Radar cross-ambiguity function shaping method and device based on joint design of transmission and reception

Abstract

In order to further consider the design freedom of the ambiguity function and improve the anti-interference performance in the range-doppler dimension, the application proposes a radar cross-ambiguity function shaping method and device based on joint design of transmission and reception. In the background of radar ambiguity function shaping, first, the maximum output signal-to-noise ratio is taken as the optimization criterion to establish the objective function. Then, in order to ensure that the radar nonlinear amplifier works in the maximum efficiency state and avoid the nonlinear distortion of the output waveform, a low peak-to-average power ratio constraint condition is further imposed on the designed transmit waveform, and a non-convex joint optimization problem for ambiguity function shaping is established. Finally, a model iterative solution method based on the maximum-minimization framework is proposed, which realizes the iterative solution of the transmit waveform and the receive filter. Through the iterative update of the transmit waveform and the receive filter, better anti-interference performance is realized.

Description

Technical Field

[0001] This invention mainly relates to the field of joint design technology of radar transmit waveforms and receive filters, and in particular to a radar mutual ambiguity function shaping method and device based on joint transmit and receive design. Background Technology

[0002] The radar ambiguity function is a two-dimensional function dependent on echo delay and Doppler modulation, and it has been widely used in anti-jamming performance evaluation. Most existing ambiguity function shaping methods, under the criterion of matched filtering, achieve the joint design of the transmitted waveform and the receiving filter by minimizing the power of the interference region or maximizing the output signal-to-interference-plus-noise ratio (SINR). When the radar receiver uses an arbitrary sequence as an unmatched filter, a cross-ambiguity function (CAF) corresponding to the transmitted waveform and the receiving filter can be obtained. However, existing ambiguity function shaping methods have limited degrees of freedom in radar ambiguity function shaping optimization, thus restricting anti-jamming performance. Summary of the Invention

[0003] To address the problem of limited freedom in radar ambiguity function shaping optimization and restricted anti-jamming performance under radar anti-jamming background, this invention proposes a radar mutual ambiguity function shaping method and device based on transceiver joint design in order to further consider the degree of freedom in ambiguity function design and improve the anti-jamming performance in the range-Doppler dimension.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: On the one hand, this invention provides a radar mutual ambiguity function shaping method based on transceiver joint design, comprising: Using maximizing the output signal-to-interference-plus-noise ratio (SINR) as the optimization criterion, and considering the imposition of a low peak-to-average power ratio constraint on the transmitted waveform, a joint optimization model for shaping the mutual ambiguity function of the radar transmitted waveform and the receiving filter is established as follows:

[0005] Among them, let This represents the radar transmission waveform, where Represents the radar transmitted waveform One sampling point, and These represent the distance position and the Doppler frequency, respectively. Indicates distance from location The relevant shift matrix, Represents the Doppler steering vector; for a given range Doppler cell , Indicates distance Doppler unit The response value at that location, This represents the unmatched filter at the receiving end. This indicates the conjugate transpose operation. Represents the Hadema product; The transmitted waveform To satisfy the finite low peak-to-average power ratio constraint, and to maximize the output signal-to-interference-plus-noise ratio, we have:

[0006] in This represents the transmit waveform that satisfies the finite low peak-to-average power ratio constraint. Set value; Solving the joint optimization model yields the optimal transmitted waveform and the optimal filter corresponding to the optimal transmitted waveform.

[0007] Furthermore, The value range is 1 to 1.5.

[0008] Furthermore, the solution process of the joint optimization model includes: By fixing the transmitted waveform in the joint optimization model, the joint optimization problem of the joint optimization model is transformed into a minimum variance distortionless response problem for the receiving filter, thereby obtaining the closed-form solution of the optimal receiving filter; Substituting the closed-form solution of the optimal filter into the joint optimization model yields a univariate optimization problem concerning the transmitted waveform; The optimal transmission waveform is obtained by solving the univariate optimization problem concerning the transmission waveform. Substituting the optimal transmission waveform into the closed-form solution of the optimal filter yields the optimal filter.

[0009] On the other hand, the present invention provides a radar mutual ambiguity function shaping device based on transceiver joint design, comprising: The first module is used to establish a joint optimization model for shaping the radar transmitted waveform and the receiving filter using the maximization of the output signal-to-interference-plus-noise ratio as the optimization criterion, while also considering the imposition of a low peak-to-average power ratio constraint on the transmitted waveform. The joint optimization model is as follows:

[0010] Among them, let This represents the radar transmission waveform, where Represents the radar transmitted waveform One sampling point, and These represent the distance position and the Doppler frequency, respectively. Indicates distance from location The relevant shift matrix, Represents the Doppler steering vector; for a given range Doppler cell , Indicates distance Doppler unit The response value at that location, This represents the unmatched filter at the receiving end. This indicates the conjugate transpose operation. Represents the Hadema product; The transmitted waveform To satisfy the finite low peak-to-average power ratio constraint, and to maximize the output signal-to-interference-plus-noise ratio, we have:

[0011] in This represents the transmit waveform that satisfies the finite low peak-to-average power ratio constraint. Set value; The second module is used to solve the joint optimization model to obtain the optimal transmission waveform and the optimal filter corresponding to the optimal transmission waveform.

[0012] Furthermore, the second module includes: The third module is used to fix the transmitted waveform in the joint optimization model, transform the joint optimization problem of the joint optimization model into a minimum variance distortionless response problem for the receiving filter, and then obtain the closed-form solution of the optimal receiving filter. The fourth module is used to substitute the closed-form solution of the optimal filter into the joint optimization model to obtain a single-variable optimization problem for the transmitted waveform; The fifth module is used to solve the single-variable optimization problem concerning the transmitted waveform to obtain the optimal transmitted waveform; The sixth module is used to substitute the optimal transmitted waveform into the closed-form solution of the optimal filter to obtain the optimal filter.

[0013] On the other hand, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: Using maximizing the output signal-to-interference-plus-noise ratio (SINR) as the optimization criterion, and considering the imposition of a low peak-to-average power ratio constraint on the transmitted waveform, a joint optimization model for shaping the mutual ambiguity function of the radar transmitted waveform and the receiving filter is established as follows:

[0014] Among them, let This represents the radar transmission waveform, where Represents the radar transmitted waveform sampling points and These represent the distance position and the Doppler frequency, respectively. Indicates distance from location The relevant shift matrix, Represents the Doppler steering vector; for a given range Doppler cell , Indicates distance Doppler unit The response value at that location, This represents the unmatched filter at the receiving end. This indicates the conjugate transpose operation. Represents the Hadema product; The transmitted waveform To satisfy the finite low peak-to-average power ratio constraint, and to maximize the output signal-to-interference-plus-noise ratio, we have:

[0015] in This represents the transmit waveform that satisfies the finite low peak-to-average power ratio constraint. Set value; Solving the joint optimization model yields the optimal transmitted waveform and the optimal filter corresponding to the optimal transmitted waveform.

[0016] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the following steps: Using maximizing the output signal-to-interference-plus-noise ratio (SINR) as the optimization criterion, and considering the imposition of a low peak-to-average power ratio (PAPR) constraint on the transmitted waveform, a joint optimization model is established for the mutual ambiguity function shaping of the radar transmitted waveform and the receiving filter. The joint optimization model is as follows:

[0017] Among them, let This represents the radar transmission waveform, where Represents the radar transmitted waveform One sampling point, and These represent the distance position and the Doppler frequency, respectively. Indicates distance from location The relevant shift matrix, Represents the Doppler steering vector; for a given range Doppler cell , Indicates distance Doppler unit The response value at that location, This represents the unmatched filter at the receiving end. This indicates the conjugate transpose operation. Represents the Hadema product; The transmitted waveform To satisfy the finite low peak-to-average power ratio constraint, and to maximize the output signal-to-interference-plus-noise ratio, we have:

[0018] in This represents the transmit waveform that satisfies the finite low peak-to-average power ratio constraint. Set value; Solving the joint optimization model yields the optimal transmitted waveform and the optimal filter corresponding to the optimal transmitted waveform.

[0019] This invention, within the context of radar ambiguity function shaping, first establishes an objective function using maximizing the output signal-to-interference-plus-noise ratio (SNR) as the optimization criterion. Subsequently, to ensure the radar nonlinear amplifier operates at maximum efficiency and avoid nonlinear distortion of the output waveform, this invention further imposes a low peak-to-average power ratio (PAR) constraint on the designed transmit waveform, thereby establishing a non-convex joint optimization problem oriented towards ambiguity function shaping. Finally, an iterative solution method based on a Majorization-Minimization (MM) framework is proposed, enabling iterative solutions for the transmit waveform and the receive filter. Through iterative updates of the transmit waveform and the receive filter, superior anti-jamming performance is achieved.

[0020] Compared with the prior art, the technical effects of the present invention are as follows: First, it offers high optimization freedom and excellent anti-interference performance. Traditional fuzzy function shaping methods only achieve this by designing the transmitted waveform, while this invention achieves CAF shaping by optimizing the transmitted waveform and the receiving filter, thus increasing the degree of freedom in fuzzy function shaping and achieving better anti-interference performance.

[0021] Second, it has low computational complexity and fast convergence speed. This invention uses linear fuzzy logic to approximate the original optimization model within the MM optimization framework, further reducing the complexity of the optimization model and achieving a better convergence rate. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a radar mutual ambiguity function shaping method based on transceiver joint design provided in one embodiment; Figure 2 This is a flowchart of a radar mutual ambiguity function shaping method based on transceiver joint design provided in another embodiment; Figure 3 This is a diagram showing the output signal-to-clutter ratio (SCR) of the radar mutual ambiguity function shaping method based on transceiver joint design provided by the present invention under different parameter conditions, and the output SCR of the radar mutual ambiguity function based on the comparison algorithm, as shown in one embodiment. Figure 4 This is a waveform amplitude value under different parameter conditions in one embodiment; Figure 5 This is a graph showing the variation of SINR with waveform length under different parameter conditions in one embodiment; Figure 6 This is a CAF shaping result diagram under different conditions in one embodiment, wherein Figure 6 (a) shows the CAF shaping result of the UniAFSIM algorithm proposed in the known literature [H. Esmaeili-Najafabadi, H. Leung, and PW Moo, “Unimodular waveform design with desired ambiguity function for cognitive radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 3, pp. 2489–2496, 2019.]. Figure 6 (b) is The present invention provides a CAF shaping result diagram based on the radar mutual ambiguity function shaping method of the transceiver joint design. Figure 6 (c) is The present invention provides a CAF shaping result diagram based on the radar mutual ambiguity function shaping method of the transceiver joint design. Figure 6 (d) is The present invention provides a CAF shaping result diagram based on the radar mutual ambiguity function shaping method of the transceiver joint design. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] Reference Figure 1 One embodiment provides a radar mutual ambiguity function shaping method based on transceiver joint design, including: Using the maximization of the output signal-to-interference-plus-noise ratio as the optimization criterion, and considering the application of a low peak-to-average power ratio constraint to the transmitted waveform, a joint optimization model for the mutual ambiguity function shaping of the radar transmitted waveform and the receiving filter is established. By solving the joint optimization model, the optimal transmission waveform and the optimal filter corresponding to the optimal transmission waveform are obtained.

[0026] In the context of radar ambiguity function shaping, maximizing the output signal-to-interference-plus-noise ratio (SNR) is used as the optimization criterion. Furthermore, applying a low peak-to-average power ratio (PAR) constraint to the transmitted waveform ensures that the radar nonlinear amplifier operates at maximum efficiency and avoids nonlinear distortion of the output waveform. Therefore, a low PAR constraint is applied to the designed transmitted waveform, establishing a joint optimization model for the mutual ambiguity function shaping of the final radar transmitted waveform and the receiving filter.

[0027] Reference Figure 2 One embodiment provides a radar mutual ambiguity function shaping method based on transceiver joint design, including: Using the maximization of the output signal-to-interference-plus-noise ratio as the optimization criterion, and considering the application of a low peak-to-average power ratio constraint to the transmitted waveform, a joint optimization model for the mutual ambiguity function shaping of the radar transmitted waveform and the receiving filter is established. Next, the joint optimization model is solved to obtain the optimal transmitted waveform and the optimal filter corresponding to the optimal transmitted waveform, specifically including: By fixing the transmitted waveform in the joint optimization model, the joint optimization problem of the joint optimization model is transformed into a minimum variance distortionless response (MVDR) problem for the receiving filter, thereby obtaining the closed-form solution of the optimal receiving filter. Substituting the closed-form solution of the optimal filter into the joint optimization model yields a univariate optimization problem concerning the transmitted waveform; The optimal transmission waveform is obtained by solving the univariate optimization problem concerning the transmission waveform. Substituting the optimal transmission waveform into the closed-form solution of the optimal filter yields the optimal filter.

[0028] Specifically, in one embodiment, let This represents the radar transmission waveform, where Represents the transmitted waveform If there are 1 sampling point, then the observation vector of the distance Doppler cell to be measured is... , is represented as:

[0029] in, This represents the complex amplitude parameter, which is related to signal propagation loss and target scattering coefficient. This represents the time-domain steering vector. The sampling noise is represented by a mean of 1 / 2. The variance is Gaussian distribution , Indicates noise power. for An identity matrix of dimension 1. Let 1 be an identity matrix. The echoes from different unrelated interference scattering points are represented as:

[0030] in, Indicates the number of interference scattering points. Indicates the first The complex amplitude of the echo at each scattering point and Indicates the first The distance and position of each scattering point and the Doppler frequency, Indicates the relationship with the first The distance of each scattering point The relevant shift matrix.

[0031] Let the unmatched filter sequence of the radar receiver be... , Let the sampling sequence of the unmatched filter at the radar receiver represent the SINR after processing at the receiver.

[0032] in, Represents the target Doppler guidance matrix. The diagonal matrix representing vectors, and the correlation matrix of the transmitted waveform. , Indicates taking the expected value. This indicates the conjugate transpose operation.

[0033] Discretize the normalized Doppler frequency range as follows If there are 10 Doppler units, then the discretized Doppler frequency range can be expressed as: ,in .thus, The mean of the samples from each distance Doppler cell can be used to approximate the calculation, i.e.

[0034] in, , Indicates Doppler frequency The corresponding Doppler steering vector, Indicates distance Doppler unit The response intensity at that point In practical applications, It can be obtained from a knowledge-assisted system [A. Aubry, A. De Maio, B. Jiang, and S. Zhang, “Ambiguity function shaping for cognitive radar via complex quartic optimization,” IEEE Transactions on Signal Processing, vol. 61, no. 22, pp.5603–5619, 2013.].

[0035] In practical applications, RF power amplifiers need to transmit waveforms with approximately constant amplitude. Therefore, the designed transmit waveform needs to further satisfy finite PAR constraints. Simultaneously, to maximize SINR, the following non-convex optimization problem is established:

[0036] in, The value is generally between 1 and 1.5.

[0037] A joint optimization model for shaping the radar transmit waveform and the receiver filter using a mutual ambiguity function is established, expressed as:

[0038] For a given range Doppler unit , Indicates distance Doppler unit The response value at that location.

[0039] For the joint optimization model of the radar transmit waveform and the receiver filter with mutual ambiguity function shaping constructed in the above embodiments, those skilled in the art can choose an appropriate method to solve it based on existing technology or their own experience, so as to obtain the optimal transmit waveform and the optimal filter corresponding to the optimal transmit waveform.

[0040] In another embodiment of the present invention, a method for solving the joint optimization model of the radar transmit waveform and the mutual ambiguity function shaping of the receive filter constructed above is proposed, as follows: By fixing the transmitted waveform in the joint optimization model, the joint optimization problem of the joint optimization model is transformed into a minimum variance distortionless response (MVDR) problem for the receiving filter, thereby obtaining the closed-form solution of the optimal receiving filter. Substituting the closed-form solution of the optimal filter into the joint optimization model yields a univariate optimization problem concerning the transmitted waveform; The optimal transmission waveform is obtained by solving the univariate optimization problem concerning the transmission waveform. Substituting the optimal transmission waveform into the closed-form solution of the optimal filter yields the optimal filter.

[0041] Specifically, effectively and simultaneously optimizing both the transmit waveform and the receive filter variables in the joint optimization model is challenging. This invention employs an iterative optimization strategy to optimize both variables separately. First, the transmit waveform in the joint optimization model is fixed. The joint optimization problem of the joint optimization model is transformed into a minimum variance distortionless response problem with respect to the receiving filter, expressed as:

[0042] The closed-form solution of the optimal receiver filter is:

[0043] in Represents the target Doppler guidance matrix. Indicates the target Doppler frequency The corresponding Doppler steering vector, A diagonal matrix representing vectors. for 3D identity matrix Indicates the target Doppler frequency Corresponding Doppler steering vector, transmitted waveform correlation matrix , Indicates noise power. To express the expectation, let The echoes from different unrelated interference scattering points are represented as:

[0044] Indicates the number of interference scattering points. Indicates the first The complex amplitude of the echo at each scattering point and Indicates the first The distance and position of each scattering point and the Doppler frequency, Indicates distance from location The relevant shift matrix.

[0045] Next, substituting the closed-form solution of the optimal filter into the joint optimization model, we obtain a univariate optimization problem concerning the transmitted waveform, expressed as:

[0046] Furthermore, the single-variable optimization problem concerning the transmitted waveform is further transformed, including: definition The univariate optimization problem of the transmitted waveform is transformed into:

[0047] By solving the single-variable optimization problem of the transformed transmission waveform, the optimal transmission waveform is obtained. Substituting the optimal transmission waveform into the closed-form solution of the optimal filter, the optimal filter is obtained.

[0048] Regarding the univariate optimization problem of solving the above-described transformed transmission waveform, those skilled in the art can choose an appropriate method to solve it based on existing technology or their own experience, and obtain the optimal transmission waveform and the optimal filter corresponding to the optimal transmission waveform.

[0049] In one specific embodiment, a method for solving the univariate optimization problem of the transformed transmission waveform is proposed, specifically including: The univariate optimization problem of the transmitted waveform is optimized, and the optimized univariate optimization problem of the transmitted waveform is expressed as:

[0050] In the In the next iteration Right now ,have , , , Indicates taking the real part, This indicates the conjugate transpose. ; The univariate optimization problem of the optimized transmission waveform is simplified and solved iteratively. The waveform at which the iteration converges is taken as the final optimal transmission waveform. The simplified problem is expressed as:

[0051] in, , This indicates taking the largest eigenvalue. ; No. The emission waveform during the next iteration , is represented as:

[0052] in Indicates the domain is Indicator functions, Indicates the domain is Indicator functions, Indicates that all element values ​​are 0. column vectors, express The number of non-zero elements in the neutron. and The calculation methods are as follows:

[0053] , express The first in Each element.

[0054] The method for setting the iterative convergence condition described in this invention is not limited, and those skilled in the art can set it based on conventional methods or common knowledge in the field. In a preferred embodiment, the maximum number of iterations can be set as the iteration stopping condition.

[0055] Regarding the single-variable optimization problem of the transformed transmission waveform described above, the transformation approach is as follows: Using the MM optimization framework: The main idea of ​​the MM optimization framework is to construct a series of simplified objective functions that are easy to solve using the principal components of the objective function, and then obtain an approximate solution to the original optimization problem by solving the simplified objective functions.

[0056] Due to the objective function about It is bidirectionally concave, and its first-order Tate expansion It is the original objective function An upper bound of this can be used as the principal component of the objective function. In the... In the next iteration, the first-order Taylor expansion of the objective function At point The specific calculation method is as follows:

[0057] in, , , , This indicates the conjugate operation. Describes the differential operator. This represents finding the rank of a matrix. Indicates taking the real part, Indicates transpose. This indicates the conjugate transpose. This represents finding the inverse of a matrix. Because... Since the constant term does not affect the optimal solution of the optimization problem, the univariate optimization problem of the transmitted waveform after ignoring the constant term can be expressed as:

[0058] The objective function in the above optimization problem is a convex function, but due to the existence of non-convex PAR constraints, the optimization problem is still a non-convex optimization problem and needs to be further simplified.

[0059] Using the MM optimization framework, the univariate optimization problem of the transmitted waveform, ignoring the constant term, is simplified again. The literature [J. Song, P. Babu, and DP Palomar, “Optimization methods for designing sequences with low autocorrelation sidelobes,” IEEE Transactions on Signal Processing, vol. 63, no. 15, pp. 3998–4009, 2015.] shows that for satisfying… of Viermitian matrix and Quadratic objective function At any point One of the principal components is:

[0060] in, express It is a positive definite matrix.

[0061] Therefore, a principal component of the univariate optimization problem of the transmitted waveform after neglecting the constant term. for:

[0062] in, This indicates taking the largest eigenvalue. Ignore. The constant term in the equation, and the univariate optimization problem of the transmitted waveform after the second simplification, can be expressed as:

[0063] in, .

[0064] make The univariate optimization problem of the aforementioned simplified transmission waveform can be further expressed as:

[0065] The following numerical simulation experiment further illustrates the effectiveness of the radar mutual ambiguity function shaping method based on transceiver joint design provided in this invention: Experimental scenario: The following experiments were conducted on a computer (2.30GHz i7-12700H core, 40.0GB RAM) using MATLAB version R2022b. During the experiments, the algorithm stopped running and output the optimized transmit waveform and receive filter when the difference in SINR between two consecutive iterations was less than 0.001. Furthermore, the interference distribution scenario was defined as follows:

[0066] The first step is to establish a joint optimization model for the mutual ambiguity function shaping of the radar transmit waveform and the receiver filter, taking the maximization of the output signal-to-interference-plus-noise ratio as the optimization criterion and considering the application of a low peak-to-average power ratio constraint to the transmit waveform. The transmit waveform and the receiver filter are then initialized.

[0067] The second step is to fix the transmitted waveform in the joint optimization model and transform the joint optimization problem of the joint optimization model into a minimum variance distortionless response problem for the receiving filter, thereby obtaining the closed-form solution of the optimal receiving filter. The third step is to substitute the closed-form solution of the optimal filter into the joint optimization model to obtain a single-variable optimization problem for the transmitted waveform. The fourth step is to iteratively solve the single-variable optimization problem concerning the transmitted waveform to obtain the optimal transmitted waveform; The fifth step is to substitute the optimal transmission waveform into the closed-form solution of the optimal filter to obtain the optimal filter.

[0068] The implementation methods of each step in the above experimental examples and the construction of the model all adopt the specific methods described in the foregoing embodiments, and will not be repeated here.

[0069] Figure 3 This document presents, in one embodiment, the output signal-to-clutter ratio (SCR) of the radar mutual ambiguity function shaping method based on transceiver joint design provided by this invention under different parameter conditions, and the output SCR of the radar mutual ambiguity function shaping method based on a comparison algorithm under different parameter conditions. The SINR variation curve under the given conditions, where the length of the transmitted waveform is... The number of discretized Doppler units is fixed at 25. .Depend on Figure 3 It can be seen that as the number of iterations increases, the output SINR value steadily increases until convergence. Furthermore, as... As the value increases, the SINR value in the convergent state also gradually increases. The PML algorithm from the paper [Yang Chen, Wu Lei, Yang Wei, et al. A method for shaping mutually fuzzy functions based on fractional quadratic programming [J]. Journal of Radar, forthcoming. doi:10.12000 / JR23126.] is used as a comparison algorithm. Figure 3 It can be seen that the radar mutual ambiguity function shaping method based on transceiver joint design provided by the present invention can achieve a higher signal-to-clutter ratio than the comparison algorithm, thus demonstrating the superiority of the method proposed in this invention.

[0070] Figure 4 Different Waveform amplitude values ​​under given conditions. Experimental results show that, as... As the value increases, the amplitude fluctuation of the waveform also gradually increases, which is consistent with the expected experimental results.

[0071] To further investigate the relationship between anti-interference performance and waveform length Figure 5 Different Output SINR under certain conditions varies with waveform length The changing relationship. From Figure 5 It can be seen that, in the same Under the given conditions, as the waveform length increases, the output SINR value also shows a monotonically increasing trend. Furthermore, regarding the waveform length... In a fixed scenario, a larger PAR value also corresponds to a higher output SINR value.

[0072] To further and more intuitively compare the CAF shaping results under different parameters, Figure 6 Schematic diagrams of CAF under different parameter conditions are provided. For comparison, Figure 6 (a) shows the ambiguity function shaping result of the UniAFSIM algorithm proposed in the known literature [H. Esmaeili-Najafabadi, H. Leung, and P. W. Moo, “Unimodular waveform design with desired ambiguity function for cognitive radar,” IEEE Transactions on Aerospace and Electronic Systems, vol.56, no. 3, pp. 2489–2496, 2019.]. Figure 6It can be seen that the fuzzy functions or CAFs implemented by the four algorithms all form obvious depressions in the interference distribution area, proving the effectiveness of the algorithms in anti-interference. However, the fuzzy function corresponding to UniAFSIM cannot precisely match the interference distribution area, and there is a certain degree of misalignment. Figure 6 (b) to Figure 6 (d) gives three types CAF results under given value conditions. Figure 6 (b) to Figure 6 (d) It can be seen that, with As the value increases, the concave region in CAF becomes deeper, exceeding the concave region formed by the UniAFSIM algorithm by more than 10 dB, further demonstrating the superiority of the method proposed in this invention.

[0073] In one embodiment, a radar mutual ambiguity function shaping device based on transceiver joint design is provided, comprising: The first module is used to establish a joint optimization model for the mutual ambiguity function shaping of the radar transmit waveform and the receive filter, taking the maximization of the output signal-to-interference-plus-noise ratio as the optimization criterion and considering the application of a low peak-to-average power ratio constraint to the transmit waveform. The second module is used to solve the joint optimization model to obtain the optimal transmission waveform and the optimal filter corresponding to the optimal transmission waveform.

[0074] Furthermore, the second module proposed in one embodiment includes: The third module is used to fix the transmitted waveform in the joint optimization model, transform the joint optimization problem of the joint optimization model into a minimum variance distortionless response problem for the receiving filter, and then obtain the closed-form solution of the optimal receiving filter. The fourth module is used to substitute the closed-form solution of the optimal filter into the joint optimization model to obtain a single-variable optimization problem for the transmitted waveform; The fifth module is used to solve the single-variable optimization problem concerning the transmitted waveform to obtain the optimal transmitted waveform; The sixth module is used to substitute the optimal transmitted waveform into the closed-form solution of the optimal filter to obtain the optimal filter.

[0075] The implementation methods of each module and the construction of the model in the above embodiments can all adopt the methods described in any of the foregoing embodiments, and will not be repeated here.

[0076] On the other hand, the present invention provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the radar mutual ambiguity function shaping method based on transceiver joint design provided in any of the above embodiments. The computer device can 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 provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and 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 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 for communication with external terminals via a network connection.

[0077] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the radar mutual ambiguity function shaping method based on transceiver joint design provided in any of the above embodiments.

[0078] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can 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 a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0079] Matters not covered in this invention are common knowledge.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0081] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0082] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A radar mutual ambiguity function shaping method based on transceiver joint design, characterized in that, include: Using maximizing the output signal-to-interference-plus-noise ratio (SINR) as the optimization criterion, and considering the imposition of a low peak-to-average power ratio constraint on the transmitted waveform, a joint optimization model for shaping the mutual ambiguity function of the radar transmitted waveform and the receiving filter is established as follows: Among them, let This represents the radar transmission waveform, where Represents the radar transmitted waveform One sampling point, and These represent the distance position and the Doppler frequency, respectively. Indicates distance from location The relevant shift matrix, Represents the Doppler steering vector; for a given range Doppler cell , Indicates distance Doppler unit The response value at that location, This represents the unmatched filter at the receiving end. This indicates the conjugate transpose operation. Represents the Hadema product; The transmitted waveform To satisfy the finite low peak-to-average power ratio constraint, and to maximize the output signal-to-interference-plus-noise ratio, we have: in This represents the transmit waveform that satisfies the finite low peak-to-average power ratio constraint. Set value; Solving the joint optimization model to obtain the optimal transmitted waveform and the corresponding optimal filter includes the following steps: By fixing the transmitted waveform in the joint optimization model, the joint optimization problem of the joint optimization model is transformed into a minimum variance distortionless response problem with respect to the receiving filter, expressed as: Therefore, the closed-form solution of the optimal receiving filter is obtained as follows: in Indicates the target Doppler frequency The corresponding Doppler guidance matrix, A diagonal matrix representing vectors. for 3D identity matrix For the target Doppler frequency, the correlation matrix of the transmitted waveform , Indicates taking the expected value. To represent noise power, let The echoes from different unrelated interference scattering points are represented as: Indicates the number of interference scattering points. Indicates the first The complex amplitude of the echo at each scattering point and Indicates the first The distance and position of each scattering point and the Doppler frequency, Indicates distance from location The relevant shift matrix; Substituting the closed-form solution of the optimal filter into the joint optimization model yields a univariate optimization problem concerning the transmitted waveform; The optimal transmission waveform is obtained by solving the univariate optimization problem concerning the transmission waveform. Substituting the optimal transmission waveform into the closed-form solution of the optimal filter yields the optimal filter.

2. The radar mutual ambiguity function shaping method based on transceiver joint design according to claim 1, characterized in that, The value range is 1 to 1.

5.

3. The radar mutual ambiguity function shaping method based on transceiver joint design according to claim 1 or 2, characterized in that, The univariate optimization problem concerning the transmitted waveform is expressed as: 。 4. The radar mutual ambiguity function shaping method based on transceiver joint design according to claim 3, characterized in that, It also includes a further transformation of the single-variable optimization problem concerning the transmitted waveform, including: definition The univariate optimization problem of the transmitted waveform is transformed into: 。 5. The radar mutual ambiguity function shaping method based on transceiver joint design according to claim 4, characterized in that, Solving the single-variable optimization problem concerning the transmitted waveform includes: The univariate optimization problem of the transmitted waveform is optimized, and the optimized univariate optimization problem of the transmitted waveform is expressed as: In the In the next iteration Right now ,have , , , Indicates taking the real part, This indicates the conjugate transpose. ; The univariate optimization problem of the optimized transmission waveform is simplified and solved iteratively. The waveform at which the iteration converges is taken as the final optimal transmission waveform. The simplified problem is expressed as: in, , This indicates taking the largest eigenvalue. ; No. The emission waveform during the next iteration , is represented as: in, Indicates the domain is Indicator functions, Indicates the domain is Indicator functions, Indicates that all element values ​​are 0. column vectors, express The number of non-zero elements in the neutron. and The calculation methods are as follows: , express The first in Each element.

6. A radar mutual ambiguity function shaping device based on a transceiver joint design, characterized in that, include: The first module, using maximizing the output signal-to-interference-plus-noise ratio as the optimization criterion and simultaneously considering the imposition of a low peak-to-average power ratio constraint on the transmitted waveform, establishes a joint optimization model for the mutual ambiguity function shaping of the radar transmitted waveform and the receiving filter, as follows: Among them, let This represents the radar transmission waveform, where Represents the radar transmitted waveform One sampling point, and These represent the distance position and the Doppler frequency, respectively. Indicates distance from location The relevant shift matrix, Represents the Doppler steering vector; for a given range Doppler cell , Indicates distance Doppler unit The response value at that location, This represents the unmatched filter at the receiving end. This indicates the conjugate transpose operation. Represents the Hadema product; The transmitted waveform To satisfy the finite low peak-to-average power ratio constraint, and to maximize the output signal-to-interference-plus-noise ratio, we have: in This represents the transmit waveform that satisfies the finite low peak-to-average power ratio constraint. Set value; The second module is used to solve the joint optimization model to obtain the optimal transmission waveform and the optimal filter corresponding to the optimal transmission waveform. The second module includes: The third module is used to fix the transmitted waveform in the joint optimization model and transform the joint optimization problem of the joint optimization model into a minimum variance distortionless response problem with respect to the receiving filter, expressed as: Therefore, the closed-form solution of the optimal receiving filter is obtained as follows: in Indicates the target Doppler frequency The corresponding Doppler guidance matrix, A diagonal matrix representing vectors. for 3D identity matrix For the target Doppler frequency, the correlation matrix of the transmitted waveform , Indicates taking the expected value. To represent noise power, let The echoes from different unrelated interference scattering points are represented as: Indicates the number of interference scattering points. Indicates the first The complex amplitude of the echo at each scattering point and Indicates the first The distance and position of each scattering point and the Doppler frequency, Indicates distance from location The relevant shift matrix; The fourth module is used to substitute the closed-form solution of the optimal filter into the joint optimization model to obtain a single-variable optimization problem for the transmitted waveform; The fifth module is used to solve the single-variable optimization problem concerning the transmitted waveform to obtain the optimal transmitted waveform; The sixth module is used to substitute the optimal transmitted waveform into the closed-form solution of the optimal filter to obtain the optimal filter.

7. The radar mutual ambiguity function shaping device based on transceiver joint design according to claim 6, characterized in that, The value range is 1 to 1.5.

Images