A new Unitary ESPRIT algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar is proposed. The properties of centro-Hermitian matrices are utilised to transform the complex-valued data matrix into a realvalued data matrix. Then the real-valued rotational invariance equations for signal subsapce are figured out to estimate DOAs and DODs which are paired automatically in a new way. The proposed algorithm provides increased estimation accuracy with reduced computational complexity owing to real-valued processing and double the number of data samples inherently in unitary ESPRIT. Simulation results are presented to verify the effectiveness of the proposed algorithm.Introduction: Multiple-input multiple-output (MIMO) radars [1], which use multiple antennas to simultaneously transmit diverse waveforms and also use multiple antennas to receive the reflected signals, have many potential advantages over conventional phased-array radars. Direction of departure (DOD) and direction of arrival (DOA) estimation is a key issue in bistatic MIMO radar. In [2], an ESPRIT algorithm by exploiting the invariance property of both the transmit array and the receive array is developed, but an additional pair matching is required. When a MIMO array is large, another problem of heavy computational complexity must be solved. In order to reduce computational complexity, the beamspace ESPRIT (B-ESPRIT) [3] algorithm which focuses on a particular directional sector of interest is developed. However, there are three drawbacks of B-ESPRIT. First, if there is no a priori information on the general angular location, B-ESPRIT cannot be used. Secondly, it has some loss of performance of angle estimation. Finally, an additional pair matching is also required. In this Letter, we derive a novel Unitary ESPRIT algorithm with real-valued processing for joint DOD and DOA estimation which has the following advantages: (a) it has lower computational complexity than ESPRIT; (b) it has slightly better angle estimation performance than ESPRIT; (c) automatic pairing for DOD and DOA estimation can be obtained.
A new linear sparse array based on the nested array is proposed, which enjoys all the good properties of the two-level nested array, and can provide more degrees of freedom (DOF). The new array is constructed by two uniform linear arrays (ULAs) and an additional sensor. The sensor locations, the array aperture, and the achievable DOF from its difference co-array (DCA) are all benefited for closed-form expressions. Furthermore, the resulting DCA is kept as a hole-free ULA. The optimal numbers of sensors in the two ULAs provided the total number of physical sensors are derived. This new array can resolve more sources and achieve better angle estimation performance than the two-level nested array. Simulation results validate these conclusions.
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