Sparse Parameter Estimation and Imaging in mmWave MIMO Radar Systems With Multiple Stationary and Mobile Targets

Abstract

This work conceives novel target detection and parameter estimation schemes in millimeterwave (mmWave) multiple-input multiple-output (MIMO) radar (mMR) systems for both stationary and mobile targets/radar platform. Initially, the orthogonal matching pursuit (OMP)-based mmR (OmMR) algorithm is proposed for stationary targets to estimate their radar cross-section (RCS) coefficients, angle, range locations together with the number of targets. Next, mMR systems with mobile targets and platform are considered, followed by development of the simultaneous OMP (SOMP)-based mMR (SmMR) algorithm for RCS, angle/range estimation together with their Doppler velocities. The proposed algorithms lead to a significant improvement in performance since they exploit the inherent sparsity of the mMR scattering scene in contrast to the conventional schemes. Two-dimensional (2D) mMR imaging procedures are also presented for both scenarios in the angle, range, and Doppler dimensions. Analytical expressions are derived for the Cramér-Rao bounds (CRBs) for the mean-squared error (MSE) of joint estimation of the RCS coefficients and Doppler velocities. Simulation results demonstrate that proposed schemes perform well even in low signal-to-noise ratio (SNR) scenarios with a few snapshots of the scattering environment and yield improved performance in comparison to existing sparse as well as non-sparse schemes.

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“…The FFT algorithm does not require multiple snapshot signals, but its angle resolution is poor with a limited number of array antennas. Orthogonal Matching Pursuit (OMP) is a signal reconstruction algorithm that requires the number of targets to be known in advance [23,24]. IAA is a spectral estimation algorithm based on weighted least squares (WLS) estimation proposed by Li Jian at the University of Florida in 2010 [25][26][27], which has demonstrated good results in linear aperture experiments.…”