Hypersonic platform radar target detection method and device and electronic equipment

Abstract

The application provides a hypersonic platform radar target detection method, device and electronic equipment, relates to the technical field of hypersonic radar target detection, and the method comprises the following steps: inputting a received first echo signal into a time domain echo signal model, converting the first echo signal from a first signal domain to a second signal domain by using the time domain echo signal model, and obtaining a second echo signal; filtering the second echo signal by using an optimal parameter filter, obtaining a filtered third echo signal, converting the third echo signal into a third signal domain, and obtaining a coherent accumulation result; determining whether a target is detected by using a constant false alarm rate detection based on the coherent accumulation result, and determining the distance of the target based on the position of a peak value in the accumulation result in the case that it is determined that the target is detected. The hypersonic platform radar target detection method, device and electronic equipment provided by the application can realize high-precision, high-efficiency and highly adaptable radar target detection.

Description

Technical Field

[0001] This application relates to the field of hypersonic radar target detection technology, and in particular to a hypersonic platform radar target detection method, device and electronic equipment. Background Technology

[0002] Hypersonic vehicles are characterized by high speed, high maneuverability, and long range, resulting in complex and variable characteristics in the radar echoes received. During the pulse compression phase, the matched peak is prone to shift and energy attenuation, leading to performance loss. During the multi-pulse accumulation phase, the target echo crosses multiple range and Doppler cells, resulting in range migration and Doppler migration, making it difficult to focus the energy.

[0003] Therefore, there is an urgent need for an echo detection method that can perform high-precision modeling of radar target echoes from hypersonic platforms, and eliminate pulse compression mismatch and correct migration through corresponding algorithms to improve detection performance. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, and electronic equipment for detecting radar targets on a hypersonic platform, which can achieve high-precision, high-efficiency, and highly adaptable radar target detection.

[0005] This application provides a method for detecting radar targets on a hypersonic platform, including:

[0006] The received first echo signal is input into a time-domain echo signal model. The time-domain echo signal model is used to convert the first echo signal from a first signal domain to a second signal domain, resulting in a second echo signal. An optimal parameter filter is used to filter the second echo signal, resulting in a filtered third echo signal. This third echo signal is then converted to a third signal domain, yielding a coherent accumulation result. Based on the coherent accumulation result, constant false alarm rate (CFAR) detection is used to determine whether a target is detected. If a target is detected, the target's distance is determined based on the position of the peak value in the coherent accumulation result. The first signal domain is a fast-time-slow-time domain; the second signal domain is a range-frequency-slow-time domain; and the third signal domain is a fast-time-Doppler domain. The optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration.

[0007] Optionally, the time-domain echo signal model is constructed based on the following steps: constructing a set of signal propagation history equations based on the motion state information of the radar platform and the target object; the motion state information includes position, velocity, and acceleration vector; the set of signal propagation history equations characterizes the relationship between: the arrival of the radar-transmitted signal at the target, the return of the target-reflected signal to the radar receiver, and the instantaneous distance and the corresponding signal propagation delay; solving and simplifying the set of signal propagation history equations yields a total propagation delay expression with fast and slow times as variables; the total propagation delay expression characterizes the relative motion between the radar and the target; and generating the time-domain echo signal model based on the total propagation delay expression and the linear frequency modulated signal transmitted by the radar.

[0008] Optionally, the optimal parameter filter is obtained based on the following steps: determining initial first-order and initial second-order coefficients, and constructing a pulse compression filter and a migration correction filter based on the initial first-order and initial second-order coefficients; merging the pulse compression filter and the migration correction filter, and using a genetic algorithm to estimate the parameters of the first-order and second-order coefficients to obtain the optimal first-order and optimal second-order coefficients; and incorporating the optimal first-order and optimal second-order coefficients into the merged filter to obtain the optimal parameter filter.

[0009] Optionally, the step of using a genetic algorithm to estimate the parameters of the initial first-order and second-order coefficients to obtain the optimal first-order and second-order coefficients includes: initializing a population and constructing a filter corresponding to each individual; each individual in the population includes: a first-order coefficient and a second-order coefficient; calculating the fitness of each individual, and based on the fitness of each individual, dividing the population into multiple subgroups using a segmented proportional selection strategy; the fitness is used to characterize the peak value of the input signal after filtering and transformation into the third signal domain; performing crossover and mutation on the population based on crossover and mutation strategies, and obtaining the optimal first-order and second-order coefficients after the iteration stops; wherein, the crossover strategy includes: performing linear interpolation between parents according to random weights to generate individuals with new parameters; the mutation strategy includes: adding random perturbations to the first-order and second-order coefficients.

[0010] This application also provides a hypersonic platform radar target detection device, comprising:

[0011] A signal conversion module is used to input the received first echo signal into a time-domain echo signal model, and use the time-domain echo signal model to convert the first echo signal from a first signal domain to a second signal domain, obtaining a second echo signal. A signal filtering module is used to filter the second echo signal using an optimal parameter filter to obtain a filtered third echo signal, and convert the third echo signal into a third signal domain to obtain a coherent accumulation result. A target detection module is used to determine whether a target is detected based on the coherent accumulation result through constant false alarm rate (CFAR) detection, and if a target is detected, to determine the target distance based on the position of the peak in the coherent accumulation result. The first signal domain is a fast-time-slow-time domain; the second signal domain is a range-frequency domain-slow-time domain; the third signal domain is a fast-time-Doppler domain; and the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration.

[0012] Optionally, the device further includes: a generation module; the generation module is used to construct a set of signal propagation history equations based on the motion state information of the radar platform and the target object; the motion state information includes position, velocity, and acceleration vector; the set of signal propagation history equations is used to characterize: the arrival of the radar transmitted signal at the target, the return of the target reflected signal to the radar receiver, and the relationship between instantaneous distance and corresponding signal propagation delay; the generation module is also used to solve and simplify the set of signal propagation history equations to obtain a total propagation delay expression with fast time and slow time as variables; the total propagation delay expression is used to characterize the relative motion between the radar and the target; the generation module is also used to generate the time-domain echo signal model based on the total propagation delay expression and the linear frequency modulated signal transmitted by the radar.

[0013] Optionally, the apparatus further includes: a filter construction module and a parameter estimation module; the filter construction module is used to determine initial first-order term coefficients and initial second-order term coefficients, and construct a pulse compression filter and a migration correction filter based on the initial first-order term coefficients and the initial second-order term coefficients; the parameter estimation module is used to merge the pulse compression filter and the migration correction filter, and then use a genetic algorithm to estimate the parameters of the initial first-order term coefficients and the initial second-order term coefficients to obtain optimal first-order term coefficients and optimal second-order term coefficients; the filter construction module is also used to integrate the optimal first-order term coefficients and the optimal second-order term coefficients into the merged filter to obtain the optimal parameter filter.

[0014] Optionally, the parameter estimation module is specifically used to initialize the population and construct a filter corresponding to each individual; each individual in the population includes: first-order term coefficients and second-order term coefficients; the parameter estimation module is further used to calculate the fitness of each individual, and based on the fitness of each individual, to divide the population into multiple subgroups using a segmented proportional selection strategy; the fitness is used to characterize the peak value of the input signal after filtering and transformation into the third signal domain; the parameter estimation module is further used to perform crossover and mutation on the population based on crossover and mutation strategies, and to obtain the optimal first-order term coefficients and the optimal second-order term coefficients after the iteration stops; wherein, the crossover strategy includes: performing linear interpolation between parents according to random weights to generate individuals with new parameters; the mutation strategy includes: adding random perturbations to the first-order term coefficients and second-order term coefficients.

[0015] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the hypersonic platform radar target detection method as described above.

[0016] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the hypersonic platform radar target detection methods described above.

[0017] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the hypersonic platform radar target detection method as described above.

[0018] The hypersonic platform radar target detection method, apparatus, and electronic equipment provided in this application firstly input the received first echo signal into a time-domain echo signal model, and use the time-domain echo signal model to convert the first echo signal from a first signal domain to a second signal domain, obtaining a second echo signal; then, use an optimal parameter filter to filter the second echo signal to obtain a filtered third echo signal, and convert the third echo signal into a third signal domain to obtain a coherent accumulation result; finally, based on the coherent accumulation result, determine whether a target is detected by constant false alarm rate (CFAR) detection, and if a target is detected, determine the target's range based on the position of the peak value in the coherent accumulation result; wherein, the first signal domain is: fast time-slow time domain; the second signal domain is: range frequency domain-slow time domain; the third signal domain is: fast time-Doppler domain; and the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration. Thus, to address the problem of insufficient modeling accuracy, a new echo model is proposed, introducing two-way propagation asymmetry to improve the accuracy of characterizing target echo characteristics; to address the difficulty of migration correction, migration is eliminated through joint correction using a unified filter; and to improve energy accumulation efficiency, motion parameter estimation is performed using a parameter optimization method based on filtering correction, reducing dependence on prior information, in order to achieve high-precision, high-efficiency, and highly adaptable radar target detection. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is one of the flowcharts of the hypersonic platform radar target detection method provided in this application;

[0021] Figure 2 This is the second flowchart of the hypersonic platform radar target detection method provided in this application;

[0022] Figure 3 This is a schematic diagram of the structure of the hypersonic platform radar target detection device provided in this application;

[0023] Figure 4 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] The terms "first," "second," etc., used in this application's specification are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, in the specification, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0026] In related technologies, traditional modeling methods based on the "stop-go-stop" assumption assume that the relative motion between the platform and the target can be ignored within the duration of a single pulse. This modeling method can obtain relatively accurate results for non-hypersonic platforms, but under hypersonic platform conditions, this assumption leads to a significant increase in modeling errors. Intra-pulse motion models take into account the relative motion between the radar and the target during the pulse duration, but these models ignore the relative motion between the radar and the target during electromagnetic wave transmission and reception, making it impossible for the model to fully describe the radar-target echo characteristics of hypersonic platforms.

[0027] For the distance migration and Doppler migration problems, existing technical solutions treat distance migration and Doppler migration independently, failing to consider the consistency of the source, resulting in performance losses in migration correction and energy accumulation. Furthermore, most of the aforementioned migration correction algorithms are based on a "stop-go-stop" model, which cannot cope with the performance loss caused by pulse compression mismatch.

[0028] To address the aforementioned technical problems in related technologies, this application provides a hypersonic platform radar target detection method. This method addresses the issue of insufficient modeling accuracy by proposing a new echo model and introducing two-way propagation asymmetry to improve the accuracy of characterizing target echo characteristics. To address the difficulty of migration correction, it no longer processes range migration and Doppler migration separately, but instead uses a unified filter for joint correction to eliminate migration. To improve energy accumulation efficiency, based on filtering correction, a parameter optimization method is used to estimate motion parameters, reducing reliance on prior information.

[0029] like Figure 1The diagram shown is a schematic representation of the overall process of the hypersonic platform radar target detection method provided in this application embodiment. First, a time-domain echo signal model of the hypersonic platform-borne radar is modeled (establishing a set of equations representing the transmission and reception processes of the radar and the target; by solving and simplifying the motion characteristic equations, a multi-pulse time-domain echo signal based on the radar's transmitted signal is obtained). Then, the signal is transformed to the range-frequency domain-slow time domain. Initial first-order and second-order coefficients are set, and a pulse compression filter and a migration correction filter are constructed. A genetic algorithm is used to estimate and solve the motion parameters. Finally, a filter is constructed based on the optimal parameters, and energy accumulation and constant false alarm rate (CFAR) detection are performed.

[0030] The hypersonic platform radar target detection method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0031] like Figure 2 As shown in the embodiment of this application, a hypersonic platform radar target detection method is provided, which may include the following steps 201 to 203:

[0032] Step 201: Input the received first echo signal into the time-domain echo signal model, and use the time-domain echo signal model to convert the first echo signal from the first signal domain to the second signal domain to obtain the second echo signal.

[0033] The first signal domain is a fast-slow time domain; the second signal domain is a distance-frequency domain-slow time domain.

[0034] For example, the first echo signal is a multi-pulse time-domain echo signal emitted by the radar and reflected by the target. The time-domain echo signal model is used to convert the echo signal from the first signal domain to the second signal domain.

[0035] Specifically, prior to step 201 above, the hypersonic platform radar target detection method provided in this application embodiment may further include steps 301 to 303:

[0036] Step 301: Based on the motion state information of the radar platform and the target object, construct a set of signal propagation history equations. These equations characterize the relationship between: the arrival of the radar-transmitted signal at the target, the return of the target-reflected signal to the radar receiver, and the instantaneous distance and the corresponding signal propagation delay.

[0037] The motion state information includes position, velocity, and acceleration vector.

[0038] Step 302: Solve and simplify the equations of the signal propagation process to obtain the total propagation delay expression with fast time and slow time as variables.

[0039] The total propagation delay expression is used to characterize the relative motion between the radar and the target.

[0040] Step 303: Based on the total propagation delay expression and the linear frequency modulated signal transmitted by the radar, generate the time-domain echo signal model.

[0041] For example, assume that the radar's initial position, velocity, and acceleration vector are respectively , and The target's initial position, velocity, and acceleration vectors are respectively , and Fast and slow times are respectively used , The launch and reception processes are represented by [specific symbols / methods]. , This indicates that the launch process delay and total delay are respectively expressed as... , express, If we represent the speed of light, then the equations governing the transmission and reception processes of radar and target can be expressed by the following formulas one through four:

[0042] (Formula 1)

[0043] (Formula 2)

[0044] (Formula 3)

[0045] (Formula 4)

[0046] Solving and simplifying this system of equations yields the equations of motion characteristics, which can be expressed using the following formula:

[0047] (Formula 5)

[0048] in, , This represents a dot product. Assuming the radar transmits a linear frequency modulated signal, the constructed time-domain echo signal model is represented by the following formula:

[0049] (Formula 6)

[0050] in, Represents a rectangular window function. Indicates the scaling factor. Indicates the pulse duration. This indicates the historical distance of the target within the observation period. Indicates the carrier frequency. This indicates the frequency modulation, and c is the speed of light.

[0051] For example, after obtaining the above time-domain echo model, the multi-pulse time-domain echo signal can be transformed from the fast time-slow time domain to the distance-frequency domain-slow time domain using this model. The transformed expression can be represented by the following formula seven:

[0052] (Formula 7)

[0053] in, Represents bandwidth, coefficients of the first-order term coefficients of the second-order term .

[0054] Step 202: Filter the second echo signal using an optimal parameter filter to obtain a filtered third echo signal, and convert the third echo signal into a third signal domain to obtain the coherent accumulation result.

[0055] The third signal domain is the fast time-Doppler domain; the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration.

[0056] For example, in this application embodiment, in order to address the problem of reduced energy accumulation efficiency and limited detection performance caused by separate processing of pulse compression mismatch and two-dimensional migration correction, a unified processing approach is proposed. A pulse compression filter and a migration correction filter containing target motion parameters are designed to achieve joint correction of distance and Doppler migration while completing full pulse compression, thereby avoiding the energy loss and performance degradation caused by traditional step-by-step processing.

[0057] Specifically, prior to step 202 above, the hypersonic platform radar target detection method provided in this application embodiment may further include steps 304 to 306:

[0058] Step 304: Determine the initial first-order term coefficients and the initial second-order term coefficients, and construct the pulse compression filter and the migration correction filter based on the initial first-order term coefficients and the initial second-order term coefficients.

[0059] Step 305: After merging the pulse compression filter and the migration correction filter, use a genetic algorithm to estimate the parameters of the first-order term coefficients and the second-order term coefficients to obtain the optimal first-order term coefficients and the optimal second-order term coefficients.

[0060] Step 306: Integrate the optimal first-order term coefficients and the optimal second-order term coefficients into the merged filter to obtain the optimal parameter filter.

[0061] For example, to achieve pulse compression, the initial first-order term coefficients are set. The pulse compression filter can be constructed using the following formula:

[0062] (Formula 8)

[0063] in, To correct for migration, initial first-order term coefficients are set. and initial second-order term coefficients The constructed migration correction filter can be expressed by the following formula:

[0064] (Formula 9)

[0065] The pulse compression filter and migration correction filter can then be combined, which can be expressed by the following formula:

[0066] (Formula 10)

[0067] For example, after merging the pulse compression filter and migration correction filter described above, a genetic algorithm can be used to estimate the parameters of the first-order and second-order terms, thereby obtaining the optimal parameter filter.

[0068] For example, after step 306 above, the hypersonic platform radar target detection method provided in this application embodiment may further include the following steps 307 to 309:

[0069] Step 307: Initialize the population and construct the filter corresponding to each individual.

[0070] Each individual in the population includes: a first-order term coefficient and a second-order term coefficient.

[0071] Step 308: Calculate the fitness of each individual, and based on the fitness of each individual, divide the population into multiple subgroups using a segmented proportional selection strategy.

[0072] The fitness is used to characterize the peak value of the input signal after it has been filtered and transformed into the third signal domain.

[0073] Step 309: Perform crossover and mutation on the population based on the crossover and mutation strategies, and obtain the optimal first-order term coefficients and the optimal second-order term coefficients after the iteration stops.

[0074] The crossover strategy includes: performing linear interpolation between parents according to random weights to generate individuals with new parameters; the mutation strategy includes: adding random perturbations to the coefficients of the first-order and second-order terms.

[0075] For example, the steps for solving the optimal parameters in this application embodiment are as follows:

[0076] S1. Initialize the population, each individual contains a set of first-order and second-order term coefficients. .

[0077] S2. Generate filters: Generate corresponding filters based on each individual parameter. .

[0078] S3. Calculate the fitness of each individual. Fitness is defined as the peak value of the input data after it has been filtered and transformed to the fast time-Doppler domain. Specifically, it can be expressed by the following formula eleven:

[0079] (Formula Eleven)

[0080] in, FFT For Fast Fourier Transform, IFFT This is the inverse fast Fourier transform.

[0081] S4. Selection: Using a segmented proportional selection strategy, individuals are divided into multiple subgroups according to their fitness from high to low. The subgroup with higher fitness has a higher selection probability.

[0082] S5. Crossover: An arithmetic crossover strategy is used to linearly interpolate between the parents with random weights to generate individuals with new parameters.

[0083] S6. Mutation: A random perturbation mutation operation is used to add random perturbations to the coefficients of the first-order and second-order terms.

[0084] S7. Determine whether to stop the iteration. If the optimal fitness does not improve significantly after several consecutive iterations, stop the iteration and output the optimal parameters. Otherwise, return to S2.

[0085] For example, after obtaining the optimal parameters, the optimal parameter filter constructed using these optimal parameters is expressed by the following formula 12:

[0086] (Formula 12)

[0087] in, These are the optimal first-order term coefficients and the optimal second-order term coefficients, respectively; Indicates bandwidth. j Represents the imaginary unit. For distance frequency variables, Indicates the carrier frequency; Indicates the scaling factor; Indicates a fast time. Indicates slow time. Indicates frequency modulation.

[0088] For example, after obtaining the above-mentioned optimal parameter filter, the second echo signal can be jointly filtered using the optimal parameter filter.

[0089] Step 203: Based on the coherent accumulation result, determine whether a target is detected by constant false alarm rate (CFAR) detection, and if the target is detected, determine the distance to the target based on the position of the peak value in the coherent accumulation result.

[0090] For example, when the condition is met At this point, the pulse compression mismatch can be completely eliminated, and the migration can be completely corrected. The input signal and the filter output can then be expressed by the following formula (Equation 13):

[0091] (Formula Thirteen)

[0092] For example, the output of the above optimal filter is transformed into the fast-time-Doppler domain, that is, by performing an inverse Fourier transform along the range spectrum dimension and then a Fourier transform along the slow-time dimension. The coherent accumulation result can be expressed by the following formula fourteen:

[0093] (Formula Fourteen)

[0094] in, R represents the accumulation time, and R is the distance variable.

[0095] For example, the distance estimation result can be obtained based on the position of the peak of the coherent result, and then constant false alarm detection is performed on the target. When the peak value is higher than the threshold value, the target is detected; otherwise, the target is not detected.

[0096] The hypersonic platform radar target detection method provided in this application considers both the relative motion between the radar and the target during the pulse duration and the two-way relative motion during electromagnetic wave transmission and reception in its echo model, eliminating the significant modeling errors caused by existing models neglecting two-way propagation asymmetry. From the perspective of unified pulse compression and migration correction, the method of full pulse compression and joint correction of two-dimensional migration avoids the energy accumulation efficiency reduction and detection performance loss caused by traditional step-by-step processing. Regarding parameter estimation accuracy and robustness, the genetic algorithm performs global optimization estimation of motion parameters, overcoming the limitation of some existing methods relying on precise prior parameters, improving parameter estimation accuracy, and increasing detection stability under low signal-to-noise ratio conditions. From the perspective of computational complexity and applicability, this method reduces computational load while maintaining detection performance. Compared with existing methods involving multiple iterations or multi-dimensional searches, computational efficiency is significantly improved, making it more suitable for the real-time processing needs of hypersonic platforms under different speeds and maneuvering conditions.

[0097] The hypersonic platform radar target detection method provided in this application firstly inputs the received first echo signal into a time-domain echo signal model, and uses the time-domain echo signal model to convert the first echo signal from a first signal domain to a second signal domain, obtaining a second echo signal; then, it uses an optimal parameter filter to filter the second echo signal, obtaining a filtered third echo signal, and converts the third echo signal into a third signal domain, obtaining a coherent accumulation result; finally, based on the coherent accumulation result, it determines whether a target is detected by constant false alarm rate (CFAR) detection, and if a target is detected, it determines the target's distance based on the position of the peak value in the coherent accumulation result; wherein, the first signal domain is: fast time-slow time domain; the second signal domain is: range frequency domain-slow time domain; the third signal domain is: fast time-Doppler domain; and the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration. Thus, to address the problem of insufficient modeling accuracy, a new echo model is proposed, introducing two-way propagation asymmetry to improve the accuracy of characterizing target echo characteristics; to address the difficulty of migration correction, migration is eliminated through joint correction using a unified filter; and to improve energy accumulation efficiency, motion parameter estimation is performed using a parameter optimization method based on filtering correction, reducing dependence on prior information, in order to achieve high-precision, high-efficiency, and highly adaptable radar target detection.

[0098] It should be noted that the hypersonic platform radar target detection method provided in this application embodiment can be executed by a hypersonic platform radar target detection device, or a control module within that device for executing the hypersonic platform radar target detection method. This application embodiment uses the execution of the hypersonic platform radar target detection method by a hypersonic platform radar target detection device as an example to illustrate the hypersonic platform radar target detection device provided in this application embodiment.

[0099] It should be noted that, in the embodiments of this application, the hypersonic platform radar target detection methods shown in the accompanying drawings are all illustrated by way of example with reference to one of the accompanying drawings in the embodiments of this application. In specific implementation, the hypersonic platform radar target detection methods shown in the accompanying drawings of the above methods can also be implemented in conjunction with any other accompanying drawings shown in the above embodiments, which will not be elaborated here.

[0100] The hypersonic platform radar target detection device provided in this application is described below. The hypersonic platform radar target detection method described below can be referred to in correspondence with the hypersonic platform radar target detection method described above.

[0101] Figure 3 This is a schematic diagram of the structure of the hypersonic platform radar target detection device provided in the embodiments of this application, as shown below. Figure 3 As shown, it specifically includes:

[0102] Signal conversion module 301 is used to input the received first echo signal into a time-domain echo signal model, and use the time-domain echo signal model to convert the first echo signal from a first signal domain to a second signal domain to obtain a second echo signal; signal filtering module 302 is used to filter the second echo signal using an optimal parameter filter to obtain a filtered third echo signal, and convert the third echo signal into a third signal domain to obtain a coherent accumulation result; target detection module 303 is used to determine whether a target is detected based on the coherent accumulation result through constant false alarm rate detection, and if a target is detected, to determine the target distance based on the position of the peak in the coherent accumulation result; wherein, the first signal domain is: fast time-slow time domain; the second signal domain is: range frequency domain-slow time domain; the third signal domain is: fast time-Doppler domain; the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration.

[0103] Optionally, the device further includes: a generation module; the generation module is used to construct a set of signal propagation history equations based on the motion state information of the radar platform and the target object; the motion state information includes position, velocity, and acceleration vector; the set of signal propagation history equations is used to characterize: the arrival of the radar transmitted signal at the target, the return of the target reflected signal to the radar receiver, and the relationship between instantaneous distance and corresponding signal propagation delay; the generation module is also used to solve and simplify the set of signal propagation history equations to obtain a total propagation delay expression with fast time and slow time as variables; the total propagation delay expression is used to characterize the relative motion between the radar and the target; the generation module is also used to generate the time-domain echo signal model based on the total propagation delay expression and the linear frequency modulated signal transmitted by the radar.

[0104] Optionally, the apparatus further includes: a filter construction module and a parameter estimation module; the filter construction module is used to determine initial first-order term coefficients and initial second-order term coefficients, and construct a pulse compression filter and a migration correction filter based on the initial first-order term coefficients and the initial second-order term coefficients; the parameter estimation module is used to merge the pulse compression filter and the migration correction filter, and then use a genetic algorithm to estimate the parameters of the initial first-order term coefficients and the initial second-order term coefficients to obtain optimal first-order term coefficients and optimal second-order term coefficients; the filter construction module is also used to integrate the optimal first-order term coefficients and the optimal second-order term coefficients into the merged filter to obtain the optimal parameter filter.

[0105] Optionally, the parameter estimation module is specifically used to initialize the population and construct a filter corresponding to each individual; each individual in the population includes: first-order term coefficients and second-order term coefficients; the parameter estimation module is further used to calculate the fitness of each individual, and based on the fitness of each individual, to divide the population into multiple subgroups using a segmented proportional selection strategy; the fitness is used to characterize the peak value of the input signal after filtering and transformation into the third signal domain; the parameter estimation module is further used to perform crossover and mutation on the population based on crossover and mutation strategies, and to obtain the optimal first-order term coefficients and the optimal second-order term coefficients after the iteration stops; wherein, the crossover strategy includes: performing linear interpolation between parents according to random weights to generate individuals with new parameters; the mutation strategy includes: adding random perturbations to the first-order term coefficients and second-order term coefficients.

[0106] The hypersonic platform radar target detection device provided in this application firstly inputs the received first echo signal into a time-domain echo signal model, and uses the time-domain echo signal model to convert the first echo signal from a first signal domain to a second signal domain, obtaining a second echo signal; then, it uses an optimal parameter filter to filter the second echo signal, obtaining a filtered third echo signal, and converts the third echo signal into a third signal domain, obtaining a coherent accumulation result; finally, based on the coherent accumulation result, it determines whether a target is detected through constant false alarm rate (CFAR) detection, and if a target is detected, it determines the target's range based on the position of the peak value in the coherent accumulation result; wherein, the first signal domain is: fast time-slow time domain; the second signal domain is: range-frequency domain-slow time domain; the third signal domain is: fast time-Doppler domain; and the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration. Thus, to address the problem of insufficient modeling accuracy, a new echo model is proposed, introducing two-way propagation asymmetry to improve the accuracy of characterizing target echo characteristics; to address the difficulty of migration correction, migration is eliminated through joint correction using a unified filter; and to improve energy accumulation efficiency, motion parameter estimation is performed using a parameter optimization method based on filtering correction, reducing dependence on prior information, in order to achieve high-precision, high-efficiency, and highly adaptable radar target detection.

[0107] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other through the communications bus 440. The processor 410 can call logic instructions in the memory 430 to execute a hypersonic platform radar target detection method. This method includes: first, inputting a received first echo signal into a time-domain echo signal model, and using the time-domain echo signal model to convert the first echo signal from a first signal domain to a second signal domain, obtaining a second echo signal; then, using an optimal parameter filter to filter the second echo signal, obtaining a filtered third echo signal, and converting the third echo signal into a third signal domain, obtaining a coherent accumulation result; finally, based on the coherent accumulation result, determining whether a target is detected using constant false alarm rate (CFAR) detection, and if a target is detected, determining the target's distance based on the position of the peak value in the coherent accumulation result; wherein the first signal domain is a fast-time-slow-time domain; the second signal domain is a range-frequency-slow-time domain; the third signal domain is a fast-time-Doppler domain; and the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration. Thus, to address the problem of insufficient modeling accuracy, a new echo model is proposed, introducing two-way propagation asymmetry to improve the accuracy of characterizing target echo characteristics; to address the difficulty of migration correction, migration is eliminated through joint correction using a unified filter; and to improve energy accumulation efficiency, motion parameter estimation is performed using a parameter optimization method based on filtering correction, reducing dependence on prior information, in order to achieve high-precision, high-efficiency, and highly adaptable radar target detection.

[0108] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] On the other hand, this application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the hypersonic platform radar target detection method provided by the above methods. The method includes: first, inputting a received first echo signal into a time-domain echo signal model, and using the time-domain echo signal model to convert the first echo signal from a first signal domain to a second signal domain to obtain a second echo signal; then, using an optimal parameter filter to filter the second echo signal to obtain a filtered third echo signal, and converting the third echo signal to a third signal domain to obtain a coherent accumulation result; finally, based on the coherent accumulation result, determining whether a target is detected by constant false alarm rate (CFAR) detection, and if a target is detected, determining the target's distance based on the position of the peak in the coherent accumulation result; wherein, the first signal domain is: fast time-slow time domain; the second signal domain is: range frequency domain-slow time domain; the third signal domain is: fast time-Doppler domain; and the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration. Thus, to address the problem of insufficient modeling accuracy, a new echo model is proposed, introducing two-way propagation asymmetry to improve the accuracy of characterizing target echo characteristics; to address the difficulty of migration correction, migration is eliminated through joint correction using a unified filter; and to improve energy accumulation efficiency, motion parameter estimation is performed using a parameter optimization method based on filtering correction, reducing dependence on prior information, in order to achieve high-precision, high-efficiency, and highly adaptable radar target detection.

[0110] Furthermore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the hypersonic platform radar target detection methods described above. The method includes: first, inputting a received first echo signal into a time-domain echo signal model, and using the time-domain echo signal model to convert the first echo signal from a first signal domain to a second signal domain, obtaining a second echo signal; then, using an optimal parameter filter to filter the second echo signal, obtaining a filtered third echo signal, and converting the third echo signal into a third signal domain, obtaining a coherent accumulation result; finally, based on the coherent accumulation result, determining whether a target is detected using constant false alarm rate (CFAR) detection, and if a target is detected, determining the target's distance based on the position of the peak value in the coherent accumulation result; wherein the first signal domain is a fast-time-slow-time domain; the second signal domain is a range-frequency-slow-time domain; the third signal domain is a fast-time-Doppler domain; and the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration. Thus, to address the problem of insufficient modeling accuracy, a new echo model is proposed, introducing two-way propagation asymmetry to improve the accuracy of characterizing target echo characteristics; to address the difficulty of migration correction, migration is eliminated through joint correction using a unified filter; and to improve energy accumulation efficiency, motion parameter estimation is performed using a parameter optimization method based on filtering correction, reducing dependence on prior information, in order to achieve high-precision, high-efficiency, and highly adaptable radar target detection.

[0111] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for detecting targets on a hypersonic platform radar, characterized in that, include: The received first echo signal is input into the time-domain echo signal model, and the first echo signal is converted from the first signal domain to the second signal domain using the time-domain echo signal model to obtain the second echo signal; The second echo signal is filtered using an optimal parameter filter to obtain a filtered third echo signal, which is then converted into a third signal domain to obtain a coherent accumulation result. Based on the coherent accumulation results, a constant false alarm rate (CFAR) is used to determine whether a target has been detected. If a target is detected, the distance to the target is determined based on the position of the peak value in the coherent accumulation results. Wherein, the first signal domain is: fast time-slow time domain; the second signal domain is: range frequency domain-slow time domain; the third signal domain is: fast time-Doppler domain; the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration; The time-domain echo signal model is constructed based on the following steps: Based on the motion state information of the radar platform and the target, a set of signal propagation history equations is constructed. The set of signal propagation history equations is used to characterize the relationship between the radar transmitted signal arriving at the target, the target reflected signal returning to the radar receiver, and the instantaneous distance and the corresponding signal propagation delay. The equations governing the signal propagation process are solved and simplified to obtain an expression for the total propagation delay, with fast and slow times as variables. This expression for the total propagation delay is used to characterize the relative motion between the radar and the target. The time-domain echo signal model is generated based on the total propagation delay expression and the linear frequency modulated signal transmitted by the radar.

2. The method according to claim 1, characterized in that, The motion state information includes position, velocity, and acceleration vector.

3. The method according to claim 2, characterized in that, The time-domain echo signal model is represented by the following formula: ； in, Represents a rectangular window function. Indicates the scaling factor. Indicates the pulse duration. This indicates the historical distance of the target within the observation period. Indicates the carrier frequency. Indicates frequency modulation. c The speed of light; j The imaginary unit, Indicates the relative position of the target and the radar platform. Represents relative velocity. Represents relative acceleration; Represents fast-time variables. This represents a slow-time variable.

4. The method according to any one of claims 1 to 3, characterized in that, The optimal parameter filter is obtained based on the following steps: Determine the initial first-order term coefficients and the initial second-order term coefficients, and construct a pulse compression filter and a migration correction filter based on the initial first-order term coefficients and the initial second-order term coefficients; After merging the pulse compression filter and the migration correction filter, a genetic algorithm is used to estimate the parameters of the initial first-order term coefficients and the initial second-order term coefficients to obtain the optimal first-order term coefficients and the optimal second-order term coefficients. The optimal first-order coefficients and the optimal second-order coefficients are incorporated into the merged filter to obtain the optimal parameter filter.

5. The method according to claim 4, characterized in that, The step of using a genetic algorithm to estimate the parameters of the first-order and second-order coefficients to obtain the optimal first-order and second-order coefficients includes: Initialize the population and construct the filter corresponding to each individual; each individual in the population includes: first-order term coefficients and second-order term coefficients; The fitness of each individual is calculated, and based on the fitness of each individual, the population is divided into multiple subgroups using a segmented proportional selection strategy; the fitness is used to characterize the peak value of the input signal after filtering and transformation into the third signal domain. The population is subjected to crossover and mutation strategies, and the optimal first-order term coefficients and the optimal second-order term coefficients are obtained after the iteration stops. The crossover strategy includes: performing linear interpolation between parents according to random weights to generate individuals with new parameters; the mutation strategy includes: adding random perturbations to the coefficients of the first-order and second-order terms.

6. A hypersonic platform radar target detection device, characterized in that, The device includes: The signal conversion module is used to input the received first echo signal into the time-domain echo signal model, and use the time-domain echo signal model to convert the first echo signal from the first signal domain to the second signal domain to obtain the second echo signal; The signal filtering module is used to filter the second echo signal using an optimal parameter filter to obtain a filtered third echo signal, and convert the third echo signal into a third signal domain to obtain a coherent accumulation result. The target detection module is used to determine whether a target is detected based on the coherent accumulation result through constant false alarm rate detection, and if it is determined that a target is detected, to determine the distance of the target based on the position of the peak in the coherent accumulation result; Wherein, the first signal domain is: fast time-slow time domain; the second signal domain is: range frequency domain-slow time domain; the third signal domain is: fast time-Doppler domain; the optimal parameter filter is used to perform joint correction processing on range migration and Doppler migration; The device further includes: a generation module; The generation module is used to construct a set of signal propagation history equations based on the motion state information of the radar platform and the target. The set of signal propagation history equations is used to characterize the relationship between: the radar transmitted signal arriving at the target, the target reflected signal returning to the radar receiver, and the instantaneous distance and the corresponding signal propagation delay. The generation module is also used to solve and simplify the signal propagation history equations to obtain a total propagation delay expression with fast time and slow time as variables; the total propagation delay expression is used to characterize the relative motion between the radar and the target; The generation module is also used to generate the time-domain echo signal model based on the total propagation delay expression and the linear frequency modulated signal transmitted by the radar.

7. The apparatus according to claim 6, characterized in that, The device further includes: a filter construction module and a parameter estimation module; The filter construction module is used to determine the initial first-order term coefficients and the initial second-order term coefficients, and to construct a pulse compression filter and a migration correction filter based on the initial first-order term coefficients and the initial second-order term coefficients. The parameter estimation module is used to combine the pulse compression filter and the migration correction filter, and then use a genetic algorithm to estimate the parameters of the initial first-order term coefficients and the initial second-order term coefficients to obtain the optimal first-order term coefficients and the optimal second-order term coefficients. The filter construction module is further configured to integrate the optimal first-order term coefficients and the optimal second-order term coefficients into the merged filter to obtain the optimal parameter filter.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the hypersonic platform radar target detection method as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the hypersonic platform radar target detection method as described in any one of claims 1 to 5.

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