CS-MIMO雷达测量矩阵构造方法研究
发布时间：2018-10-16 12:31
【摘要】：多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达作为一种新体制雷达,能够运用空间分集和波形分集技术获得更大的系统自由度,从而有效提高目标探测能力和参数估计性能。MIMO雷达探测场景中目标分布的稀疏性为压缩感知(Compressed Sensing,CS)理论的应用提供了条件,基于压缩感知的MIMO雷达参数估计已成为雷达信号处理领域的研究热点之一。本文将压缩感知理论引入MIMO雷达系统架构,研究波形设计、阵列配置与压缩感知测量的关系,提出基于发射波形、阵列构型等效构造测量矩阵的思路,并通过波形设计、阵列配置实现对测量矩阵的优化,提升CS-MIMO雷达参数估计性能。本文的主要工作如下:研究了CS-MIMO雷达信号模型,阐述了压缩感知基础理论和CS-MIMO雷达参数估计原理;并对重构性能影响因素进行分析,讨论测量矩阵的构造及优化方法,提出基于发射波形、阵列构型实现测量矩阵等效构造的思路。利用混沌序列设计MIMO雷达发射信号,将混沌伪随机发射信号作为测量算子,CS-MIMO雷达中测量矩阵的优化设计问题便等效为波形优化问题,有效降低系统模型的复杂度。为优化频谱形状、抑制干扰噪声,同时保证期望波形的随机性、正交性,提出一种认知MIMO雷达波形优化设计算法。仿真实验表明等效模型以及优化算法的有效性,可明显提高目标参数估计的准确度和精度。研究基于稀疏随机线阵的CS-MIMO雷达信号模型,利用阵元位置的随机性实现压缩观测,并证明所构造的感知矩阵满足非均匀重构条件。为降低等效感知矩阵的列间相关性,提升算法的稀疏度上限和重构性能,基于模拟退火算法对阵列进行优化,实现测量矩阵的优化。仿真实验表明优化阵列可提高目标DOA重构概率和估计精度。将一维稀疏随机线阵扩展至二维L阵形式,研究基于稀疏随机L阵的CS-MIMO雷达信号模型,证明阵列导引矢量矩阵可等效为测量矩阵并满足非均匀重构条件。研究基于粒子群算法的L阵优化方法,对等效测量矩阵进行优化,进一步降低感知矩阵列间相关性。仿真实验表明该方法可有效提高CS-MIMO雷达二维角度估计性能。
[Abstract]:As a new system radar, multi-input multiple-output (Multiple-Input Multiple-Output,MIMO) radar can obtain greater degree of freedom by using spatial diversity and waveform diversity techniques. The sparsity of target distribution in MIMO radar detection scene provides the condition for the application of compressed perception (Compressed Sensing,CS) theory. MIMO radar parameter estimation based on compressed sensing has become one of the hot topics in the field of radar signal processing. In this paper, the compression sensing theory is introduced into the MIMO radar system architecture, and the waveform design, the relationship between array configuration and compression sensing measurement is studied, and the idea of constructing measurement matrix based on transmitting waveform and array configuration is put forward, and the waveform design is adopted. Array configuration can optimize the measurement matrix and improve the performance of CS-MIMO radar parameter estimation. The main work of this paper is as follows: the CS-MIMO radar signal model is studied, the basic theory of compression sensing and the principle of CS-MIMO radar parameter estimation are expounded, and the factors affecting the reconstruction performance are analyzed, and the construction and optimization method of the measurement matrix are discussed. In this paper, an equivalent structure of measurement matrix is proposed based on transmitting waveform and array configuration. Using chaotic sequence to design MIMO radar transmit signal, using chaotic pseudorandom transmit signal as measurement operator, the optimal design problem of measurement matrix in CS-MIMO radar is equivalent to waveform optimization problem, which effectively reduces the complexity of system model. In order to optimize the spectrum shape, suppress the interference noise and ensure the randomness and orthogonality of the desired waveform, a cognitive MIMO radar waveform optimization algorithm is proposed. Simulation results show that the effectiveness of the equivalent model and the optimization algorithm can significantly improve the accuracy and accuracy of the target parameter estimation. In this paper, the CS-MIMO radar signal model based on sparse random linear array is studied. The randomness of the position of the array elements is used to realize the compression observation, and it is proved that the constructed perceptual matrix satisfies the non-uniform reconstruction condition. In order to reduce the correlation between columns of the equivalent perceptual matrix and improve the sparse upper limit and reconstruction performance of the algorithm, the array is optimized based on simulated annealing algorithm, and the measurement matrix is optimized. Simulation results show that the optimized array can improve the probability and accuracy of target DOA reconstruction. The one-dimensional sparse random linear array is extended to 2-D L-matrix, and the CS-MIMO radar signal model based on sparse random L-matrix is studied. It is proved that the array guidance vector matrix can be equivalent to the measurement matrix and satisfy the condition of non-uniform reconstruction. The L matrix optimization method based on particle swarm optimization algorithm is studied to optimize the equivalent measurement matrix and further reduce the correlation between the columns of the perceptual matrix. Simulation results show that this method can effectively improve the performance of CS-MIMO radar two-dimensional angle estimation.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN958
本文编号：2274369
[Abstract]:As a new system radar, multi-input multiple-output (Multiple-Input Multiple-Output,MIMO) radar can obtain greater degree of freedom by using spatial diversity and waveform diversity techniques. The sparsity of target distribution in MIMO radar detection scene provides the condition for the application of compressed perception (Compressed Sensing,CS) theory. MIMO radar parameter estimation based on compressed sensing has become one of the hot topics in the field of radar signal processing. In this paper, the compression sensing theory is introduced into the MIMO radar system architecture, and the waveform design, the relationship between array configuration and compression sensing measurement is studied, and the idea of constructing measurement matrix based on transmitting waveform and array configuration is put forward, and the waveform design is adopted. Array configuration can optimize the measurement matrix and improve the performance of CS-MIMO radar parameter estimation. The main work of this paper is as follows: the CS-MIMO radar signal model is studied, the basic theory of compression sensing and the principle of CS-MIMO radar parameter estimation are expounded, and the factors affecting the reconstruction performance are analyzed, and the construction and optimization method of the measurement matrix are discussed. In this paper, an equivalent structure of measurement matrix is proposed based on transmitting waveform and array configuration. Using chaotic sequence to design MIMO radar transmit signal, using chaotic pseudorandom transmit signal as measurement operator, the optimal design problem of measurement matrix in CS-MIMO radar is equivalent to waveform optimization problem, which effectively reduces the complexity of system model. In order to optimize the spectrum shape, suppress the interference noise and ensure the randomness and orthogonality of the desired waveform, a cognitive MIMO radar waveform optimization algorithm is proposed. Simulation results show that the effectiveness of the equivalent model and the optimization algorithm can significantly improve the accuracy and accuracy of the target parameter estimation. In this paper, the CS-MIMO radar signal model based on sparse random linear array is studied. The randomness of the position of the array elements is used to realize the compression observation, and it is proved that the constructed perceptual matrix satisfies the non-uniform reconstruction condition. In order to reduce the correlation between columns of the equivalent perceptual matrix and improve the sparse upper limit and reconstruction performance of the algorithm, the array is optimized based on simulated annealing algorithm, and the measurement matrix is optimized. Simulation results show that the optimized array can improve the probability and accuracy of target DOA reconstruction. The one-dimensional sparse random linear array is extended to 2-D L-matrix, and the CS-MIMO radar signal model based on sparse random L-matrix is studied. It is proved that the array guidance vector matrix can be equivalent to the measurement matrix and satisfy the condition of non-uniform reconstruction. The L matrix optimization method based on particle swarm optimization algorithm is studied to optimize the equivalent measurement matrix and further reduce the correlation between the columns of the perceptual matrix. Simulation results show that this method can effectively improve the performance of CS-MIMO radar two-dimensional angle estimation.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN958
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