Adaptive robust anti-jamming method and device for subarray FDA-MIMO radar

Abstract

The application discloses a kind of subarray FDA-MIMO radar adaptive robust anti-interference method and device, it is related to signal processing technique, it solves the problem that main lobe type deception jamming signal main lobe from radar main lobe direction cannot be distinguished and inhibited in prior art, realize the ability of distinguishing and forwarding main lobe deception jamming in transmitting spatial frequency domain, improve the environmental perception ability and signal processing performance of MIMO radar in complex electromagnetic environment, the method comprises: the equivalent received signal of frequency diversity multiple-input multiple-output radar of subarray division is constructed;Realize the detection of non-uniform sample according to the covariance matrix of each range gate constructed by three-dimensional echo matrix of received signal;Remove the target covariance matrix reconstructed in non-uniform sample covariance matrix to obtain interference plus noise covariance matrix;Filter vector is calculated according to interference plus covariance matrix.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to an adaptive robust anti-interference method and apparatus for a subarray FDA-MIMO radar. Background Technology

[0002] Xi'an University of Electronic Science and Technology proposed a method for suppressing main lobe deception interference in its authorized patent document "A Method for Suppressing Main Lobe Deception Interference in FDA-MIMO Radar" (Patent Authorization No.: ZL201710739763.1). This invention utilizes an adaptive beamforming method to suppress main lobe deception interference.

[0003] In their paper "Anti-jamming Beamforming Algorithm for Phased Array MIMO Radar," Xu Feng et al. proposed a low-sidelobe anti-jamming pattern design method for phased array MIMO radar. The transmitting array employs subarray-level processing, with different subarrays maintaining waveform orthogonality. Coherent waveforms are transmitted within each subarray, and the pointing direction is determined by adjusting the phase, creating nulls at designated locations. The resulting MIMO pattern balances low sidelobes with anti-sidelobe interference requirements.

[0004] In existing technologies, phased array MIMO radars employing subarray-level processing can determine the pointing direction by adjusting the phase and create nulls at designated locations, ultimately resulting in a MIMO pattern that balances low sidelobes and resistance to sidelobe interference. However, they cannot distinguish or suppress the main lobe of main lobe-type deception jamming signals originating from the radar's main lobe direction. Summary of the Invention

[0005] This invention provides an adaptive robust anti-jamming method and apparatus for subarray FDA-MIMO radar, which solves the problem in the prior art that it is impossible to distinguish and suppress the main lobe of the main lobe deception interference signal from the direction of the radar main lobe. It realizes the ability to distinguish and forward the main lobe deception interference in the transmission space frequency domain, and improves the environmental perception capability and signal processing performance of MIMO radar in complex electromagnetic environments.

[0006] In a first aspect, the present invention provides an adaptive robust anti-jamming method for a subarray FDA-MIMO radar, the method comprising:

[0007] Construct an equivalent linear array and obtain the transmitted signal and equivalent received signal of each array element;

[0008] The multiple equivalent received signals are converted into a three-dimensional echo dataset, the three-dimensional echo dataset is distance compensated to obtain a compensated three-dimensional echo dataset, and the compensated three-dimensional echo dataset is filtered to obtain a filtered three-dimensional echo dataset.

[0009] Calculate the first set of covariance matrices corresponding to the filtered three-dimensional echo dataset, and determine the second set of covariance matrices within multiple distance gates;

[0010] Calculate the eigenvalue matrix corresponding to the second set of covariance matrices, and use the detection threshold to filter the second set of covariance matrices to obtain the third covariance matrix;

[0011] Obtain the compensated three-dimensional echo dataset corresponding to the third covariance matrix, calculate the non-uniform sample covariance matrix corresponding to the data, and remove the target covariance matrix from the non-uniform sample covariance matrix to obtain the interference covariance matrix.

[0012] The weight vector is calculated using the interference covariance matrix.

[0013] The received signal is subjected to interference removal using the weight vector, resulting in an anti-interference received signal.

[0014] In conjunction with the first aspect, in one possible implementation, constructing an equivalent linear array and obtaining the transmitted signal and equivalent received signal of each array element includes:

[0015] Obtain M transmitting elements and N receiving elements from the equivalent linear array, and divide the M transmitting elements into K subarrays; wherein the interval between each element is...

[0016] The transmit signals of each subarray are set, and the complex envelope signal corresponding to the transmit signal is calculated; wherein, the transmit signal is a phase-coded pulse composed of P self-pulses with different carrier frequencies;

[0017] Determine the center spacing between each subarray, and use the center spacing to calculate the received signal of each of the N receiving array elements;

[0018] Each array element is treated as an independent receiving array element, and the received signal is subjected to multi-channel matched filtering to obtain the multi-channel filtered signal corresponding to each array element, thereby obtaining the equivalent received signal corresponding to the array element.

[0019] In conjunction with the first aspect, in one possible implementation, performing matched filtering on the received signal to obtain multiple filtered signals corresponding to each of the array elements includes:

[0020] Each receiving channel mixes the received signal to obtain a mixed signal;

[0021] The mixed signal is sampled by an ADC to obtain the sampled signal;

[0022] Then, inter-pulse initial phase compensation and digital mixing are performed on the sampled signal to obtain the compensated signal;

[0023] The compensated signal is subjected to matched filtering to obtain the multi-channel matched filtered signal corresponding to each array element.

[0024] In conjunction with the first aspect, in one possible implementation, the distance compensation of the three-dimensional echo dataset to obtain the compensated three-dimensional echo dataset includes:

[0025] Construct the frequency domain compensation amount for the transmitter and determine the spatial domain compensation vector related to the compensation amount;

[0026] The spatial domain compensation vector is used to compensate the three-dimensional echo dataset to obtain the compensated three-dimensional echo dataset.

[0027] In conjunction with the first aspect, in one possible implementation, the step of filtering the compensated 3D echo dataset to obtain a filtered 3D echo dataset includes:

[0028] Construct a covariance matrix for each range gate, and use the constructed covariance matrix to calculate the echo energy for each range gate;

[0029] The echo energy is compared with the detection threshold, and the three-dimensional echo data with an echo energy less than the detection threshold are deleted to obtain the filtered three-dimensional echo dataset.

[0030] In conjunction with the first aspect, in one possible implementation, the step of calculating the non-consistent sample covariance matrix corresponding to the data and removing the target covariance matrix from the non-consistent sample covariance matrix to obtain the interference covariance matrix includes:

[0031] The steering vector corresponding to each receiving spatial frequency within the region is projected onto the signal subspace to obtain the estimated steering vector; wherein, the projection matrix of the signal subspace is constructed as P. s =(VV H V H V, where V represents the set of eigenvectors corresponding to each large eigenvalue;

[0032] Using the estimated guidance vector, the target guidance vector is obtained; wherein, the target guidance vector is the estimated guidance vector with the largest correlation coefficient among the estimated guidance vectors;

[0033] Using the target guidance vector, the target feature values ​​are obtained, and the target covariance matrix is ​​obtained using the target feature values;

[0034] The interference covariance matrix is ​​obtained using the target covariance matrix and the non-consistent sample covariance matrix.

[0035] In conjunction with the first aspect, in one possible implementation, obtaining the interference covariance matrix using the target covariance matrix and the non-uniform sample covariance matrix includes:

[0036] By using the difference between the non-uniform sample covariance matrix and the target covariance matrix, a covariance matrix containing only interference is obtained;

[0037] The interference-only covariance matrix is ​​then subjected to noise addition to obtain the interference-plus-noise covariance matrix.

[0038] In a second aspect, the present invention provides a subarray FDA-MIMO radar adaptive robust anti-jamming device, the device comprising:

[0039] The initialization module is used to construct the equivalent linear array and obtain the transmitted signal and equivalent received signal of each array element.

[0040] The echo data acquisition module is used to convert multiple equivalent received signals into a three-dimensional echo dataset, perform distance compensation on the three-dimensional echo dataset to obtain a compensated three-dimensional echo dataset, and filter the compensated three-dimensional echo dataset to obtain a filtered three-dimensional echo dataset.

[0041] The second covariance matrix set acquisition module is used to calculate the first covariance matrix set corresponding to the filtered three-dimensional echo dataset, and determine the second covariance matrix set within multiple distance gates.

[0042] The third covariance matrix acquisition module is used to calculate the eigenvalue matrix corresponding to the second covariance matrix set, and to filter the second covariance matrix set using a detection threshold to obtain the third covariance matrix.

[0043] The interference covariance matrix acquisition module is used to acquire the compensated three-dimensional echo dataset corresponding to the third covariance matrix, calculate the non-uniform sample covariance matrix corresponding to the data, and remove the target covariance matrix from the non-uniform sample covariance matrix to obtain the interference covariance matrix.

[0044] The weight vector acquisition module is used to calculate the weight vector using the interference covariance matrix;

[0045] The receiving signal processing module is used to remove interference from the received signal using the weight vector, resulting in an anti-interference received signal.

[0046] Thirdly, the present invention provides an adaptive robust anti-jamming server for a subarray FDA-MIMO radar, the server including a memory and a processor;

[0047] The memory is used to store computer-executable instructions;

[0048] The processor is used to execute the computer-executable instructions to implement the subarray FDA-MIMO radar adaptive robust anti-jamming method.

[0049] Fourthly, the present invention provides a computer-readable storage medium having executable instructions, wherein when a computer executes the executable instructions, it can realize an adaptive robust anti-jamming method for subarray FDA-MIMO radar.

[0050] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0051] (1) This invention employs a polyphase code signal with a frequency stepping amount on the carrier frequency, which enables the radiation pattern to have two-dimensional resolution capability of range and angle, thereby distinguishing and suppressing main lobe deceptive interference from the range dimension.

[0052] (2) Based on the FDA-MIMO radar, this invention divides the radar into subarrays and ensures the orthogonality between the transmitted signals of each subarray, so that it can still effectively distinguish between real targets and false targets when facing repeater-type deception interference.

[0053] (3) Based on the FDA-MIMO radar with subarray division, this invention deletes the target covariance matrix in the covariance matrix of non-consistent samples, thereby effectively suppressing interference while ensuring that the main lobe is focused on the target spatial frequency. Attached Figure Description

[0054] 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 of the present invention or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating the steps of the subarray FDA-MIMO radar adaptive robust anti-jamming method provided in this embodiment of the invention;

[0056] Figure 2The transmitted signal model provided in the embodiments of the present invention;

[0057] Figure 3 The received signal model provided in the embodiments of the present invention;

[0058] Figure 4 The specific processing flow for received signals provided in the embodiments of the present invention. Detailed Implementation

[0059] 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 some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0060] This invention provides an adaptive and robust anti-jamming method for subarray FDA-MIMO radar, such as... Figure 1 As shown, the method includes the following steps S101 to S105.

[0061] S101, construct an equivalent linear array and obtain the transmitted signal and equivalent received signal of each array element.

[0062] Specifically, in step S101, an equivalent linear array is constructed, and the transmitted signal and equivalent received signal of each array element are obtained, including the following steps S1011 to S1014.

[0063] S1011, obtain M transmitting elements and N receiving elements from the equivalent linear array, and divide the M transmitting elements into K subarrays; wherein the interval between each element is...

[0064] S1012 sets the transmission signal for each subarray and calculates the complex envelope signal corresponding to the transmission signal; wherein, the transmission signal is a phase-coded pulse composed of P self-pulses with different carrier frequencies. The transmission signal model is as follows. Figure 2 As shown, specifically, the transmitted signal is represented as follows:

[0065]

[0066] Where E represents the total emitted energy, T P The pulse duration is represented by m = 1, 2, ..., K, and f is the pulse width. m = f0 + (m-1)Δf, where f0 represents the reference carrier frequency, i.e., the carrier frequency of the signal transmitted by the first transmitting subarray; x m (t) represents the complex envelope signal.

[0067] Complex envelope signal x m(t) is specifically represented as:

[0068]

[0069] in, This indicates the duration of each sub-pulse, where P represents the signal code length.

[0070] S1013, determine the center spacing between each subarray, and use the center spacing to calculate the received signal of each element in the N receiving array elements.

[0071] For example, in the simulation, a subarray is treated as a single transmitting element. Since every M / K elements form a subarray, the spacing between transmitting elements... Therefore, the center spacing d between each adjacent transmitting subarray t It becomes Md / K.

[0072] For a far-field target located at a spatial distance R0 and a spatial angle at the main lobe angle θ0, the signal echo transmitted by the m-th (m = 1, 2, ..., K) subarray and received by the n-th (n = 1, 2, ..., N) array element can be expressed as:

[0073]

[0074] Where α represents the target scattering coefficient. c represents the speed of light.

[0075] S1014, treating each array element as an independent receiving array element, performs matched filtering on the received signal to obtain multiple filtered signals corresponding to each array element, and adds the multiple filtered signals to obtain the equivalent received signal corresponding to the array element. Specifically, in step S1014, performing matched filtering on the received signal to obtain multiple filtered signals corresponding to each array element includes:

[0076] (1) Each receiving channel mixes the received signal to obtain the mixed signal;

[0077] (2) Perform ADC sampling on the mixed signal to obtain the sampled signal;

[0078] (3) Then perform inter-pulse initial phase compensation and digital mixing on the sampled signal to obtain the compensated signal;

[0079] (4) Perform compensation filtering on the compensated signal to obtain multiple filtered signals corresponding to each array element.

[0080] For example, each element of the entire array acts as a separate receiving element, and the received signal is passed through K sets of matched filters corresponding to the K sets of transmitted waveforms. The received signal model is as follows: Figure 3 The model is shown.

[0081] like Figure 4 The diagram shows the specific processing flow for the received signal.

[0082] First, after each receiving channel is mixed and sampled by the ADC, inter-pulse initial phase compensation and digital mixing are performed to eliminate the Δft term.

[0083] The specific formula is as follows:

[0084]

[0085] Where, d r Indicates the spacing between the transmitting array elements. n = 1, 2, ..., N, θ0 represents the main lobe angle, λ0 represents the wavelength of the transmitted signal, and m represents the m-th transmitting subarray.

[0086] Then, K sets of waveform matching are performed, assuming the m-th transmitting element corresponds to the matched filter. The result of the signal received by the nth array element passing through the mth matched filter is:

[0087]

[0088] If a specific time delay point t0 = τ0 + Δτ is selected, where Δτ is the difference between the sampled time delay and the actual target time delay, then

[0089]

[0090] in,

[0091] The result of the signal received by the nth array element passing through the mth matched filter can be further expressed as:

[0092]

[0093] Then the total echo signal of the nth receiving element, after matched filtering, can be expressed as:

[0094]

[0095] in,(·) T The matrix transpose is represented by (·). H R represents the transpose and conjugate of a matrix. n =[R n,1 ,R n,2 ,…,R n,K ] T ,

[0096] Then the total received signal y of the nth receiving array element n (t,θ0) is represented as:

[0097]

[0098] in, This represents the signal wavelength, f0(m-1). 2 If Δf << 1 and f0(m-1)(n-1)Δf << 1, then the received signal, after mixing with the reference carrier frequency, yields:

[0099]

[0100] in,

[0101] When the received signal is received, since each element in the subarray acts as a separate receiving element, the receiving steering vector can be expressed as:

[0102]

[0103] in, Let λ be the distance between transmitting array elements, λ be the signal wavelength, θ0 be the main lobe angle, and n = 1, 2, ..., N.

[0104] The total echo signal from N receiving array elements at a spatial distance R0, after matched filtering, can be expressed as:

[0105]

[0106] Where R = [R1, R2, ..., R K ].

[0107] S102, convert multiple equivalent received signals into a three-dimensional echo dataset, perform distance compensation on the three-dimensional echo dataset to obtain a compensated three-dimensional echo dataset, and filter the compensated three-dimensional echo dataset to obtain a filtered three-dimensional echo dataset.

[0108] Specifically, in step S102, multiple equivalent received signals are converted into a three-dimensional echo dataset, which includes:

[0109] For echo data from all L range gates, the received signal can be represented as three-dimensional echo data Y∈C. K×N×L .

[0110]

[0111] Among them, y s This represents the echo signal from the target, y q Let n represent the echo signal from the q-th interference, and n represent the noise.

[0112] Specifically, in step S102, distance compensation is performed on the three-dimensional echo dataset to obtain the compensated three-dimensional echo dataset, including the following steps.

[0113] (1) Construct the transmitter frequency domain compensation quantity and determine the spatial domain compensation vector related to the compensation quantity. Specifically, since the transmit spatial frequency of the FDA-MIMO radar has range-dependent characteristics, the transmitter frequency compensation quantity can be constructed using... Where r is determined by the range gate number and range gate size, the transmit spatial domain compensation vector can be constructed as follows:

[0114]

[0115] The compensation vector in the joint transmit-receive spatial frequency domain can then be expressed as:

[0116]

[0117] Where K represents the number of transmitting subarrays; 1 N×1 This represents a column vector consisting entirely of 1s.

[0118] (2) The spatial domain compensation vector is used to compensate the three-dimensional echo dataset to obtain the compensated three-dimensional echo dataset.

[0119] By using frequency compensation to compensate for the transmission spatial frequency of the real target and each false target respectively, the distance information in the transmission guidance vector of the real target and the false target after compensation only includes the index of the distance ambiguity interval in which it is located.

[0120] Specifically, in step S102, the compensated three-dimensional echo dataset is filtered to obtain the filtered three-dimensional echo dataset, including the following steps.

[0121] (1) Construct the construction covariance matrix corresponding to each range gate, and use the construction covariance matrix to calculate the echo energy corresponding to each range gate.

[0122] (2) Determine the size of the echo energy and the detection threshold, and delete the three-dimensional echo data whose echo energy is less than the detection threshold to obtain the filtered three-dimensional echo dataset.

[0123] For example, the sample selection problem can be constructed as a quaternary hypothesis testing problem.

[0124]

[0125] Where X represents the number of digits. The radar signal sample of the k-th pulse with a range cell, where S, J, and N represent the real target, the false target, and the noise component, respectively.

[0126] Construct the covariance matrix using the l-th distance gate Where, x k,l Let P represent the transmit-receive snapshot of the k-th pulse and the l-th range gate, and let P represent the pulse number. The echo energy of the l-th range gate can be estimated as P. x =trace(R) l If ), then the detection threshold for non-conforming samples is . Where P f Let σ be the set false alarm probability. n To represent noise power, we can select non-uniform samples that include the signal and / or exclude interference (i.e., those that satisfy the H1, H2 / H3 assumptions).

[0127] The selection criteria are: echo energy > threshold.

[0128] S103, calculate the first covariance matrix set corresponding to the filtered three-dimensional echo dataset, and determine the second covariance matrix set within multiple distance gates.

[0129] Specifically, L distance gates are selected within the fuzzy distance Ru, and eigenvalue decomposition is performed on the covariance matrix within each distance gate to obtain the eigenvalue matrix S∈R. KN×L The eigenvalue matrix is:

[0130] S = [tr(R1), tr(R2), ..., tr(R L )]

[0131] in, x k,l This represents the echo data obtained from the k-th pulse and the l-th distance gate.

[0132] S104, calculate the eigenvalue matrix corresponding to the second covariance matrix set, and use the detection threshold to filter the second covariance matrix set to obtain the third covariance matrix. Specifically, by finding large eigenvalues ​​exceeding the detection threshold, find e distance gates containing non-consistent samples (including the target and interference), according to the formula... Using the echo data from these range gates, the non-uniform sample covariance matrix R is constructed, where l n This represents the distance gate where non-consistent samples are located.

[0133] S105, obtain the compensated three-dimensional echo dataset corresponding to the third covariance matrix, calculate the non-consistent sample covariance matrix R corresponding to the data, and remove the target covariance matrix RS from the non-consistent sample covariance matrix R to obtain the interference covariance matrix.

[0134] Specifically, in step S105, the non-consistent sample covariance matrix R corresponding to the data is calculated, and the target covariance matrix R is removed from the non-consistent sample covariance matrix R. S The interference covariance matrix is ​​obtained by the following steps S1051 to S1054.

[0135] S1051, the steering vector corresponding to each received spatial frequency within the region is projected onto the signal subspace to obtain the estimated steering vector; first, the covariance matrix of all samples is eigenvalued to obtain each large eigenvalue and its corresponding eigenvector. The projection matrix of the signal subspace is constructed as P. s =(VV H V H V, where V represents the set of eigenvectors corresponding to each large eigenvalue. Specifically, to avoid performance degradation due to the presence of the real target, the covariance matrix corresponding to the real target needs to be eliminated from the covariance matrix constructed from non-consistent samples. The projection matrix of the signal subspace can be constructed as P. s =(VV H V H V, where V represents the set of eigenvectors corresponding to each large eigenvalue. Assume only the spatial frequency range Θ of the true target exists. s Given that region Θ s The steering vector corresponding to each transmit / receive spatial frequency is projected onto the signal subspace, and the relationship between the signal subspace and Θ is found. s The steering vector with the highest correlation within the range is used as the estimated steering vector, i.e.

[0136]

[0137] S1052, using the estimated guidance vector, obtain the target guidance vector; where the target guidance vector is the estimated guidance vector with the largest correlation coefficient among the estimated guidance vectors. Specifically, to further find the guidance vector corresponding to the target, calculate the correlation coefficients between all guidance vectors within V and the estimated guidance vectors obtained in step 3, thus obtaining the guidance vector within V with the largest correlation coefficient with the estimated guidance vectors as the target guidance vector, i.e.

[0138]

[0139] S1053, using the target guidance vector, obtain the corresponding target eigenvalues, and then use the target eigenvalues ​​to obtain the target covariance matrix R. S Specifically, the large eigenvalue λ corresponding to the target guidance vector is obtained. s Thus achieving the target covariance matrix The construction.

[0140] S1054, using the target covariance matrix and the non-uniform sample covariance matrix R, obtain the interference covariance matrix R. j Specifically, this includes: utilizing the non-uniform sample covariance matrix R and the target covariance matrix R0. S The difference is used to obtain a covariance matrix containing only interference; noise is added to this covariance matrix to obtain the interference covariance matrix. Specifically, it is represented as: R containing only the interference covariance matrix. j =RR S Then the interference plus noise covariance matrix in This represents noise power.

[0141] S106, using the interference covariance matrix R j+n The weight vector is calculated.

[0142] For example, in order to form arbitrary nulls in the transmit-receive two-dimensional spatial domain, an MVDR transmit-receive two-dimensional beamformer can be designed:

[0143]

[0144] stw opt H u(R0,θ0)=1

[0145] Where wopt is the adaptive optimal weight vector. This represents the sending and receiving of a two-dimensional virtual guide vector.

[0146] The weight vector can be obtained as follows:

[0147] S107 uses weight vectors to remove interference from the received signal, resulting in an anti-interference received signal.

[0148] Secondly, the present invention provides a subarray FDA-MIMO radar adaptive robust anti-jamming device, which includes: an initialization module, an echo data acquisition module, a second covariance matrix set acquisition module, a third covariance matrix acquisition module, an interference covariance matrix acquisition module, a weight vector acquisition module, and a received signal processing module.

[0149] The initialization module is used to construct the equivalent linear array and obtain the transmitted and received signals of each array element.

[0150] The echo data acquisition module is used to convert multiple equivalent received signals into a three-dimensional echo dataset, perform distance compensation on the three-dimensional echo dataset to obtain a compensated three-dimensional echo dataset, and filter the compensated three-dimensional echo dataset to obtain a filtered three-dimensional echo dataset.

[0151] The second covariance matrix set acquisition module is used to calculate the first covariance matrix set corresponding to the filtered three-dimensional echo dataset, and determine the second covariance matrix set within multiple distance gates.

[0152] The third covariance matrix acquisition module is used to calculate the eigenvalue matrix corresponding to the second covariance matrix set, and to filter the second covariance matrix set using a detection threshold to obtain the third covariance matrix.

[0153] The interference covariance matrix acquisition module is used to acquire the compensated three-dimensional echo dataset corresponding to the third covariance matrix, calculate the non-consistent sample covariance matrix corresponding to the data, and remove the target covariance matrix from the non-consistent sample covariance matrix to obtain the interference covariance matrix.

[0154] The weight vector acquisition module is used to calculate the weight vector using the interference covariance matrix.

[0155] The receiving signal processing module is used to remove interference from the received signal using weight vectors, resulting in an anti-interference received signal.

[0156] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. In implementing this invention, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0157] The methods, apparatus, or modules described in this invention can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, for example, as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0158] Some modules in the apparatus described in this invention can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0159] This invention provides an adaptive robust anti-jamming server for a subarray FDA-MIMO radar. The server includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes the computer-executable instructions to implement the adaptive robust anti-jamming method for the subarray FDA-MIMO radar.

[0160] This invention provides a computer-readable storage medium having executable instructions, which, when executed by a computer, enables an adaptive and robust anti-jamming method for a subarray FDA-MIMO radar.

[0161] The aforementioned storage media include, but are not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions.

[0162] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in this embodiment is merely one possible execution order among many and does not represent the only possible execution order. In actual device or client product execution, the methods shown in this embodiment or the accompanying drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0163] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a 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, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0164] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. The focus of each embodiment is on its differences from other embodiments. All or part of this invention can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0165] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the present invention. Although the present invention 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present invention.

Claims

1. An adaptive robust anti-jamming method for a subarray FDA-MIMO radar, characterized in that, include: Construct an equivalent linear array and obtain the transmitted signal and equivalent received signal of each array element; The multiple equivalent received signals are converted into a three-dimensional echo dataset, the three-dimensional echo dataset is distance compensated to obtain a compensated three-dimensional echo dataset, and the compensated three-dimensional echo dataset is filtered to obtain a filtered three-dimensional echo dataset. Calculate the first set of covariance matrices corresponding to the filtered three-dimensional echo dataset, and determine the second set of covariance matrices within multiple distance gates; Calculate the eigenvalue matrix corresponding to the second set of covariance matrices, and use the detection threshold to filter the second set of covariance matrices to obtain the third covariance matrix; Obtain the compensated 3D echo dataset corresponding to the third covariance matrix, calculate the non-consistent sample covariance matrix corresponding to the data, and remove the target covariance matrix from the non-consistent sample covariance matrix to obtain the interference covariance matrix; wherein, the non-consistent sample covariance matrix is ​​obtained by filtering e distance gates according to the detection threshold, and then constructing the non-consistent sample covariance matrix according to the echo data corresponding to the e distance gates; The weight vector is calculated using the interference covariance matrix. The received signal is subjected to interference removal using the weight vector to obtain an anti-interference received signal.

2. The subarray FDA-MIMO radar adaptive robust anti-jamming method according to claim 1, characterized in that, The construction of the equivalent linear array and the acquisition of the transmitted signal and equivalent received signal of each array element include: Obtain M transmitting elements and N receiving elements from the equivalent linear array, and divide the M transmitting elements into K subarrays; wherein the interval between each element is... ; The transmit signals for each subarray are defined, and the complex envelope signal corresponding to the transmit signal is calculated; wherein, the transmit signal is a signal with a different carrier frequency, generated by... A phase-coded pulse composed of self-pulses; Determine the center spacing between each subarray, and use the center spacing to calculate the received signal of each of the N receiving array elements; Each array element is treated as an independent receiving array element, and the received signal is subjected to matched filtering to obtain the multi-channel filtered signal corresponding to each array element, thereby obtaining the multi-channel equivalent received signal corresponding to the array element.

3. The subarray FDA-MIMO radar adaptive robust anti-jamming method according to claim 2, characterized in that, The step of performing matched filtering on the received signal to obtain the multi-channel filtered signal corresponding to each of the array elements includes: Each receiving channel mixes the received signal to obtain a mixed signal; The mixed signal is sampled by an ADC to obtain the sampled signal; Then, inter-pulse initial phase compensation and digital mixing are performed on the sampled signal to obtain the compensated signal; The compensated signal is subjected to matched filtering to obtain the multi-channel matched filtered signal corresponding to each array element.

4. The subarray FDA-MIMO radar adaptive robust anti-jamming method according to claim 1, characterized in that, The step of performing distance compensation on the three-dimensional echo dataset to obtain the compensated three-dimensional echo dataset includes: Construct the frequency domain compensation amount for the transmitter and determine the spatial domain compensation vector related to the compensation amount; The spatial domain compensation vector is used to compensate the three-dimensional echo dataset to obtain the compensated three-dimensional echo dataset.

5. The subarray FDA-MIMO radar adaptive robust anti-jamming method according to claim 1, characterized in that, The step of filtering the compensated 3D echo dataset to obtain a filtered 3D echo dataset includes: Construct a covariance matrix for each range gate, and use the constructed covariance matrix to calculate the echo energy for each range gate; The echo energy is compared with the detection threshold, and the three-dimensional echo data with an echo energy less than the detection threshold are deleted to obtain the filtered three-dimensional echo dataset.

6. The subarray FDA-MIMO radar adaptive robust anti-jamming method according to claim 1, characterized in that, The calculation of the non-consistent sample covariance matrix corresponding to the data, and the removal of the target covariance matrix from the non-consistent sample covariance matrix to obtain the interference plus noise covariance matrix, includes: The steering vector corresponding to each received spatial frequency within the region is projected onto the signal subspace to obtain the estimated steering vector; wherein, the projection matrix of the signal subspace is constructed as follows: ,in, This represents the set of eigenvectors corresponding to each large eigenvalue; Using the estimated guidance vector, the target guidance vector is obtained; wherein, the target guidance vector is the estimated guidance vector with the largest correlation coefficient among the estimated guidance vectors; Using the target guidance vector, the target feature values ​​are obtained, and the target covariance matrix is ​​obtained using the target feature values; The interference plus noise covariance matrix is ​​obtained using the target covariance matrix and the non-uniform sample covariance matrix.

7. The subarray FDA-MIMO radar adaptive robust anti-jamming method according to claim 6, characterized in that, The step of obtaining the interference plus noise covariance matrix using the target covariance matrix and the non-uniform sample covariance matrix includes: By using the difference between the non-uniform sample covariance matrix and the target covariance matrix, a covariance matrix containing only interference is obtained; The interference-only covariance matrix is ​​then subjected to noise addition to obtain the interference-plus-noise covariance matrix.

8. A subarray FDA-MIMO radar adaptive robust anti-jamming device, characterized in that, include: The initialization module is used to construct the equivalent linear array and obtain the transmitted signal and equivalent received signal of each array element. The echo data acquisition module is used to convert multiple equivalent received signals into a three-dimensional echo dataset, perform distance compensation on the three-dimensional echo dataset to obtain a compensated three-dimensional echo dataset, and filter the compensated three-dimensional echo dataset to obtain a filtered three-dimensional echo dataset. The second covariance matrix set acquisition module is used to calculate the first covariance matrix set corresponding to the filtered three-dimensional echo dataset, and determine the second covariance matrix set within multiple distance gates. The third covariance matrix acquisition module is used to calculate the eigenvalue matrix corresponding to the second covariance matrix set, and to filter the second covariance matrix set using a detection threshold to obtain the third covariance matrix. The interference covariance matrix acquisition module is used to acquire the compensated three-dimensional echo dataset corresponding to the third covariance matrix, calculate the non-consistent sample covariance matrix corresponding to the data, and remove the target covariance matrix from the non-consistent sample covariance matrix to obtain the interference plus noise covariance matrix; wherein, the non-consistent sample covariance matrix is ​​obtained by filtering e distance gates according to the detection threshold, and then constructing the non-consistent sample covariance matrix according to the echo data corresponding to the e distance gates; The weight vector acquisition module is used to calculate the weight vector using the interference plus noise covariance matrix; The receiving signal processing module is used to filter the received signal using the weight vector to obtain an anti-interference received signal.

9. A subarray FDA-MIMO radar adaptive robust anti-jamming server, characterized in that, Including memory and processor; The memory is used to store computer-executable instructions; The processor is configured to execute the computer-executable instructions to implement the subarray FDA-MIMO radar adaptive robust anti-jamming method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium has executable instructions, and when the computer executes the executable instructions, it can implement the subarray FDA-MIMO radar adaptive robust anti-jamming method as described in any one of claims 1-7.

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