A reduced-dimension space time adaptive processing (STAP) for airbornemultiple-inputmultiple-output (MIMO) radar based on three iterations is proposed. The optimum filter for MIMO-STAP is described as a separable fi...
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A reduced-dimension space time adaptive processing (STAP) for airbornemultiple-inputmultiple-output (MIMO) radar based on three iterations is proposed. The optimum filter for MIMO-STAP is described as a separable filter, and the optimal weight vector in high dimension can be iteratively solved by three lower dimension weight vectors. Consequently, the quite high sample support and computational complexity in optimal MIMO-STAP can be efficiently reduced. For short data records, the proposed method can achieve better clutter suppression performance. Simulation results demonstrate the effectiveness of the proposed method.
A method for selecting auxiliary channels in reduced-dimension space-time adaptive processing (STAP) is proposed for airborne multiple-input multiple-output radar. The auxiliary channel selection of the proposed appro...
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A method for selecting auxiliary channels in reduced-dimension space-time adaptive processing (STAP) is proposed for airborne multiple-input multiple-output radar. The auxiliary channel selection of the proposed approach is data dependent. Based on maximum cross-correlation energy metric, the significance of each spatial-Doppler channel is evaluated, and the auxiliary channels are selected step-by-step through utilising iteration. For the sake of achieving better performance as much as possible, the proposed approach will select two auxiliary channels at the first step, and select one channel at the next each step. Due to that the explicit physical meaning is very important for a STAP algorithm, the physical meaning of the maximum cross-correlation energy metric is discussed, and the fact that the local optimal output signal-to-interference-noise (SINR) performance can be assured by the maximum cross-correlation energy metric is proved theoretically. The simulations demonstrated that the output SINR loss of the proposed approach is about -1.9 dB when only two auxiliary channels are selected. Consequently, the proposed approach can reduce the requirement of the sample support dramatically. This will be more obvious advantage for the practical application in heterogeneous clutter environments where the number of secondary samples is extremely limited.
This paper investigates the robust cognitive joint design of transmit waveform and receive filter to improve the system performance of airbornemultiple-inputmultiple-output (MIMO) radar. Considering the target Doppl...
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This paper investigates the robust cognitive joint design of transmit waveform and receive filter to improve the system performance of airbornemultiple-inputmultiple-output (MIMO) radar. Considering the target Doppler frequency and spatial cone angle uncertainties are present, we formulate the averaged signal-to-clutter-plus-noise ratio (SCNR) as the optimization goal. Specifically, the averaged SCNR is maximized under only constant modulus constraint and that under constant modulus and similarity constraints. Four iterative optimization algorithms are developed to deal with the joint design problem. The first kind of iterative optimization algorithm is based on semidefinite programming (SDP) relaxation and randomization or rank-one decomposition techniques. The second kind of computational efficient iterative optimization algorithm utilizes the fractional programming and power method-like iteration. The proposed algorithms can achieve a monotonic output SCNR enhancement and are robust against the inaccuracies of target parameters. Several simulations results are implemented to validate the superiority of the proposed algorithms considering the output SCNR, space-time beampattern and computational complexity. (C) 2020 Elsevier Inc. All rights reserved.
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