MIMO radar imaging method based on sparse estimation
阅读说明：本技术 基于稀疏估计的mimo雷达成像方法 (MIMO radar imaging method based on sparse estimation ) 是由 王敏 邓晓云 丁杰如 于 2020-08-29 设计创作，主要内容包括：本发明公开了一种基于稀疏估计的MIMO雷达成像方法,解决了稀疏度未知时,影响成像精度的问题,以及解决了现有技术运算量大的问题。实现包括：建立MIMO雷达成像模型,获取MIMO雷达回波数据；对测量矩阵Φ进行预处理,将测量矩阵均匀分为多个子通道,对每个子通道预判,获得预处理后的测量矩阵；结合稀疏度恢复算法SAMP或SBL算法,完成基于稀疏估计的MIMO雷达成像。本发明对测量矩阵进行预处理,将不含信号的子通道测量矩阵置零,减小噪声影响。本发明将测量矩阵预处理与SAMP或SBL算法相结合,提高稀疏恢复的准确度,降低了成像误差,使在稀疏恢复过程中运算复杂度降低,提高运算速度。仿真和实验证明了本发明成像精度高,运行量低。用于MIMO雷达成像。(The invention discloses a sparse estimation-based MIMO radar imaging method, which solves the problem that imaging precision is influenced when sparsity is unknown and solves the problem of large operation amount in the prior art. The implementation comprises the following steps: establishing an MIMO radar imaging model, and acquiring MIMO radar echo data; preprocessing a measurement matrix phi, uniformly dividing the measurement matrix into a plurality of sub-channels, and prejudging each sub-channel to obtain a preprocessed measurement matrix; and combining a sparsity recovery algorithm SAMP or SBL algorithm to complete MIMO radar imaging based on sparse estimation. The invention preprocesses the measuring matrix, sets the measuring matrix of the sub-channel without signals to zero, and reduces the noise influence. According to the invention, the measurement matrix pretreatment is combined with the SAMP or SBL algorithm, so that the accuracy of sparse recovery is improved, the imaging error is reduced, the operation complexity in the sparse recovery process is reduced, and the operation speed is improved. Simulation and experiment prove that the invention has high imaging precision and low running amount. The method is used for MIMO radar imaging.)
1. A MIMO radar imaging method based on sparse estimation is characterized by comprising the following steps:
the method comprises the following steps: establishing an MIMO radar imaging model: in a rectangular coordinate system, the origin of coordinates is Q, the MIMO radar imaging center is represented by O points, and the transmitting and receiving array elements are respectively represented asRTx,mAnd RRx,nRespectively showing the distance from the Mth transmitting array element and the Nth receiving array element to the O point,
where n is noise, y is radar echo,
step two: preprocessing a measurement matrix phi: uniformly partitioning the measurement matrix phi into L subchannels; setting a threshold Th, and comparing the coefficient eta with the initial residual r0The threshold value Th is obtained by multiplying two norms, and the coefficient eta belongs to (0, 1)](ii) a Calculating a subchannel residual error, prejudging each subchannel according to a threshold value, if the subchannel residual error is smaller than the threshold value, setting the measurement matrix of the subchannel to zero, and if the subchannel residual error is larger than the threshold value, keeping the measurement matrix of the subchannel unchanged; pre-judging each sub-channel according to the pre-judging criterion, and pre-judging all the sub-channels to form a new measurement matrix, namely a pre-processed measurement matrix theta, wherein the pre-processed measurement matrix theta hasSparsity;
step three: the preprocessed measurement matrix with sparsity is utilized to complete target estimation, namely, the MIMO radar imaging model is imaged according to the preprocessed measurement matrix
2. The MIMO radar imaging method based on sparse estimation according to claim 1, wherein the preprocessing the measurement matrix Φ in the step two specifically comprises the following steps:
2.1: and (3) partitioning the radar measurement matrix: uniformly partitioning a radar measurement matrix phi, dividing the measurement matrix phi into L blocks as a whole, wherein each block is a subchannel, the length of each subchannel is D, and expressing the measurement matrix phi after partitioning as follows:
wherein phikMeasuring a matrix for a kth subchannel, k being 1, 2.., L;
2.2: setting a channel threshold Th: setting a threshold Th according to radar echo by combining an Orthogonal Matching Pursuit (OMP) algorithm;
Th＝η||r0||2
wherein the coefficient eta ∈ (0, 1)]，r0Y is the initial residual. Expressing the threshold judgment as:
||rk||＜Th(k∈{1,2,...,L})
where L is the number of subchannels, rkIs the residual error of the k channel;
2.3: denoising the sub-channels by using a threshold value: applying an Orthogonal Matching Pursuit (OMP) algorithm to each subchannel to achieve a denoising function; the OMP algorithm is iterated once, the most closely related parts in the subchannels are deleted, whether the subchannels contain signals or not is roughly estimated, namely, a threshold value is obtained through calculation, a residual error is updated in a loop, and meanwhile, the subchannels are pre-judged; if the channel residual is smaller than the threshold, setting the measurement matrix of the subchannel to zero, namely phi [ k ] is 0, and if the subchannel residual is larger than the threshold, keeping the measurement matrix of the subchannel unchanged; pre-judging all the sub-channels to form a new measurement matrix, namely a pre-processed measurement matrix theta, wherein the pre-processed measurement matrix theta has sparsity;
3. the sparsity estimation-based MIMO radar imaging target estimation method of claim 2, wherein said denoising processing is performed on the sub-channels in step 2.3, as described in detail below;
2.3.1: initialization processing: channelizing the measurement matrix, i.e. uniformly dividing the measurement matrix into L blocks and initializing k to 0, r0＝y；
2.3.2: the cycle starts: when k is less than or equal to L, k is k +1, and the following circulation is carried out;
2.3.3: calculating subchannel related parameters Pk:
2.3.4: calculating subchannel residuals: according to calculated PkCalculating the subchannel residual rk，
2.3.5: judging a threshold value: if it is notZeroing the measurement matrix of the subchannel, i.e. Φ k]＝0；
2.3.6: and (4) ending the circulation: and when k is larger than L, ending the circulation, ending the pretreatment of the measurement matrix phi, and obtaining the pretreated measurement matrix theta.
Technical Field
The invention relates to the technical field of radar, mainly relates to MIMO radar imaging, and particularly relates to a MIMO radar imaging method based on sparsity estimation. The method is applied to radar imaging.
Background
The Multiple Input Multiple Output (MIMO) radar of the new radar system has the significant advantages of high resolution and real-time performance in imaging, so research on MIMO radar imaging is also continuously proposed. Han and the like have analyzed the problem of distributed multi-channel radar imaging based on the MIMO regime from the perspective of spatial spectrum sampling. The MIMO radar imaging algorithm is comprehensively researched by people of the Wang Huan Jun, the national defense science and technology university, the classic back projection algorithm, the distance offset algorithm and the like are improved and then applied to MIMO radar imaging, the principle verification is carried out, the fusion imaging processing algorithm is provided, and the problem of the MIMO radar imaging spectrum loss is solved. The classic BP algorithm is most widely applied due to the fact that the classic BP algorithm is not limited by an array form and plane wave approximation does not exist, imaging accuracy is highest, but the defect is that operation amount is large when a depth visual angle scene is imaged, and real-time performance of an imaging system is limited. Iterative adaptive methods (IAA) and iterative minimized Sparse Learning (SLIM) are introduced into MIMO radar imaging due to their advantage of ultra-high resolution in radar imaging. However, the two algorithms are proved to have the defect of large calculation amount, and are not easy to be applied in practice like the time domain imaging algorithm. The high calculation amount becomes a main reason for restricting the development of MIMO radar imaging, and is in the need of solving. Although the existing compressed sensing algorithm, such as a greedy algorithm, has the advantage of low computational complexity, sparsity needs to be taken as a known condition, and the sparsity is difficult to acquire in practice and has poor engineering applicability. When a greedy algorithm is used in practical application, if the sparsity is unknown, imaging accuracy is affected, the greedy algorithm is sensitive to noise, interference on radar imaging is increased, and the problem has certain influence on the MIMO radar imaging effect.
Disclosure of Invention
The invention aims to provide a sparse estimation-based MIMO radar imaging method with higher estimation accuracy aiming at the defects and defects of the prior art, which is characterized by comprising the following steps:
the method comprises the following steps: establishing an MIMO radar imaging model: in a rectangular coordinate system, the origin of coordinates is Q, the MIMO radar imaging center is represented by O points, and the transmitting and receiving array elements are respectively represented asRTx,mAnd RRx,nRespectively showing the distance from the Mth transmitting array element and the Nth receiving array element to the O point,
where n is noise, y is radar echo,
step two: preprocessing a measurement matrix phi: uniformly partitioning the measurement matrix phi into L subchannels; setting a threshold Th, and comparing the coefficient eta with the initial residual r0Multiplying y by two norms to obtain a threshold Th, and setting the coefficient eta to be (0, 1)](ii) a According toThe threshold value pre-judges each subchannel, if the subchannel residual error is smaller than the threshold value, the measurement matrix of the subchannel is set to zero, and if the subchannel residual error is larger than the threshold value, the measurement matrix of the subchannel is kept unchanged; pre-judging each subchannel according to the pre-judging criterion, and forming a new measurement matrix, namely a pre-treated measurement matrix theta after pre-judging all the subchannels, wherein the pre-treated measurement matrix theta has sparsity;
step three: utilizing the preprocessed measurement matrix theta to complete target estimation, namely, using the MIMO radar imaging modelThe updating is as follows:
The method solves the problems that the signal reconstruction effect is poor and the imaging accuracy is influenced in the case of unknown sparsity of an Orthogonal Matching Pursuit (OMP) algorithm. Meanwhile, the problem of interference of errors caused by noise to radar imaging in the signal reconstruction process is solved.
Compared with the prior art, the invention has the beneficial effects that:
the accuracy is improved: aiming at the problem that the Orthogonal Matching Pursuit (OMP) algorithm is poor in signal reconstruction effect under the condition that the sparsity is unknown, the SAMP algorithm and the SBL algorithm are introduced, and the SAMP algorithm and the SBL algorithm do not need to take the sparsity as a known condition, so that the signal reconstruction effect is better compared with the OMP algorithm. Therefore, the SAMP algorithm or the SBL algorithm is combined with the channelized measurement matrix to solve the problem of unknown sparsity caused by the OMP algorithm and improve the radar imaging accuracy.
And (3) error reduction: aiming at the problem of errors caused by noise in the sparse reconstruction process of the greedy algorithm, the denoising function is achieved by adding the measurement matrix phi for preprocessing, the accuracy of the reconstructed signal is improved, and the imaging errors are reduced.
The calculation amount is reduced: in the channelized preprocessing process, the observation matrix is preprocessed according to the threshold value, and the subchannel observation matrix without the target is set to be zero, so that the operation amount in the signal recovery process is reduced, and the operation speed is improved.
Description of the drawings:
the invention is described in detail below with reference to the figures and the specific embodiments.
FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a MIMO radar imaging geometry, which is also applied in embodiments of the present invention;
FIG. 3 is a graph of the present invention and a prior art sparse recovery algorithm at different SNR, wherein FIG. 3(a) is a minimum mean square error graph and FIG. 3(b) is a target background ratio variation graph;
FIG. 4 is a graph of the present invention under different channel numbers and signal-to-noise ratios, wherein FIG. 4(a) is a minimum mean square error graph and FIG. 4(b) is a graph of variation of target background ratio;
fig. 5 is a graph of the present invention and the prior sparse recovery algorithm under different sparse ratios, wherein fig. 5(a) is a minimum mean square error graph, and fig. 5(b) is a target background ratio variation graph.
The invention is described in detail below with reference to the drawings and examples
Detailed Description
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