Bistatic MIMO radar angle estimation method under joint error condition

Abstract

The invention discloses a bistatic MIMO radar angle estimation method under a joint error condition, comprising the following steps: performing matched filtering processing on data of a receiving array, and constructing one three-dimensional tensor model; then decomposing the three-dimensional tensor model by adopting a COMFAC algorithm and performing iterative computation on a receiving directionmatrix, a transmitting direction matrix and a target covariance matrix by utilizing an alternating least square method until convergence conditions are met, thus estimated values of the receiving direction matrix, the transmitting direction matrix and the target covariance matrix are obtained; and finally estimating DOD and DOA of a target by utilizing a least square method by virtue of a guidance vector of an auxiliary array element. The estimation method disclosed by the invention utilizes an array measurement multidimensional structure, so that precision of the estimation method is obviously better than that of a Reduced-MUSIC method; meanwhile, computational complexity of the estimation method is far below that of the Reduced-MUSIC method, and efficiency of the estimation method is higher than that of the Reduced-MUSIC method.

Description

technical field [0001] The invention relates to a radar signal processing technology, in particular to a bistatic MIMO radar angle estimation method under joint error conditions. Background technique [0002] Multiple-input multiple-output (MIMO) radar is the mainstream direction of next-generation radar. In bistatic MIMO radars, joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is a fundamental task for target location. At present, many algorithms with excellent performance have been developed, such as spectral peak search algorithm, rotation invariant algorithm, maximum likelihood estimation algorithm, parallel factor algorithm and so on. Among them, the parallel factor (PARAFAC) algorithm is the most eye-catching in terms of estimation accuracy, because it makes full use of the inherent multi-dimensional structure of radar matched filtering data. Usually, the transceiver array is assumed to be deterministic in existing algorithms. However, th...

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Problems solved by technology

On the other hand, singular value decomposition (singular value decomposition, SVD) is not suitable for large transceiver arrays

Also, spectral peak search is computationally inefficient

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