Abstract
In this paper, a Multiple Input Multiple Output (MIMO) radar system based on a sparse-array is proposed. In order to reduce the side-lobe level, a genetic algorithm (GA) is used to optimize the array arrangement. To reduce the complexity of the system, time-division multiplexing (TDM) technology is adopted. Since the signals are received in different periods, a frequency migration will emerge if the target is in motion, which will lead to the lower direction-of-arrival (DOA) performance of the system. To solve this problem, a stretching transformation method in the fast-frequency slow-time domain is proposed, in order to eliminate frequency migration. Only minor adjustments need to be implemented for the signal processing, and the root-mean-square error (RMSE) of the DOA estimation will be reduced by about 90%, compared with the one of an uncalibrated system. For example, a uniform linear array (ULA) MIMO system with 2 transmitters and 20 receivers can be replaced by the proposed system with 2 transmitters and 12 receivers, achieving the same DOA performance. The calibration formulations are given, and the simulation results of the automotive radar system are also provided, which validate the theory.
Highlights
Multiple-input multiple-output (MIMO) technology [1,2,3] has been widely used in imaging, detection and estimation domain
Imaging is an important application of MIMO radar; Ciuonzo [5] designed and analyzed the computational wideband time-reversal (TR) imaging algorithms, while Devaney [6] employed the methods of time-reversal imaging to image and locate the point targets obscured by an inhomogeneous background medium such as the ionosphere or foliage
Compared with the uniform linear array (ULA), both beamforming and DOA based on sparse-array [12] have more advantages, but considering the large influence on the antenna side-lobe caused by the sparse-array, many optimization methods of the sparse-array emerge
Summary
Multiple-input multiple-output (MIMO) technology [1,2,3] has been widely used in imaging, detection and estimation domain. In recent years, it has been used extensively in automotive radars [4]. Imaging is an important application of MIMO radar; Ciuonzo [5] designed and analyzed the computational wideband time-reversal (TR) imaging algorithms, while Devaney [6] employed the methods of time-reversal imaging to image and locate the point targets obscured by an inhomogeneous background medium such as the ionosphere or foliage. In [7], Cao et al applied the tensor-based methods to DOA estimation in a MIMO radar. Compared with the uniform linear array (ULA), both beamforming and DOA based on sparse-array [12] have more advantages, but considering the large influence on the antenna side-lobe caused by the sparse-array, many optimization methods of the sparse-array emerge
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