Multiple-Parameter Estimation Method Based on Spatio-Temporal 2-D Processing for Bistatic MIMO Radar

Abstract

A novel spatio-temporal 2-dimensional (2-D) processing method that can jointly estimate the transmitting-receiving azimuth and Doppler frequency for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise and an unknown number of targets is proposed. In the temporal domain, the cross-correlation of the matched filters’ outputs for different time-delay sampling is used to eliminate the spatial colored noise. In the spatial domain, the proposed method uses a diagonal loading method and subspace theory to estimate the direction of departure (DOD) and direction of arrival (DOA), and the Doppler frequency can then be accurately estimated through the estimation of the DOD and DOA. By skipping target number estimation and the eigenvalue decomposition (EVD) of the data covariance matrix estimation and only requiring a one-dimensional search, the proposed method achieves low computational complexity. Furthermore, the proposed method is suitable for bistatic MIMO radar with an arbitrary transmitted and received geometrical configuration. The correction and efficiency of the proposed method are verified by computer simulation results.

Fast Noncircular 2D-DOA Estimation for Rectangular Planar Array

“…Two-dimensional direction of arrival (2D-DOA) estimation has been widely used in mobile communication systems, sonar, navigation, radar, etc. [ 1 , 2 , 3 , 4 , 5 ], which is an important research branch in array signal processing. Many 2D-DOA estimation algorithms have sprung up in recent years in order to improve the performance of angle estimation, which include the two dimensional multiple signal classification(2D MUSIC) algorithm [ 6 ], the 2D Unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm [ 7 ], the modified 2D-ESPRIT algorithm [ 8 ], the matrix pencil method [ 9 ], the maximum likelihood method [ 10 , 11 ], the parallel factor (PARAFAC) algorithm [ 12 ], and so on [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Joint DOA and Doppler frequency estimation of coherent and/or same‐direction signals

“…A series of estimation methods have been proposed and widely applied to multiple fields such as radar, sonar, radio astronomy, seismic prospecting, and mobile communications. In the literature, various estimators have been developed such as linear prediction-based method, 1 the maximum likelihood (ML) method, 2-6 estimation of signal parameters via rotational invariance technique-based algorithms, [7][8][9][10][11][12] multiple signal classification (MUSIC)-based algorithms, [13][14][15][16] and united estimation algorithm based on compressed sensing. 17,18 The ML method is an optimal estimation method, which requires multidimensional exhaustive search.…”

Parameter Estimation of a Ground Moving Target Using Image Sharpness Optimization

“…The target’s moving parameters, such as radial velocity, along-track velocity, and original azimuth position, play an important role in monitoring ground vehicles. Using these parameters, we can reposition them to the true azimuth location and extrapolate a target’s future position [7,8]. Generally, the interferometric phase is employed to determine the radial velocity while the along-track velocity is acquired by the target’s Doppler parameters.…”