Kalman filtering method for sparse off‐grid angle estimation for bistatic multiple‐input multiple‐output radar

Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

“…Having obtained an approximated signal from (13), the received signal is then represented in the sparse domain so as to exploit its target sparsity in the scenario with gain-phase errors. To begin with, a discretized set, S ∅ , S θ , is formed that is made up of all the possible angles of the target as follows [20,25]:…”

“…To begin with, the angle estimation for the targets requires the recovery of the sparse vector S from the obtained signal subspace Z, but the exact solution of the optimization problem in (18) is nondeterministic hard. Hence, we employ the CS-based SOMP method [25,29,39] to obtain the sparse matrix and subsequently estimate the DOD/DOA of the targets. SOMP is a greedy approach that adopts a multiple measurement vector technique to reconstruct sparse vectors.…”

“…CS-based methods have proven to present better performance even with a low number of snapshots and signal-to-noise ratio in addition to estimation accuracies for correlated targets than the traditional methods [20][21][22]. In [23], the CS sparse representation methods, orthogonal matching pursuit method (OMP), multiple OMP, optimised OMP [24], and simultaneous OMP (SOMP) [25] were proposed to resolve the angle estimation problem at the expense of low computation. e fast iterative shrinkagethresholding algorithm (FISTA) [26] and the CS sparse Bayesian learning [27] have also been applied to realize high target resolution.…”

“…In [29], an iterative CS method is proposed to resolve the mutual coupling between antenna arrays. However, the methods in [24][25][26][27][28][29] suffer from high computational complexity and hence difficult to apply in practical scenarios.…”

Hermitian Transform-Based Differencing Approach for Angle Estimation for Bistatic MIMO Radar Under Unknown Colored Noise Field

“…In [6], the more efficient estimation of signal parameters via rotational invariance technique (ESPRIT) method, which activates the invariance property to gain a closed-form solution for DOD and DOA estimation is presented. Then [7,8] adopted a sparsity-aware technique at the penalty of the computational grid search, although presents improved performance over the subspace-based methods for angle estimation. Further, the tensor-based algorithm [9] and the parallel factor (PARAFAC) method [10] have also been applied for angle estimation in bistatic MIMO radar systems.…”