分布式MIMO雷达：自适应稀疏恢复算法提升运动目标成像

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"分布式MIMO雷达的运动目标自适应稀疏恢复算法是一种针对运动目标成像的技术，旨在在目标以未知速度移动时提高分辨率和恢复性能。该方法利用了分布式MIMO雷达系统的优势，通过提出一种新的自适应稀疏恢复策略，实现对目标速度和图像的同时估计，且具有较高的计算效率。算法的核心是通过迭代机制，交替最小化Lp范数和恢复误差，从而更新图像估计并确定速度。数值模拟证明，即使在低信噪比环境下，该算法也能提供高分辨率图像和精确的速度估计。关键词包括：自适应稀疏表示、分布式MIMO雷达、运动目标成像以及FOCal Underdetermined System Solver（FOCUSS）。文章介绍了分布式MIMO雷达在运动目标成像中的应用，这种系统因其高分辨率而受到广泛关注。传统的雷达系统在目标运动速度未知时可能面临性能下降的问题，而提出的自适应稀疏恢复算法则有效地解决了这一挑战。算法的设计思路是利用MIMO雷达的多个输入和多个输出，通过分布式的天线阵列捕获更丰富的信息。在每一步迭代中，算法优化Lp范数以促进稀疏性，同时减少恢复误差，以达到对目标位置和速度的精确估计。 FOCUSS（FOCal Underdetermined System Solver）作为一种解算器，在这个算法中起到了关键作用。它是解决欠定系统的工具，能够处理数据不足的情况，对于分布式MIMO雷达系统中复杂的信号处理问题尤其适用。通过FOCUSS，算法能够在数据不足的情况下仍能有效地重建图像并估计目标的运动状态。在实际应用中，低信噪比通常会降低雷达系统的性能。然而，文中提到的自适应稀疏恢复算法在数值仿真中表现出色，即使在恶劣的信噪比条件下，依然可以同时获得高质量的图像和准确的目标速度。这表明该算法具有较强的鲁棒性，能够适应各种复杂的环境条件，为分布式MIMO雷达系统提供了更强大的运动目标探测和跟踪能力。总结来说，这篇论文提出的自适应稀疏恢复算法为分布式MIMO雷达在处理运动目标成像时提供了新的解决方案。通过高效的计算策略和迭代优化，该算法能够在目标速度未知的情况下保持高分辨率和恢复性能，对于未来雷达技术的发展和应用具有重要的理论与实践意义。"

Adaptive Sparse Recovery of Moving Targets

for Distributed MIMO Radar

Hui Yu

1, a

, Guanghua Lu

1,b

Hailong Zhang

1,c

University of Science and Technology of China, Hefei, Anhui, P.R.China

yuhuimia@mail.ustc.edu.cn,

lugh@ustc.edu.cn,

hailong@mail.ustc.edu.cn

Keywords: Adaptive sparse representation, distributed multiple-input multiple-output(MIMO) radar,

moving target imaging, FOCal Underdetermined System Solver(FOCUSS).

Abstract. The high resolution and better recovery performance with distributed MIMO radar would

be significantly degraded when the target moves at an unknown velocity. In this paper, we propose

an adaptive sparse recovery algorithm for moving target imaging to estimate the velocity and image

jointly with high computation efficiency. Numerical simulations are carried out to demonstrate that

the proposed algorithm can retrieve high-resolution image and accurate velocity simultaneously even

in low SNR.

Introduction

The distributed multi-input multi-output(MIMO) radar system has attracted a lot of attentions due to

its high resolution for imaging[1]. It employs widely separated antennas within the transmit and

receive aperture to exploit the spatial diversity. One of its applications is imaging of moving targets.

It is necessary to estimate the velocity of moving targets. The reconstructed targets will be blurred

when we ignores the velocity information[2] as the observation matrix will be affected by the

velocity[3]. In [4], an over complete dictionary(OCD) method is used. It linearizes the forward model

with every possible velocity and solves the moving target problem as a larger, unified regularized

inversion problem. Obviously, it leads to a huge computational burden. In[5], an iterative algorithm

named adaptive subspace pursuit(ASP) is proposed. It linearizes the dictionary matrix at local

neighborhood of the velocity with Taylor expansion, then obtains the new velocity by alternatively

estimating. However, the performance of ASP relies on a priori knowledge of the target’s sparse

degree and the initial velocity which is difficult to know in priori for non-cooperative targets. Without

the priori knowledge of target, ASP fails.

High resolution is another aspect we must care about. Compressive sensing(CS) has shown good

performance in radar imaging recently for its promotion of resolution[6]. Under the assumption that

the scatters of the targets are sparsely distributed, which is the usual case, it can provide higher

resolution than the traditional IDFT method. FOCal Underdetermined System Solver(FOCUSS)[7]

is an implementation of CS. It is shown that FOCUSS can not only recover static image accurately

without a priori knowledge of the targets but also has a good performance even in noisy environments.

In this paper, an adaptive sparse recovery via velocity estimation(ASRVE) method based on

FOCUSS is devised. With an iteration mechanism, the proposed method updates the image and

estimates the velocity alternately by sequentially minimizing the norm and the recovery error.

ASRVE can recover the image and estimate the velocity accurately without a priori knowledge of

target while it is with high computation efficiency.

The next of the paper is as follows. In section 2, the signal model is given and the imaging problem

is formulated. In Section 3, the proposed adaptive sparse recovery algorithm is presented. In Section

4, some experimental results are given to verify the algorithm. Finally, some conclusions are drawn

in Section 5.

Signal Model for a Distributed MIMO Radar System

Consider a distributed MIMO radar system with M transmitters and N receivers[8] which are located

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