Receiving and transmitting combined anti-spectrum dispersion interference method and device based on Riemannian optimization

Abstract

The invention discloses a receiving and transmitting combined spectrum dispersion interference resisting method and device based on Riemannian optimization, and the method comprises the steps: taking the integration level of a minimum spectrum dispersion interference component and the integration sidelobe level of a target component as an optimization target, and considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matched filtering peak loss constraint; establishing a joint constraint optimization model for resisting spectrum dispersion interference; establishing a Riemannian optimization model for optimizing a radar transmitted waveform and a Riemannian optimization model for optimizing a receiving filter so as to realize synchronous updating of the radar transmitted waveform and the receiving filter; the method comprises the following steps: initializing a radar emission waveform, initializing a receiving filter, updating the radar emission waveform and the receiving filter by using a meta optimizer based on a forward pass algorithm, updating meta optimizer model parameters based on a backward pass algorithm until convergence, and outputting the current radar emission waveform and the current receiving filter. According to the method, the effective suppression of the SMSP can be realized, and the convergence rate is higher.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of radar anti-active interference, and in particular to a method and device for jointly anti-spectral dispersion interference of transmission and reception based on Riemann element optimization. Background Art

[0002] Smeared Spectrum (SMSP) is a type of intra-pulse active coherent jammer. In actual operation, the SMSP jammer first intercepts the radar transmission waveform of the enemy radar system, generates interference components after downsampling and replicating the intercepted signal, and then forwards the generated interference components outward. Since the SMSP component has a strong correlation with the radar transmission waveform, the interference component will generate a large number of false targets after matched filtering at the receiving end, thereby affecting the detection of real targets.

[0003] In response to the SMSP suppression problem, domestic and foreign scholars have conducted a lot of research and achieved certain results. From the perspective of processing mechanism, these methods can be mainly divided into two types: one is the passive interference suppression method, and the other is the active interference suppression method. The passive interference suppression method mainly reconstructs the interference signal based on the estimation of the interference parameters, and then uses the cancellation strategy to eliminate the impact of the false target on the real target. The active interference suppression method means that before the interference signal enters the receiver, the radar system adaptively adjusts its own transmission and reception parameters according to the environmental parameters, actively reduces the system's response to the interference signal, and tries to avoid the interference energy from entering the radar receiver. Summary of the invention

[0004] Aiming at the joint design problem of radar transmitting waveform and receiving filter under the anti-SMSP background, the limited anti-SMSP performance of receiving-end adaptive signal processing and transmitting-end waveform optimization methods, and the low operating efficiency of classical iterative algorithms, the present invention proposes a transmit-receive joint anti-spectral dispersion interference method and device based on Riemann element optimization, specifically a radar transmitting waveform and receiving filter solution method based on Riemann element optimization, which realizes the simultaneous updating of radar transmitting waveform and receiving filter through meta-learning method, and achieves better waveform convergence performance and interference suppression performance.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows: On the one hand, the present invention provides a method for jointly transmitting and receiving to resist spectrum dispersion interference based on Riemann element optimization, comprising: Taking minimizing the integral level of the spectrum dispersion interference component and the integral sidelobe level of the target component as the optimization goal, while considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matching filter peak loss constraint, a joint constrained optimization model for resisting spectrum dispersion interference is established. Establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; Initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward transfer algorithm, update the meta-optimizer model parameters based on the backward transfer algorithm until convergence, and output the current radar transmit waveform and receive filter.

[0006] On the other hand, the present invention provides a Riemann element optimization-based transmit-receive joint anti-spectral dispersion interference device, comprising: The first module is used to establish a joint constraint optimization model for resisting spectral dispersion interference by taking the minimization of the integral level of the spectral dispersion interference component and the integral sidelobe level of the target component as the optimization target, while considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matching filter peak loss constraint; The second module is used to establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; The third module is used to initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward transfer algorithm, update the meta-optimizer model parameters based on the backward transfer algorithm until convergence, and output the current radar transmit waveform and receive filter.

[0007] In another aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Taking minimizing the integral level of the spectrum dispersion interference component and the integral sidelobe level of the target component as the optimization goal, while considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matching filter peak loss constraint, a joint constrained optimization model for resisting spectrum dispersion interference is established. Establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; Initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward transfer algorithm, update the meta-optimizer model parameters based on the backward transfer algorithm until convergence, and output the current radar transmit waveform and receive filter.

[0008] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps: Taking minimizing the integral level of the spectrum dispersion interference component and the integral sidelobe level of the target component as the optimization goal, while considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matching filter peak loss constraint, a joint constrained optimization model for resisting spectrum dispersion interference is established. Establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; Initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward transfer algorithm, update the meta-optimizer model parameters based on the backward transfer algorithm until convergence, and output the current radar transmit waveform and receive filter.

[0009] In order to further improve the degree of freedom of active anti-interference and enhance the interference suppression performance, the present invention proposes a method and device for jointly anti-spectral dispersion interference of transmission and reception based on Riemann element optimization.

[0010] Under the background of SMSP suppression, the present invention aims to minimize the integral level of the interference component and the integral sidelobe level of the target component, and considers minimizing the peak loss of non-matching filtering, the constant modulus constraint of the waveform, and the energy constraint of the receiving filter, so as to establish a joint constrained optimization model for resisting spectrum dispersion interference; a joint optimization model solving method based on Riemann element optimization is proposed, which is used to realize the synchronous update of the radar transmission waveform and the receiving filter until convergence, and output the current radar transmission waveform and the non-matching filter used by the radar receiving end.

[0011] Compared with the prior art, the technical effects of the present invention are as follows: First, the optimization degree of freedom is high. The traditional anti-SMSP method only uses one degree of freedom of the transmitting end or the receiving end, while the present invention adopts a method of jointly optimizing the radar transmitting waveform and the receiving filter to improve the waveform anti-interference performance and algorithm operation efficiency.

[0012] Second, the simultaneous iteration of the radar transmission waveform and the receiving filter is realized. For the joint optimization problem, the classic solution is to adopt an alternating optimization strategy, that is, fix one variable and optimize the other variable, and alternately optimize until convergence. To address this problem, the present invention introduces a meta-learning strategy and uses the geometric characteristics of constant modulus constraints and energy constraints to establish a Riemann meta-optimization model, which realizes the simultaneous iteration of the radar transmission waveform and the receiving filter and the adaptive update of the optimizer.

[0013] Third, the anti-SMSP performance is good. The present invention proposes a Riemann element optimization method for the joint optimization of radar transmission waveform and receiving filter to achieve effective suppression of SMSP. Compared with the classical method, the method proposed in the present invention has a faster convergence rate and better anti-SMSP performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0015] Figure 1 An embodiment provides a flow chart of a method for jointly resisting spectrum dispersion interference by transmitting and receiving based on Riemann element optimization; Figure 2 It is the matched filtering result diagram of the initialized random waveform under SMSP interference conditions, where (a) is the matched filtering result diagram of waveform channel 1, and (b) is the matched filtering result diagram of waveform channel 2; Figure 3 It is a non-matched filtering result diagram output by the Riemann element optimization method when SMSP interference occurs, wherein (a) is a matched filtering result diagram of waveform channel 1 after Riemann element optimization based on the solution provided by the present invention, and (b) is a matched filtering result diagram of waveform channel 2 after Riemann element optimization based on the solution provided by the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Reference Figure 1 In one embodiment, a method for jointly preventing spectrum dispersion interference by transmitting and receiving based on Riemann element optimization is provided, including: Taking minimizing the integral level of the spectrum dispersion interference component and the integral sidelobe level of the target component as the optimization goal, while considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matching filter peak loss constraint, a joint constrained optimization model for resisting spectrum dispersion interference is established. Establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; Initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward transfer algorithm, update the meta-optimizer model parameters based on the backward transfer algorithm until convergence, and output the current radar transmit waveform and receive filter.

[0018] In one embodiment, the established joint constraint optimization model for resisting spectral dispersion interference is as follows: ;in The dimension is Radar transmit waveform and receive filter matrix, is the radar transmission waveform and the number of sampling points in the filter time domain, Indicates the number of radar channels; , They represent the radar transmit waveform and receive filter of the mth channel respectively; , Respectively represent Radar transmit waveform and receive filter for each channel; represents the Fourier transform matrix, and It is a preset parameter. Its value range is usually related to the waveform dimension. The general value is , ; , and express , , Corresponding The zero-padding result of the dimension; express The nth element; , , They represent the modulo operation, the vector two-norm, and the Hadamard product, respectively. represents the conjugate transpose, represents complex conjugate; represents the waveform constant modulus constraint; represents the filter constant energy constraint. represents the spectral dispersion interference component, and express Corresponding The zero-fill result of dimension, where represents the shift matrix of spectral dispersive interference, and Expressed as ; in, represents the sub-pulse length of the spectral dispersive interference, is the number of sub-pulses.

[0019] Establish a Riemann element optimization model for radar transmission waveform optimization, including: make Indicates the number of network updates. Represents the number of network layers, then in the Radar transmission waveform in layer network Updated to: ; in, represents the Riemann meta-optimization model, represents the shrinkage operator of the radar transmit waveform, express At the point The Riemann gradient of express middle The value is , The value is The objective function value of , Indicates Radar transmission waveform in the layer network.

[0020] Due to the waveform constant modulus constraint, the radar transmission waveform variable is always in the Riemann complex circular manifold. Relative to The Riemann gradient for: ; in, represents the real part operation, represents complex conjugate.

[0021] A Riemann element optimization model for receive filter optimization is established, including: In the When the network is updated, The receiving filter of the layer network is updated as: ; in, represents the Riemann meta-optimization model, express Relative to The Riemann gradient of Indicates The receiving filter of the layer network, represents the shrinkage operator of the receive filter, express middle The value is The objective function value when .

[0022] Due to the constant energy constraint, the receiving filter variable is always on the Riemann complex oblique manifold. for: ; in, It means to sum the columns of the matrix. represents the real part operation, Indicates that the input column vector is complex valued to form a new two-dimensional matrix, express The complex conjugate of .

[0023] In one embodiment of the present invention, a radar transmission waveform and a receiving filter are updated using a meta-optimizer based on a forward transfer algorithm, including: In the Update network parameters, During the forward transmission process, the radar transmission waveform and the receiving filter can be updated successively as ; ; in, and The LSTM network is used as the basic unit to update the radar transmission waveform and the meta-optimizer of the receiving filter. and Indicates network The model parameters of the meta-optimizer at the time of update, and Indicates the status of each unit, and Indicates the update amount of the radar transmit waveform and receive filter.

[0024] Update the meta-optimizer model parameters based on the backward pass algorithm, including: ; ; in, and Representation Meta-Optimizer and The learning rate, express Adam Optimizer, and Respectively Relative to model parameters and gradient.

[0025] Initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward propagation algorithm, update the meta-optimizer model parameters based on the backward propagation algorithm until convergence, and output the current radar transmit waveform and receive filter. The setting method of the iterative convergence condition described in the present invention is not limited, and those skilled in the art can set it based on the usual means or common knowledge in the art. In a preferred embodiment, the maximum number of iterations can be set as the convergence condition, or the loss function calculated in the current and subsequent iterations can be used as the convergence condition. When the difference is less than the set threshold (the threshold is set according to experience, such as 0.001), the iteration stops and the radar transmission waveform and the receiving filter are output.

[0026] The effect of the combined transmit-receive anti-spectral dispersion interference method based on Riemann element optimization provided by the present invention is further illustrated by the following numerical simulation experiment: Experimental scenario: The following experiments were conducted on a computer (core 2.30GHz i7-12700H, RAM 40.0GB), using Pytorch version 2.2.2. During the experiment, a two-channel radar system was considered, the radar waveform time domain width was 20u, the sampling rate was 50MHz, and the jammer downsampled the radar waveform Set to 4, the sub-pulse width of the interference component is 250, and the preset parameter values ​​are set to , For the meta-optimizer, it is set to a two-layer LSTM network with 500 hidden units in each layer, and the learning rate of the Adam optimizer is set to 0.0001. The loss function is output between the current and the last two iterations When the error is less than 0.0001, the algorithm stops running and outputs the optimized radar transmit waveform and receive filter.

[0027] Experimental steps: (1) Problem model: Taking minimizing the integral level of the spectral dispersion interference component and the integral sidelobe level of the target component as the optimization objective, while considering the waveform constant modulus constraint, the receiving filter energy constraint, and the non-matching filter peak loss constraint, a joint constrained optimization model for resisting spectral dispersion interference is established. Initialize the radar transmit waveform and the receiving filter.

[0028] (2) Establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; (3) Using the meta-optimizer based on the forward propagation algorithm to update the radar transmit waveform and receive filter, and using the meta-optimizer model parameters based on the backward propagation algorithm; (4) Repeat step (3) until convergence condition is reached, stop iteration and output the current radar transmit waveform and receive filter.

[0029] The implementation method of each of the above steps can adopt the method described in any of the above embodiments, which will not be repeated here.

[0030] Figure 2 The figure is the matched filtering result of the initialized random waveform under SMSP interference conditions, where (a) is the matched filtering result of waveform channel 1, and (b) is the matched filtering result of waveform channel 2. The initialized random waveform has a constant amplitude of 1 and a phase value of Due to the effect of SMSP, a group of false targets appeared after matched filtering, and the peak heights of the false targets in the two channels were -2.82dB and -6.41dB respectively, which affected the target detection.

[0031] Figure 3 It is a non-matched filtering result diagram output by the Riemann element optimization method when SMSP interference occurs, wherein (a) is a matched filtering result diagram of waveform channel 1 after Riemann element optimization based on the solution provided by the present invention, and (b) is a matched filtering result diagram of waveform channel 2 after Riemann element optimization based on the solution provided by the present invention. In the result diagram, only two peaks appear at the target position, and the highest sidelobe values ​​in the two channels are -14.81dB and -13.33dB respectively, which effectively suppresses SMSP interference.

[0032] The experimental results show that the present invention can achieve effective suppression of SMSP and has greater application value in radar cognitive active interference suppression.

[0033] In one embodiment, a Riemann element optimization-based transmitting and receiving joint anti-spectral dispersion interference device is provided, comprising: The first module is used to establish a joint constraint optimization model for resisting spectral dispersion interference by taking the minimization of the integral level of the spectral dispersion interference component and the integral sidelobe level of the target component as the optimization target, while considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matching filter peak loss constraint; The second module is used to establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; The third module is used to initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward transfer algorithm, update the meta-optimizer model parameters based on the backward transfer algorithm until convergence, and output the current radar transmit waveform and receive filter.

[0034] The implementation methods of the above modules and the construction of the model can adopt the methods described in any of the above embodiments, which will not be repeated here.

[0035] On the other hand, the present invention provides a computer device, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the Riemann element optimization-based transmit-receive joint anti-spectral dispersion interference method provided in any of the above embodiments when executing the computer program. The computer device may be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store sample data. The network interface of the computer device is used to communicate with an external terminal via a network connection.

[0036] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the Riemann element optimization-based transmit-receive joint anti-spectral dispersion interference method provided in any of the above embodiments are implemented.

[0037] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0038] Matters not covered by the present invention are known technologies.

[0039] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0040] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for joint transmission and reception anti-spectral dispersion interference based on Riemann element optimization, characterized in that: include: Taking minimizing the integral level of the spectrum dispersion interference component and the integral sidelobe level of the target component as the optimization goal, while considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matching filter peak loss constraint, a joint constrained optimization model for resisting spectrum dispersion interference is established. Establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; Initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward transfer algorithm, update the meta-optimizer model parameters based on the backward transfer algorithm until convergence, and output the current radar transmit waveform and receive filter.

2. The method for jointly resisting spectrum dispersion interference of transmission and reception based on Riemann element optimization according to claim 1 is characterized in that: The established joint constrained optimization model for anti-spectral dispersion interference is as follows: ； in The dimension is Radar transmit waveform and receive filter matrix, is the radar transmission waveform and the number of time domain sampling points of the receiving filter, Indicates the number of radar channels; , They represent the radar transmit waveform and receive filter of the mth channel respectively; , Respectively represent Radar transmit waveform and receive filter for each channel; represents the Fourier transform matrix, and is the preset parameter; , and express , , Corresponding The zero-padding result of the dimension; express The nth element; , , They represent the modulo operation, the vector two-norm, and the Hadamard product, respectively. represents the conjugate transpose, represents complex conjugate; represents the waveform constant modulus constraint; represents the filter constant energy constraint; represents the spectral dispersion interference component, and express Corresponding The zero-fill result of dimension, where represents the shift matrix of spectral dispersive interference, and Expressed as in, represents the sub-pulse length of the spectral dispersive interference, is the number of sub-pulses.

3. The method for jointly resisting spectrum dispersion interference of transmission and reception based on Riemann element optimization according to claim 2 is characterized in that: Establish a Riemann element optimization model for radar transmission waveform optimization, including: make Indicates the number of network updates. Represents the number of network layers, then in the Radar transmission waveform in layer network Updated to: ； in, represents the Riemann meta-optimization model, represents the shrinkage operator of the radar transmit waveform, express At the point The Riemann gradient of express middle The value is , The value is The objective function value of , Indicates Radar transmission waveform in the layer network.

4. The method for jointly resisting spectrum dispersion interference of transmission and reception based on Riemann element optimization according to claim 3 is characterized in that: Due to the waveform constant modulus constraint, the radar transmission waveform variable is always in the Riemann complex circular manifold. Relative to The Riemann gradient for: ； in, represents the real part operation, represents complex conjugate.

5. The method for jointly resisting spectrum dispersion interference by transmitting and receiving based on Riemann element optimization according to claim 3 or 4, characterized in that: A Riemann element optimization model for receive filter optimization is established, including: In the When the network is updated, The receiving filter of the layer network is updated as: ； in, represents the Riemann meta-optimization model, express Relative to The Riemann gradient of Indicates The receiving filter of the layer network, represents the shrinkage operator of the receive filter, express middle The value is The objective function value when .

6. The method for jointly resisting spectrum dispersion interference of transmission and reception based on Riemann element optimization according to claim 5 is characterized in that: Due to the constant energy constraint, the receiving filter variable is always on the Riemann complex oblique manifold. for: ； in, It means to sum the columns of the matrix. represents the real part operation, Indicates that the input column vector is complex valued to form a new two-dimensional matrix, express The complex conjugate of .

7. The method for jointly resisting spectrum dispersion interference of transmission and reception based on Riemann element optimization according to claim 5 is characterized in that: The radar transmit waveform and receive filter are updated using a meta-optimizer based on a forward pass algorithm, including: In the Update network parameters, During the forward transmission process, the radar transmission waveform and the receiving filter can be updated successively as ； ； in, and The LSTM network is used as the basic unit to update the radar transmission waveform and the meta-optimizer of the receiving filter. and Indicates network The model parameters of the meta-optimizer at the time of update, and Indicates the status of each unit, and Indicates the update amount of the radar transmit waveform and receive filter.

8. The method for jointly resisting spectrum dispersion interference of transmission and reception based on Riemann element optimization according to claim 7 is characterized in that: Update the meta-optimizer model parameters based on the backward pass algorithm, including: ； ； in, and Representation Meta-Optimizer and The learning rate, express Adam Optimizer, and Respectively Relative to model parameters and gradient.

9. A transmit-receive joint anti-spectral dispersion interference device based on Riemann element optimization, characterized in that: include: The first module is used to establish a joint constraint optimization model for resisting spectral dispersion interference by taking the minimization of the integral level of the spectral dispersion interference component and the integral sidelobe level of the target component as the optimization target, while considering the waveform constant modulus constraint, the receiving filter energy constraint and the non-matching filter peak loss constraint; The second module is used to establish a Riemann element optimization model for radar transmit waveform optimization and a Riemann element optimization model for receive filter optimization to achieve synchronous update of radar transmit waveform and receive filter; The third module is used to initialize the radar transmit waveform, initialize the receive filter, update the radar transmit waveform and receive filter using the meta-optimizer based on the forward transfer algorithm, update the meta-optimizer model parameters based on the backward transfer algorithm until convergence, and output the current radar transmit waveform and receive filter.

10. The Riemann element optimization-based transmitting and receiving joint anti-spectral dispersion interference device according to claim 9, characterized in that: In the first module, the joint constraint optimization model for anti-spectral dispersion interference is established as follows: ； in The dimension is Radar transmit waveform and receive filter matrix, is the radar transmission waveform and the number of sampling points in the filter time domain, Indicates the number of radar channels; , They represent the radar transmit waveform and receive filter of the mth channel respectively; , Respectively represent Radar transmit waveform and receive filter for each channel; represents the Fourier transform matrix, and is the preset parameter, , ; , and express , , Corresponding The zero-padding result of the dimension; express The nth element; , , They represent the modulo operation, the vector two-norm, and the Hadamard product, respectively. represents the conjugate transpose, represents complex conjugate; represents the waveform constant modulus constraint; represents the filter constant energy constraint; represents the spectral dispersion interference component, and express Corresponding The zero-fill result of dimension, where represents the shift matrix of spectral dispersive interference, and Expressed as ； in, represents the sub-pulse length of the spectral dispersive interference, is the number of sub-pulses.

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