Abstract
A joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation algorithm based on tensor subspace approach for partially calibrated bistatic multiple-input multiple-output (MIMO) radar is proposed. By exploiting the multidimensional structure of the received data, a third-order measurement tensor is constructed. Consequently, the tensor-based signal subspace is achieved using the higher-order singular value decomposition (HOSVD). To achieve accurate DOA estimation with partially calibrated array, a closed-form solution is provided to estimate the gain-phase uncertainties of the transmit and receive arrays by modeling the imperfections of the arrays. Simulation results demonstrate the effectiveness of the proposed calibration algorithm.
Highlights
Array signal processing, which includes parameter estimation [1, 2] and beamforming [3, 4] has been widely investigated in past decades [5] which has found various applications in radar, sonar, and satellite navigation system
Several experimental simulations are carried out to demonstrate the performance of the proposed method
A bistatic multiple-input multiple-output (MIMO) radar with Mt = 6 transmit antennas and Mr = 8 receive antennas is used in the following simulations
Summary
Array signal processing, which includes parameter estimation [1, 2] and beamforming [3, 4] has been widely investigated in past decades [5] which has found various applications in radar, sonar, and satellite navigation system. In [8], the rotational invariance property of the MIMO radar is exploited which enables the DOD and DOA estimation with bistatic MIMO radar This method determines the DODs and DOAs using two independent estimation of signal parameters via rotational invariance technique (ESPRIT) and requires an extra procedure to achieve the paired DOD and DOA estimation. To avoid the pair matching procedure, the relationship between the two independent ESPRIT is exploited in [9] to estimate the DODs and DOAs. In [10], a reduceddimension MUltiple SIgnal Classification (MUSIC) algorithm is proposed which only requires one-dimensional peak search. Based on the double polynomial root-finding procedure, a polynomial root-finding technique for joint DOD and DOA estimation is proposed in [11] This algorithm allows an efficient estimation with automatic pairing. The DOAs can be achieved from the eigenvector which is related to the corresponding DODs
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