Low-Complexity Off-Grid STAP Algorithm Based on Local Search Clutter Subspace Estimation

2020

IEEE Access

Sparse recovery (SR) based space-time adaptive processing (STAP) has attracted much attention due to its small requirement of snapshots in the estimation of the clutter plus noise covariance matrix (CNCM). However, most of the existing SR STAP methods suffer from the grid mismatch effect of the dictionary matrix. In this paper, a novel grid-less total variation minimization (TVM) based STAP approach is proposed, which avoids the discretization of the spatial-temporal profile and possible mismatch of the spatialtemporal gird. The optimization problem is firstly introduced to estimate the clutter subspace steering vector by minimizing the defined atomic norm based on the TVM. Then the optimization problem is reformulated via utilizing the property of the radar space-time steering vector and approximation of the Bessel function. Finally, with a solution obtained by the optimization problem, a projection method is presented to obtain an accurate estimation of the CNCM. The proposed STAP method can be applied for both the side-looking and non-side-looking case. Numerical results validate its effectiveness compared with the other SR STAP methods.

Section: Simullation Resultsmentioning

confidence: 99%

Section: Simullation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Grid-Less Total Variation Minimization-Based Space-Time Adaptive Processing for Airborne Radar

2020

IEEE Access

“…To mitigate this off-grid effect, several remedies [29][30][31][32][33][34] including grid-based and gridless STAP algorithms have been proposed most recently. Generally, the grid-based remedies [29][30][31][32][33] attempt to generate new grid points that match the location of the clutter ridge by dictionary learning [29], local searching [30][31][32], or prior platform information [33]. Nevertheless, discretisation is still assumed in [29][30][31][32][33], and hence the off-grid effect cannot be avoided completely.…”

Section: Introductionmentioning

confidence: 99%

A fast and gridless STAP algorithm based on mixed‐norm minimisation and the alternating direction method of multipliers

2021

IET Radar Sonar & Navi

Sparse recovery (SR) based space-time adaptive processing (STAP) methods have received much attention recently due to their dramatically reduced requirements of training samples. However, most of the existing SR-STAP algorithms suffer from the offgrid effect induced by the discretisation of angle-Doppler plane. To eliminate this effect and improve the performance of clutter suppression, a novel gridless mixed-norm minimisation (MNM) based STAP algorithm is proposed. Unlike previous grid-based SR-STAP methods, the clutter covariance matrix (CCM) is estimated in continuous domain by solving a semidefinite programme (SDP), which is established by utilising the properties of the CCM and the compact formulation of MNM. Furthermore, to reduce the computational burden of solving this SDP, a fast iterative algorithm via the framework of the alternating direction method of multipliers (ADMM) is also derived, where the unknown parameters are iteratively updated with closed-form expressions. Simulation results show that our proposed algorithm achieves better performance of clutter suppression and target detection with lower computational complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

“…We also note that, the sparsity-based STAP has been recently extended to various application scenarios, such as: STAP in the extremely heterogeneous environment where only one training sample is available [40], [41], STAP for different radar system [42]- [45], and STAP methods to solve the problem of off-grid [46]- [48]. The optimization problems in [40]- [48] are different from that in this paper since here we focus on the performance improvement of clutter suppression and target detection for the ordinary airborne radar via the structure-aware two-level block sparsity when a few training samples are available.…”

Section: Introductionmentioning

confidence: 99%

Space-Time Adaptive Processing by Employing Structure-Aware Two-Level Block Sparsity

Jiang

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

et al. 2021

Traditional radar space-time adaptive processing (STAP) cannot efficiently suppress heterogeneous clutter because of a small number of independent and identically distributed training snapshots. In the paper, we propose a new STAP approach exploiting structure-aware two-level block sparsity (STBS) of radar echoes, namely STBS-STAP. It enhances the performance on clutter suppression and target detection with limited training snapshots. The clutter angle-Doppler profile always appears in a continuous diagonal clustering structure and the radar echoes at the adjacent range cells commonly share the same sparse pattern. STBS-STAP employs STBS, i.e., both the diagonal clustering structure and the common sparsity property, to acquire a precise clutter covariance matrix estimation. Thus, the new STBS-STAP achieves better performance on clutter suppression compared with existing STAP methods with a small number of training samples. Besides, STBS-STAP achieves superior target detection performance due to the precise estimation of the statistical properties of the clutter. The superiority of STBS-STAP is verified by experiments on both simulated data and measured Mountain-Top data.