Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar’s estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method.

Section: Introductionmentioning

confidence: 99%

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar

Zhang

Zeng

et al. 2016

“…Direction of arrival (DOA) estimation of multiple targets from the received data is an important aspect of array signal processing [ 4 , 5 , 6 ] and MIMO radar applications in practice [ 7 , 8 , 9 , 10 , 11 ]. Some subspace-based methods have been proposed for DOA estimation in MIMO radar, such as the multiple signal classification (MUSIC) algorithm [ 8 ], the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm [ 9 ] and tensor analysis-based algorithms [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar

Wang

et al. 2015

In this paper, a real-valued covariance vector sparsity-inducing method for direction of arrival (DOA) estimation is proposed in monostatic multiple-input multiple-output (MIMO) radar. Exploiting the special configuration of monostatic MIMO radar, low-dimensional real-valued received data can be obtained by using the reduced-dimensional transformation and unitary transformation technique. Then, based on the Khatri–Rao product, a real-valued sparse representation framework of the covariance vector is formulated to estimate DOA. Compared to the existing sparsity-inducing DOA estimation methods, the proposed method provides better angle estimation performance and lower computational complexity. Simulation results verify the effectiveness and advantage of the proposed method.

“…Approaches for direction of arrival (DOA) estimation have been widely studied [ 1 , 2 , 3 , 4 , 5 , 6 ]. In recent years, sparse representations and reconstruction theory have also been applied to DOA estimation [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. The ability of multi-source resolution and efficient estimation in a few snapshots are two important advantages of DOA estimation using sparse theory.…”

Section: Introductionmentioning

confidence: 99%

A Modified Rife Algorithm for Off-Grid DOA Estimation Based on Sparse Representations

Chen

Guo

et al. 2015

In this paper we address the problem of off-grid direction of arrival (DOA) estimation based on sparse representations in the situation of multiple measurement vectors (MMV). A novel sparse DOA estimation method which changes MMV problem to SMV is proposed. This method uses sparse representations based on weighted eigenvectors (SRBWEV) to deal with the MMV problem. MMV problem can be changed to single measurement vector (SMV) problem by using the linear combination of eigenvectors of array covariance matrix in signal subspace as a new SMV for sparse solution calculation. So the complexity of this proposed algorithm is smaller than other DOA estimation algorithms of MMV. Meanwhile, it can overcome the limitation of the conventional sparsity-based DOA estimation approaches that the unknown directions belong to a predefined discrete angular grid, so it can further improve the DOA estimation accuracy. The modified Rife algorithm for DOA estimation (MRife-DOA) is simulated based on SRBWEV algorithm. In this proposed algorithm, the largest and sub-largest inner products between the measurement vector or its residual and the atoms in the dictionary are utilized to further modify DOA estimation according to the principle of Rife algorithm and the basic idea of coarse-to-fine estimation. Finally, simulation experiments show that the proposed algorithm is effective and can reduce the DOA estimation error caused by grid effect with lower complexity.