the carrier frequency of the transmitted signal from each transmitting station and the center frequency of each channel are different, that is... Differences exist, leading to variations in Doppler frequencies across different channels. There are also differences. Furthermore, due to the long distances between the transmitting and receiving stations of a distributed radar system, the echo delay... Significant differences also exist. To facilitate subsequent processing, the echo delay differences between different receiving stations can be compensated through multi-station synchronization to overcome the aforementioned shortcomings.

[0019] S102. By synchronizing the target echo signals of multiple receiving stations, the synchronization pulse compression result of multiple receiving stations is obtained.

[0020] Specifically, in this embodiment, the compression result of the synchronization pulses of multiple receiving stations is expressed as follows: ;

[0021] in, This represents the initial radial distance between the target and the reference broadband distributed radar. Represents the radial velocity of the target relative to a broadband distributed radar reference. Indicates the target relative to the first Radial velocity of each receiving station.

[0022] As can be seen from the above equation, the inter-channel dispersive phase term Doppler frequency term Differences in the chromaticity can lead to difficulties in coherent accumulation between channels. Furthermore, during long-term accumulation, the radial motion of the target relative to the radar within the same channel can cause the peak value of the sinc function to be located in different range cells, resulting in range migration. Therefore, it is necessary to adjust the dispersive phase... And the target's velocity (radial velocity). Optimization is performed to correct range migration within the channel and compensate for velocity-related phase differences between channels, thereby achieving fully coherent accumulation of the target echo.

[0023] S103. Some parameters from the synchronization pulse compression results of multiple receiving stations are used as parameters to be optimized, and a preset dual-mode collaborative optimization algorithm is used to optimize them to obtain the parameter optimization results.

[0024] Specifically, this embodiment proposes a dual-modal cooperative optimization algorithm, which applies the population classification idea from the sparrow search algorithm, dividing the population into three categories: discoverers, followers, and scouts. By enabling cooperation between individuals of different types, the algorithm's global search capability is enhanced. Figure 3 As shown, Figure 3 This is a schematic diagram of a dual-modal collaborative optimization algorithm model provided in an embodiment of the present invention.

[0025] In this embodiment, as Figure 4 As shown, Figure 4 This is a flowchart of a bimodal cooperative optimization algorithm provided in an embodiment of the present invention. The preset construction process of the bimodal cooperative optimization algorithm includes: S1. Initialize the maximum number of iterations, the function to be optimized, the population size, the proportion of discoverers, the proportion of scouts, the safety value, the step size control parameters, the inertia weight, the local acceleration coefficient, and the global acceleration coefficient, and set the position and initial velocity of the individuals.

[0026] S2. Calculate the fitness value of all individuals in the population, sort all individuals according to the size of the fitness value, divide the population into discoverers, followers and scouts, and record the optimal position of the individual and the optimal position of the population.

[0027] S3. Update the velocity of all individuals. The expression for velocity update is: ; in, Indicates inertia weight, Indicates the local acceleration coefficient. Represents the global acceleration coefficient. express Random numbers between express Random numbers between Indicates the first The optimal position for each individual Indicates the optimal position of the population. Indicates the first The individual in the first Dimensional speed, Indicates the current iteration number. Indicates the first The individual in the first The position of the dimension.

[0028] S4. Update the discoverer's location. The expression for updating the discoverer's location is: ; in, Represents a matrix of random numbers that follows a normal distribution. This represents a matrix where all elements are 1. Indicates the warning value. This indicates a safe value.

[0029] The discoverer's early warning of the environment is mainly determined by the relationship between warning values ​​and safety values. Safety value By adjusting the safety value This allows adjusting the probability of the discoverer leaving, thereby controlling the duration of the population's search in a certain area. When the warning value... Less than the safe value When the area is deemed safe, the finder continues foraging; when the warning value is reached... Greater than or equal to the safety value At that moment, the area was deemed dangerous, and the discoverer moved to another random location. Warning value. The primary method involves generating random numbers to ensure that discoverers do not over-search the same area. Simultaneously, when a scout issues an early warning signal, indicating that the group is converging prematurely, [the process will proceed]. Setting it to 1 forces all discoverers to search other areas.

[0030] By introducing the velocity update concept of particle swarm optimization to update the position of the discoverer, the directionality of the discoverer's movement is enhanced, allowing the discoverer to search in regions with high fitness more quickly. At the same time, due to the velocity update and the updates of the individual optimal and population optimal positions, after the discoverer jumps out of the original region to search in other regions, even if the new region has poor fitness, the discoverer can return to the original region to search after several iterations because the population optimal and individual optimal positions remain unchanged, which greatly improves the local search capability of the algorithm.

[0031] S5. Update the position of the follower. The expression for updating the follower's position is: ; in, This indicates the optimal position of the discoverer in the current iteration. , This represents a matrix whose elements are randomly set to 1 or -1. To represent the transpose of a matrix, This indicates the worst position in the current global population.

[0032] Followers will approach the discoverer and search for food around the discoverer. As can be seen from the formula for follower position updates above, follower position updates also fall into two categories: when... When the density of individuals around the discoverer is considered low, followers move closer to the discoverer's optimal position, searching around the discoverer to find a better position, thus ensuring the algorithm's local search capability; when If the density around the discoverer is too high, other followers will move to other locations to search other areas to prevent the population from overcrowding, thus ensuring the algorithm's global search capability.

[0033] S6. Update the scout's position. The expression for updating the scout's position is: ; in, This indicates the current optimal position of the global population. This indicates the step size control parameter. Represents a random number. Indicates the first The fitness value of each individual This represents the fitness value of the worst individual in the current global dataset. This represents the fitness value of the currently globally optimal individual. This represents the minimum value, used to ensure that the denominator is not zero.

[0034] The individuals with the lowest fitness in the population are designated as scouts, typically comprising 10% to 20%. Scouts are responsible for detecting potential dangers within the population. When a scout detects danger, it signals to disperse the population. The formula for updating scout positions shows two scenarios: when a scout's fitness value is not equal to the population's optimal fitness value, it indicates that the current iteration has not yet converged, and the population needs to continue searching for the best position. In this case, the scout moves towards the population's optimal position to increase its fitness. When a scout's fitness value equals the population's optimal fitness value, it indicates that the iteration has fully converged. If the iteration has not yet reached the maximum number of iterations, it means the iteration converged prematurely, and the scout issues a warning signal, deeming the environment dangerous. At this point, the scout moves to another position and simultaneously sends a signal to control other discoverers to move to other positions, preventing premature convergence from leading to a local optimum.

[0035] S7. After updating the positions of all individuals in the population, update the fitness values ​​of all individuals in the population, reorder them, reclassify discoverers, followers and scouts, and update the optimal positions of individuals, the optimal positions of the population and the optimal values ​​of the population.

[0036] S8. Continue this iteration until the maximum number of iterations is reached, or the optimal value of the population meets the preset requirements, to obtain the optimal value and optimal position of the population.

[0037] The aforementioned dual-modal collaborative optimization algorithm enables a dynamic role division mechanism among discoverers, followers, and scouts, achieving a balanced optimization of global exploration and local development.

[0038] This invention proposes a dual-modal collaborative optimization algorithm. By introducing the velocity update mechanism of the particle swarm optimization algorithm and the population partitioning mechanism of the sparrow search algorithm, the stability of the algorithm is enhanced, and it has better search performance in high-dimensional solution space.

[0039] In this embodiment, in order to achieve fully coherent accumulation of broadband distributed radar echoes, it is necessary to... The dispersive phase of each channel and The radial velocity of each receiving station is searched, and then the optimal solution is used for Doppler term compensation, dispersion phase compensation, and range migration correction, ultimately achieving coherent accumulation of the target echo. For example... Figure 5 As shown, Figure 5 This is a flowchart of searching for the optimal parameters provided in an embodiment of the present invention.

[0040] Some parameters from the synchronization pulse compression results of multiple receiving stations are used as parameters to be optimized. A preset dual-mode collaborative optimization algorithm is used for optimization to obtain the parameter optimization results, including: S1. Based on the synchronization pulse compression results from multiple receiving stations, Dispersion phase of each channel ,as well as Radial velocity of each receiving station Set the parameters to be optimized; simultaneously, initialize the parameters of the preset bimodal collaborative optimization algorithm; such as... Figure 6 As shown, Figure 6 This is a flowchart of an algorithm initialization provided in an embodiment of the present invention.

[0041] S2. When using the preset bimodal collaborative optimization algorithm for iterative search, during the current iterative search process, the positions of all individuals are updated, and the fitness value of each individual is calculated based on the updated positions. The expression is: ; in, This represents the current compensated signal obtained after applying Doppler frequency compensation, dispersion phase compensation, and range migration correction to the synchronization pulse compression results of the multiple receiving stations based on the current parameter optimization results obtained from the current iterative search process. This means taking the absolute value of each element of the vector. This indicates taking the maximum value.

[0042] It should be noted that the calculation of individual fitness is a function for evaluating the optimization effect, and different fitness functions have a significant impact on the optimization effect. For the broadband distributed radar accumulation problem, a peak value needs to be accumulated in the pulse compression result to indicate that the target exists in that range cell, and the maximum value of the accumulated result is used as the individual fitness.

[0043] During accumulation, distance migration correction within the channel is required. Since the target speed is slow and the pulse accumulation time is short, it can be assumed that the target's distance migration within a coherent processing time is only caused by the target speed, ignoring the distance migration caused by the small acceleration metric.

[0044] S3. Repeat this process until the maximum number of iterations is reached, or the optimal value of the population meets the preset requirements, to obtain the parameter optimization result, such as... Figure 7 As shown, Figure 7 This is a flowchart of an algorithm initialization provided in an embodiment of the present invention; wherein, the preset requirement is that the fitness value of an individual is maximized, and its expression is: ; in, This represents the optimal dispersive phase. This represents the optimal radial velocity.

[0045] It should be noted that during the search process, when the frequency dispersion phase term and Doppler frequency term of migration correction and compensation deviate from the actual situation, the individual fitness will be lower than the ideal value. However, when the frequency dispersion phase term and Doppler frequency term of migration correction and compensation are consistent with the ideal situation, the individual fitness will reach the maximum value.

[0046] Furthermore, Doppler frequency compensation, dispersion phase compensation, and range migration correction are performed on the synchronization pulse compression results from multiple receiving stations, including: Get the Distance migration of each pulse relative to the first pulse , is represented as: ; in, Indicates the radar pulse repetition time interval. This represents the search parameters related to the target speed, i.e., the specific values ​​used when searching for speed. Indicates pulse index; Using the distance migration momentum For distance migration correction, the signal after distance migration correction is represented as: ; in, Represents the imaginary unit; The Doppler frequency compensation term is obtained by using the relationship between radial velocity and Doppler frequency, and is expressed as: ; in, Indicates speed The corresponding Doppler frequency, Indicates the radar wavelength; Using the dispersion phase and the Doppler frequency compensation term from the current parameter optimization results, Doppler frequency compensation and dispersion phase compensation are performed. Combined with the signal after range migration correction, the currently compensated signal is obtained, expressed as: ; in, Indicates the first The receiving station separated the first The path propagation loss of each channel signal. This represents the initial radial distance between the target and the reference broadband distributed radar. Indicates amplitude. Indicates time, Indicates the transmit pulse width. It represents the speed of light.

[0047] S104. Using the parameter optimization results, perform Doppler frequency compensation, dispersion phase compensation, and distance migration correction on the synchronization pulse compression results of multiple receiving stations to obtain the compensated signal.

[0048] Specifically, in this embodiment, after obtaining the parameter optimization results, Doppler frequency compensation, dispersion phase compensation, and distance migration correction are performed to obtain the compensated signal.

[0049] S105. Perform coherent accumulation on the compensated signal and detect whether a target exists. If a target exists, obtain the target information and display the target.

[0050] Specifically, this embodiment also includes: Remove target information from the synchronous pulse compression results of multiple receiving stations; A pre-defined dual-mode collaborative optimization algorithm is used to optimize some parameters in the compression results of synchronization pulses from multiple receiving stations after removing target information, thereby obtaining the optimized result of another parameter. The results of another parameter optimization are used to perform Doppler frequency compensation, dispersion phase compensation and range migration correction on the compression results of multiple receiving station synchronization pulses after removing target information, so as to obtain another compensated signal; The coherent accumulation of the other compensated signal is performed, and the presence of a target is detected. If a target is found, the information of the other target is obtained and the other target is displayed. Remove another target's information from the multiple receiver station synchronization pulse compression results after removing the target information, until all targets in the multiple receiver station synchronization pulse compression results have been detected.

[0051] Please see Figure 8 , Figure 8 This is a flowchart of an accumulation detection method provided in this embodiment of the invention. After coherent accumulation processing is completed, signal detection is required to determine whether a target echo exists in the accumulation result. If no target is detected, the processing ends, and the target echo is processed for the next coherent processing time. If a target is detected, the target information is processed, and the target is displayed on the P-display after data processing. Since multiple targets may exist in a single detection, and the dispersion phase and velocity of multiple targets are different, only one target can be searched in one iteration. To achieve multi-target detection, after a target is detected, the range cell where the target is located can be removed from the echo, and the search can be repeated to detect other possible targets until all targets in the echo are detected.

[0052] In summary, this invention provides a broadband distributed radar target echo coherent accumulation method based on DMSO. Addressing the shortcomings of traditional optimization algorithms that easily get trapped in local optima or lack convergence in complex high-dimensional searches, this invention proposes a dual-mode collaborative optimization algorithm tailored to the practical application scenarios of broadband distributed radar. By introducing the velocity update mechanism of particle swarm optimization and the population partitioning mechanism of sparrow search algorithm, the stability of the algorithm is enhanced, resulting in better search performance in high-dimensional solution spaces. Using this optimization algorithm for broadband distributed radar echo accumulation can achieve a higher signal-to-noise ratio compared to traditional optimization algorithms.

[0053] In an optional embodiment of the present invention, the effectiveness of the broadband distributed radar target echo coherent accumulation method based on DMSO provided in the above embodiment is verified by simulation experiments, specifically as follows: 1. Performance test of the dual-modal collaborative optimization algorithm.

[0054] This study compares and analyzes the optimization performance of the bimodal collaborative optimization algorithm with particle swarm optimization and sparrow search algorithms on standard test functions. Six standard test functions are selected, and their definitions are shown in Table 1.

[0055] Table 1 Definition of Standard Test Functions

[0056] It should be noted that function 1 represents the Quartic function, function 2 represents the Step function, function 3 represents the Schwefel problem, function 4 represents the Non-continuous Rastrigion function, function 5 represents the Ackley function, and function 6 represents the Weierstrass function.

[0057] The standard test functions described above include three unimodal functions and three multimodal functions. To maintain consistency with radar experimental scenarios, the solutions to the test functions have a total of 68 dimensions. The Step function and the Ackley function are selected as representatives of the unimodal and multimodal functions, respectively, and their graphs are shown below. Figure 9a~9b As shown, Figure 9a This is a schematic diagram of the Step function as a unimodal function provided in an embodiment of the present invention. Figure 9b This is a schematic diagram of the Ackley function as a multimodal function provided in an embodiment of the present invention.

[0058] As can be seen from the function graphs in 9a and 9b, the single-peaked function represented by the Step function has only one local minimum point, and the function as a whole is relatively simple. There are no other extreme points around the minimum value, making the minimum value search relatively easy. However, the Ackley function has a large number of local minima in its domain. These local minima are distributed around the global minimum, forming a complex multi-peaked structure. This makes it easy for the algorithm to get stuck in these local minima during the search process, making it difficult to find the global minimum.

[0059] During testing, to simulate the performance of the algorithm proposed in this invention when solving complex real-world problems and to verify its fast convergence capability, the maximum number of iterations was set to 200, and the population size was set to 100, which is much smaller than the test parameters in other experiments. To avoid the influence of randomness on the experimental results, all algorithms were run independently for 50 computations.

[0060] The three algorithms were each subjected to 50 search operations on six test functions. The results are shown in Table 2, where MEAN represents the average of the 50 search results and SD represents the variance of the 50 search results. arrive These represent the six test functions in step 1.

[0061] Table 2 Standard test results for the three algorithms

[0062] The experimental results show that, overall, the DMSO algorithm outperforms the other two algorithms in both mean and variance. Looking at different functions, for relatively simple unimodal functions, both Particle Swarm Optimization (PSO) and Sparrow Search Algorithm (SSA) achieve good optimization results. However, for more complex multimodal functions, the search performance of both algorithms declines significantly. Furthermore, for relatively simple test functions... SSA, with its superior global search capability, can complete the search in fewer iterations and yield better results; however, for more complex test functions... and SSA fails to converge in a limited number of iterations, performing worse than the PSO algorithm. In contrast, the DMSO algorithm, with its balanced global and local search capabilities, achieves better test results across all test functions.

[0063] 2. Simulation of coherent accumulation of target echoes from broadband distributed radar.

[0064] Four radar stations were set up, sharing both transmitting and receiving capabilities. The radar stations were linearly distributed. A coordinate system was established with the first receiving station as the origin; the coordinates of the four radar stations were (0, 0, 0), (1, 0, 0), (2, 0, 0), and (3, 0, 0) km, respectively. The target's coordinates were (70, 0, 10) km, and its velocity was (50, 0, 0) m / s. The distances of each station relative to the target were calculated to be 70.71 km, 69.72 km, 68.73 km, and 67.74 km, respectively, and the radial velocities of each station were 49.658 m / s, 49.649 m / s, 49.641 m / s, and 49.632 m / s, respectively.

[0065] Each transmitting station has 16 array elements, each with a gain of 17dB, and the array gain of each transmitting station is 29dB. To achieve wide bandwidth transmission, signals from different frequency bands transmitted by each array element are combined to form a broadband signal. The transmit power of a single array element is 14.5dBmW. The pulse width of the transmitted signal is 244µs, and the pulse repetition period is 735µs. The bandwidth of a single array element signal is 16MHz, and the combined bandwidth of a single station is 256MHz. With four stations transmitting signals from different frequency bands, the total signal bandwidth is 1GHz. In digital channelization, 64 channels are divided, each with a bandwidth of 16MHz and a sampling rate of 20MHz. The CPI time is 1s, accumulating a total of 1360 pulses.

[0066] After range migration correction, the peak positions of each pulse in the multi-pulse are located within the same range cell. After Doppler frequency compensation, they can be directly added together to complete the coherent accumulation between the multi-pulses. Then, dispersive phase compensation between channels is performed to complete the fully coherent accumulation. The accumulation result is as follows: Figure 10a~10b As shown, Figure 10a This is a schematic diagram of the coherent accumulation result provided in an embodiment of the present invention. Figure 10b This is a schematic diagram of an incoherent accumulation result provided in an embodiment of the present invention.

[0067] Compared to incoherent accumulation, coherent accumulation is performed on 64 channels, and the theoretical improvement of coherent accumulation is 9dB. Figure 10a and 10b The coherent accumulation results improved the accuracy by approximately 8.3 dB compared to the incoherent accumulation results, which is close to the theoretical value. After searching using the DMSO algorithm, the radial velocities of the four receiving stations were 49.416 m / s, 49.4178 m / s, 49.4254 m / s, and 49.45 m / s, which are also close to the theoretical values. The coherent accumulation results showed that the distance between the target and the master station was approximately 70.717 km, consistent with the theoretical value.

[0068] To verify the effectiveness of the proposed dual-modal cooperative optimization algorithm in broadband distributed radar coherent accumulation, a sparrow search algorithm and a particle swarm optimization algorithm were used to search for dispersive phase and velocity, with the maximum number of iterations and the number of populations remaining consistent with the dual-modal cooperative optimization algorithm. After multiple simulation experiments, it was found that the sparrow search algorithm, due to its weak local convergence ability, failed to converge during phase search and therefore could not perform coherent accumulation.

[0069] The accumulation results of the PSO and DMSO algorithms were tested by CA-CFAR as follows: Figure 11 and Figure 12 As shown, the fitness iteration curves of the two are as follows: Figure 13 As shown, Figure 11 This is a schematic diagram of the CFAR detection result of the DMSO algorithm provided in an embodiment of the present invention. Figure 12 This is a schematic diagram of the CFAR detection result of the PSO algorithm provided in an embodiment of the present invention. Figure 13 This is a schematic diagram of the fitness iteration curve of the algorithm provided in an embodiment of the present invention. Figure 13 The upper curve is the fitness curve of DMSO, and the lower curve is the fitness change curve of particle swarm optimization algorithm.

[0070] The accumulated results of the two algorithms show that the DMSO algorithm exceeds the threshold by approximately 4.5 dB, while the PSO algorithm exceeds it by approximately 1.5 dB. In terms of accumulation performance, the DMSO algorithm improves upon the PSO algorithm by approximately 3 dB. The fitness iteration curves of the two algorithms show that PSO converges at 350 iterations, exhibiting premature convergence and getting trapped in a local optimum. In contrast, the fitness of the DMSO algorithm continuously improves during iteration, resulting in a higher convergence fitness and better overall performance. Furthermore, a closer look at the DMSO algorithm's fitness convergence curve reveals that the DMSO algorithm continuously performs random jump operations during iteration, frequently searching other regions and ensuring that the fitness continuously improves during iteration, ultimately achieving a better convergence result.

[0071] To test the algorithm's ability to accumulate signals from multiple targets, a simulation was conducted to demonstrate the coherent accumulation of echoes from three different targets. The target positions and velocities are shown in Table 3, and the remaining parameters are consistent with those described above.

[0072] Table 3 Multi-objective parameters

[0073] After iterative searching and target elimination, the coherent accumulation results obtained are as follows: Figure 14~Figure 16 As shown, Figure 14 This is a schematic diagram of the accumulation result of objective 1 provided in an embodiment of the present invention. Figure 15 This is a schematic diagram of the accumulation result of objective 2 provided in an embodiment of the present invention. Figure 16 This is a schematic diagram of the accumulation results of target 3 provided in an embodiment of the present invention. The radial velocity of the target relative to the main station obtained during the search process is shown in Table 4.

[0074] Table 4 Speed ​​Search Results

[0075] Based on the pulse compression results and velocity search results, the algorithm accurately measured the distance and radial velocity of the three targets through three iterations. Furthermore, the accumulated results from targets 2 and 3 show that, for targets with similar distances but different velocities, the algorithm can still distinguish them based on their phase and velocity differences and accurately measure their parameters.

[0076] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that an article or device comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device comprising said element. Terms such as "connected" or "linked" are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect. The orientations or positional relationships indicated by terms such as "upper," "lower," "left," and "right" are based on the orientations or positional relationships shown in the accompanying drawings and are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0077] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is 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.

[0078] 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 concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A broadband distributed radar target echo coherent accumulation method based on DMSO, characterized in that, include: Get the The target echo signal of the receiving station, and the first... The target echo signal from the receiving station is processed by pulse compression to obtain the first... Pulse compression results of the target echo signal from each receiving station; By synchronizing the target echo signals from multiple receiving stations, the synchronization pulse compression result of multiple receiving stations is obtained. Some parameters from the synchronization pulse compression results of the multiple receiving stations are used as parameters to be optimized, and a preset dual-mode collaborative optimization algorithm is used to optimize them to obtain the parameter optimization results. The parameter optimization results are used to perform Doppler frequency compensation, dispersion phase compensation, and range migration correction on the synchronization pulse compression results of the multiple receiving stations to obtain the compensated signal. The compensated signal is coherently accumulated, and the presence of a target is detected. If a target is found, the target information is obtained and the target is displayed.

2. The broadband distributed radar target echo coherent accumulation method based on DMSO according to claim 1, characterized in that, The first The target echo signal of the receiving station The expression is: ； ； ； in, Indicates the index of the launching station. Indicates the number of launch stations. This represents the path propagation loss of the signal. Indicates the first The complex scattering cross section of a target when a launch station conducts target detection. Indicates its amplitude, Indicates its phase, Indicates the propagation delay. Indicates noise signal, Indicates the Doppler frequency. This represents the radial velocity of the target relative to the broadband distributed radar. Indicates the first The center frequency of the transmitted signal from each transmitting station Indicates signal bandwidth. express The minimum carrier frequency of each transmitting station, Represents the speed of light. Indicates the first The transmission signals from each transmitting station Indicates time, Indicates the transmit pulse width. This indicates the modulation frequency of a linear frequency modulated signal. Represents the gate function. It represents the imaginary unit.

3. The broadband distributed radar target echo coherent accumulation method based on DMSO according to claim 2, characterized in that, In the case of the first Before the target echo signal from each receiving station undergoes pulse compression processing, it includes: Using carrier frequency For the first The target echo signals from each receiving station are mixed to obtain the first mixing result. , is represented as: ； The first mixing result is processed using a filter to separate the... The channel signal, of which the first The first receiving station separated the first Each channel signal is represented as: ； in, Indicates the first The receiving station separated the first The path propagation loss of each channel signal. Indicates the first The target complex scattering cross section corresponding to each channel signal Indicates its amplitude, Indicates its phase, Indicates the first The first receiving station Target echo delay of each channel signal Indicates the first The first receiving station The Doppler frequencies corresponding to each channel signal Indicates the target and the first Radial velocity of each receiving station; Using carrier frequency For the first The first receiving station separated the first The channel signals are mixed to obtain the second mixing result. , is represented as: 。 4. The broadband distributed radar target echo coherent accumulation method based on DMSO according to claim 3, characterized in that, For the first The target echo signal from each receiving station undergoes pulse compression processing, including: The impulse response function of a matched filter is used to process the second mixing result to obtain the third... The pulse compression result of the target echo signal of each receiving station is expressed as follows: 。 5. The broadband distributed radar target echo coherent accumulation method based on DMSO according to claim 4, characterized in that, The result of the synchronization pulse compression of the multiple receiving stations is expressed as follows: ； in, This represents the initial radial distance between the target and the reference broadband distributed radar. Represents the radial velocity of the target relative to a broadband distributed radar reference. Indicates the target relative to the first Radial velocity of each receiving station.

6. The broadband distributed radar target echo coherent accumulation method based on DMSO according to claim 1, characterized in that, The construction process of the preset dual-modal collaborative optimization algorithm includes: Initialize the maximum number of iterations, the function to be optimized, the population size, the proportion of discoverers, the proportion of scouts, the safety value, the step size control parameters, the inertia weight, the local acceleration coefficient, and the global acceleration coefficient, and set the position and initial velocity of the individuals; Calculate the fitness value of all individuals in the population, sort all individuals according to the size of the fitness value, divide the population into discoverers, followers and scouts, and record the optimal position of the individuals and the optimal position of the population. Update the velocity of all individuals; the expression for velocity update is: ； in, Indicates inertia weight, Indicates the local acceleration coefficient. Represents the global acceleration coefficient. express Random numbers between express Random numbers between Indicates the first The optimal position for each individual Indicates the optimal position of the population. Indicates the first The individual in the first Dimensional speed, Indicates the current iteration number. Indicates the first The individual in the first The position of the dimension; Update the discoverer's location; the expression for updating the discoverer's location is: ； in, Represents a matrix of random numbers that follows a normal distribution. This represents a matrix where all elements are 1. Indicates the warning value. Indicates a safe value; Update the position of the follower; the expression for updating the follower's position is: ； in, This indicates the optimal position of the discoverer in the current iteration. , This represents a matrix whose elements are randomly set to 1 or -1. To represent the transpose of a matrix, This indicates the worst position in the current global population. Update the scout's position; the expression for updating the scout's position is: ； in, This indicates the current optimal position of the global population. This indicates the step size control parameter. Represents a random number. Indicates the first The fitness value of each individual This represents the fitness value of the worst individual in the current global dataset. This represents the fitness value of the currently globally optimal individual. This represents the minimum value, used to ensure that the denominator is not zero; After updating the positions of all individuals in the population, update the fitness values ​​of all individuals in the population, reorder them, reclassify discoverers, followers and scouts, and update the optimal positions of individuals, the optimal positions of the population and the optimal values ​​of the population. This process is repeated until the maximum number of iterations is reached, or the optimal value of the population meets the preset requirements, thus obtaining the optimal value and optimal position of the population.

7. The broadband distributed radar target echo coherent accumulation method based on DMSO according to claim 1, characterized in that, A subset of parameters from the synchronization pulse compression results of the multiple receiving stations are used as parameters to be optimized. A preset dual-modal collaborative optimization algorithm is employed to optimize these parameters, yielding the optimized parameters, including: Based on the synchronization pulse compression results of the multiple receiving stations, Dispersion phase of each channel ,as well as Radial velocity of each receiving station Set as parameters to be optimized; When using a pre-defined bimodal collaborative optimization algorithm for iterative search, the positions of all individuals are updated during the current iteration. Based on the updated positions, the fitness value of each individual is calculated, expressed as: ； in, This represents the current compensated signal obtained after applying Doppler frequency compensation, dispersion phase compensation, and range migration correction to the synchronization pulse compression results of the multiple receiving stations based on the current parameter optimization results obtained from the current iterative search process. This means taking the absolute value of each element of the vector. This indicates taking the maximum value; This process is iterated until the maximum number of iterations is reached, or the optimal value of the population meets a preset requirement, to obtain the parameter optimization result; wherein, the preset requirement is that the fitness value of an individual is maximized, and its expression is: ； in, This represents the optimal dispersive phase. This represents the optimal radial velocity.

8. The broadband distributed radar target echo coherent accumulation method based on DMSO according to claim 7, characterized in that, The compression results of the synchronization pulses from the multiple receiving stations are subjected to Doppler frequency compensation, dispersion phase compensation, and range migration correction, including: Get the Distance migration of each pulse relative to the first pulse , is represented as: ； in, Indicates the radar pulse repetition time interval. Indicates the search parameters related to the target speed. Indicates pulse index; Using the distance migration momentum For distance migration correction, the signal after distance migration correction is represented as: ； in, Represents the imaginary unit; The Doppler frequency compensation term is obtained by using the relationship between radial velocity and Doppler frequency, and is expressed as: ； in, Indicates speed The corresponding Doppler frequency, Indicates the radar wavelength; Using the dispersion phase and the Doppler frequency compensation term from the current parameter optimization results, Doppler frequency compensation and dispersion phase compensation are performed. Combined with the signal after range migration correction, the currently compensated signal is obtained, expressed as: ； in, Indicates the first The receiving station separated the first The path propagation loss of each channel signal. This represents the initial radial distance between the target and the reference broadband distributed radar. Indicates amplitude. Indicates time, Indicates the transmit pulse width. It represents the speed of light.

9. The broadband distributed radar target echo coherent accumulation method based on DMSO according to claim 1, characterized in that, Also includes: The target information is removed from the synchronization pulse compression results of the multiple receiving stations. The preset dual-modal collaborative optimization algorithm is used to optimize some parameters in the compression results of the synchronization pulses of the multiple receiving stations after removing the target information, so as to obtain the optimization result of another parameter. The optimization results of the other parameter are used to perform Doppler frequency compensation, dispersion phase compensation, and range migration correction on the compression results of the synchronization pulses of the multiple receiving stations after removing the target information, to obtain another compensated signal; The other compensated signal is coherently accumulated, and the presence of a target is detected. If a target is found, the other target information is obtained and the other target is displayed. The other target information is removed from the multiple receiving station synchronization pulse compression results after removing the target information, until all targets in the multiple receiving station synchronization pulse compression results are detected.

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