MIMO radar single measurement vector DOA estimation method based on iterative weighted near-end projection

Abstract

The invention discloses an MIMO (Multiple Input and Multiple Output) radar single measurement vector DOA (Direction of Arrival) estimation method based on iterative weighted near-end projection. The method comprises the following steps of: vectorizing a covariance matrix of received data after dimensionality reduction; constructing a weighting matrix by using high-order power of a covariance inverse matrix after dimensionality reduction so as to perform proper weight constraint on a sparse vector; establishing a weighted near-end function optimization model to represent a non-convex and non-smooth sparse optimization problem in MIMO radar single measurement vector DOA estimation; and finally, obtaining a near-end operator through an SCAD (Smoothly Clipped Absolute Deviation Penalty) function in an iteration process, and projecting the near-end operator to a feasible set to solve the weighted function optimization model so as to obtain a sparse solution, and obtaining a real target DOA estimation value by searching the position of a spectral peak. Compared with a reweighted l1-SVD algorithm and a weighted SL0 (Smoothed l0norm) algorithm, the method can obtain the better DOA estimation performance, and the prior information of the number of the targets is not needed to be known in advance.

Description

technical field [0001] The invention relates to the field of multiple-input multiple-output (MIMO) radar target parameter estimation, in particular to a DOA estimation method for MIMO radar single-measurement vectors based on iterative weighted near-end projection. Background technique [0002] Multiple Input and Multiple Output (MIMO) radar system is a new radar system proposed in recent years. Compared with phased array radar, MIMO radar is more effective in target detection, anti-jamming, target parameter estimation and target recognition. It has potential advantages and thus has attracted widespread attention. MIMO radar uses multiple transmitting antennas to simultaneously transmit mutually orthogonal signals at the transmitting end, and uses multiple receiving antennas at the receiving end to receive echo signals and process them with matched filters, thereby expanding the aperture of the MIMO radar array. According to different configurations of transceiver arrays, M...

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Problems solved by technology

However, the algorithm needs to predict the prior information of the number of targets when calculating the weighted vector. When the wrong judgment of the number of targets will lead to the decline of the DOA estimation performance of the algorithm

The above uses the SL0 algorithm and l 1 -In the method of SVD algorithm for MIMO radar DOA estimation, the non-convex and non-smooth sparse representation problem is usually approximated as a convex smooth function and a convex non-smooth problem for solution, so there is a certain degree of error in the sparse representation model, which leads to Its DOA estimation performance is not optimal

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