Abstract
Most angle estimation methods developed until this day require knowledge of the noise covariance matrix to be known or to be within a certain multiplicative constant. In this paper, to resolve the influence of unknown colored noise of the bistatic multiple input multiple output radar system for the joint direction of departure (DOD) and direction of arrival (DOA) estimation problem, a new denoising technique based on Hermitian transform differencing method is introduced. Then, by deriving a conjugate reduce dimension MUSIC estimator, the DOD and DOA of the target are obtained. The proposed method presents no virtual aperture loss which translates into improved performance and has low computational load over several competing methods. Lastly, the derivation of the stochastic Cramer-Rao bound for the joint angle estimation under consideration is presented. Numerical simulation conducted under varying conditions verifies the effectiveness of the proposed approach.
Highlights
The multiple-input multiple-output (MIMO) system is an antenna modelling technique which exploit multiple antennas to mutually emit orthogonal waveforms and collect the reflected echoes
As shown in fig.1, a uniform linear array-based narrowband bistatic MIMO radar system furnish with M transmitter antennas, and N receiver antennas with both transmit and receive arrays located in a plane are considered
MIMO radar configured with M = 7 transmit antennas and N = 8 receive antennas assumed to be a uniform linear array with half-wavelength element spacing is adopted as the system model
Summary
The multiple-input multiple-output (MIMO) system is an antenna modelling technique which exploit multiple antennas to mutually emit orthogonal waveforms and collect the reflected echoes. The tensor-based algorithm [9] and the parallel factor (PARAFAC) method [10] have been applied for angle estimation in bistatic MIMO radar systems These existing methods are most suitable in the case of an ideal scenario such as orthogonal waveform, calibrated sensor arrays, spatially uniform white noise etc. Motivated by the statistics property of the noise covariance matrix, we propose a new denoising technique with a modified subspace method for angle estimation in bistatic MIMO radar. In essence, the contribution of this paper is to introduce an imaginary transformation technique via a differencing procedure for noise elimination, resolve the joint 2D DOD and DOA estimation as a 1D pairwise problem, and propose a conjugate reduce dimension MUSIC algorithm to achieve high computational efficiency and performance.
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