Direction finding of MIMO radar through ESPRIT and Kalman filter

Abstract

A novel method employing an ESPRIT and Kalman filter is proposed for the one-dimensional direction finding problem of MIMO radar. The rotational invariance among the multiple equivalent virtual subarrays of MIMO radar is exploited to derive multiple estimates of the direction of each target through ESPRIT. The multiple estimates of each target are fused through a Kalman filter to refine the accuracy of estimation. Numerical examples demonstrate the effectiveness of the proposed method.Introduction: By transmitting multiple orthogonal signals via multiple sensors and separating their echoes through matched filter banks, MIMO radar can extend the array aperture by virtual sensors [1], from which the accuracy of direction finding would benefit. The computationally efficient ESPRIT algorithm has been applied to the one-dimensional direction finding problem of bistatic MIMO radar in [2] and the reference therein, which is also applicable to the one-dimensional direction finding problem of monostatic MIMO radar concerned in this Letter. However, the estimation and eigen-decomposition of the covariance matrix of the huge virtual sensor array as required by the method in [2] are still computational complex. In this Letter, the virtual sensor array of monostatic MIMO radar is decomposed into multiple identical virtual subarrays [3]. The rotational invariance among these virtual subarrays is exploited to derive multiple estimates of the one-dimensional direction of each target through a modified ESPRIT algorithm. Then, the multiple estimates are fused through a Kalman filter to refine the estimation accuracy. Moreover, the sample multiplexing based on the symmetric data structure of MIMO radar further improves the estimation accuracy. The proposed method is more computationally efficient and accurate than the method presented in [2].

Joint Transmitter–Receiver Optimization in Multitarget MIMO Radar

“…Thus, the orthogonal waveforms are usually utilized in the MIMO radar in order to provide an equivalent gain for the whole space and keep the echoes from the multiple targets uncorrelated to each other within a single pulse period. Thus, many direction finding algorithms employed in passive and active radars can be applied directly for the multitarget localization in MIMO radar such as Least Squares (LS), Capon, Amplitude and Phase EStimation (APES) [8], [9], ESPRIT [10], [11] and Iterative Adaptive Approach (IAA) [12] [18], etc. In [14] a multitarget localization method for MIMO radar with mutual target interference cancellation capabilities has been proposed, outperforming the existing methods.…”

Active jamming suppression based on transmitting array designation for colocated multiple‐input multiple‐output radar

“…MIMO radar is generally defined as a radar system with multiple linearly independent transmitted waveforms and joint processing signal received by multiple receive antennas [1–6]. MIMO radar can be either equipped with widely separated antennas [1–3] and colocated antennas [4–12]. The transmitting antennas of the distributed MIMO radar are widely separated so that each antenna can view a different aspect of the target.…”

“…The transmitting antennas of the colocated MIMO radar are closely spaced to view the same aspect of the target. It cannot provide spatial diversity, but can provide extra degrees‐of‐freedom (DOF) to increase the spatial resolution [5, 6] and the identification of the system [7, 8], improve the accuracy of the parameter estimation [9–11], and design the transmitting beampatterns flexibly [12–14].…”

Direction Finding for Bistatic MIMO Radar with Uniform Circular Array

“…Research on multiple-input multiple-output (MIMO) radar has been growing as evidenced by an increasing body of literature [1][2][3][4][5][6][7][8][9][10]. MIMO radar is characterized by using multiple antennas to simultaneously transmit orthogonal waveforms and multiple antennas to receive the reflected signals.…”

“…Many methods in bistatic MIMO radar are proposed to identify and locate multiple targets [3][4][5][6][7][8][9] in which both the transmit array and the receive array are uniform linear arrays (ULAs). In order to avoid angle search, estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm is applied to bistatic MIMO radar [3][4][5][6][7] by exploiting the invariance property of the transmit and receive arrays. In [8,9], several algorithms based on polynomial root finding procedure are proposed to estimate DOA and DOD of targets.…”