Radar Coincidence Imaging for Off-Grid Target Using Frequency-Hopping Waveforms

Xiaoli Zhou,Haowen Chen,Yongqiang Cheng,Hongqiang Wang,Yuliang Qin

doi:10.1155/2016/8523143

Abstract

Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of the target relative motion. To achieve better imaging performance, sparse reconstruction is commonly used. While its performance is based on the assumption that the scatterers are located at the prediscretized grid-cell centers, otherwise, off-grid emerges and the performance of RCI degrades significantly. In this paper, RCI using frequency-hopping (FH) waveforms is considered. The off-grid effects are analyzed, and the corresponding constrained Cramér-Rao bound (CCRB) is derived based on the mean square error (MSE) of the “oracle” estimator. For off-grid RCI, the process is composed of two stages: grid matching and off-grid error (OGE) calibration, where two-dimension (2D) band-excluded locally optimized orthogonal matching pursuit (BLOOMP) and alternating iteration minimization (AIM) algorithms are proposed, respectively. Unlike traditional sparse recovery methods, BLOOMP realizes the recovery in the refinement grids by overwhelming the shortages of coherent dictionary and is robust to noise and OGE. AIM calibration algorithm adaptively adjusts the OGE and, meanwhile, seeks the optimal target reconstruction result.

Highlights

Radar coincidence imaging (RCI), originated from the optical coincidence imaging, is a staring imaging technique which can obtain focused high-resolution image without the limitation of the target relative motion [1,2,3]
In RCI, the continuous target space needs to be discretized to a fine grid and the target-scattering centers are assumed to be exactly located at these prediscretized grid-cell centers [3]
This paper investigates the high-resolution off-grid RCI using FH waveforms

Summary

Introduction

Radar coincidence imaging (RCI), originated from the optical coincidence imaging, is a staring imaging technique which can obtain focused high-resolution image without the limitation of the target relative motion [1,2,3]. Sparse recovery approaches and compressive sensing (CS) [7, 8] are suitable for RCI by exploiting the sparsity of target in the target space. The merit of SBL is its flexibility in modeling sparse signals that can promote the sparsity of its solution and exploit the possible structure of the signal to be recovered [21], whereas it offers few guarantees on the signal recovery accuracy. Another way to sidestep the off-grid is to work directly on the continuous parameter space. ‖ ⋅ ‖2 denotes the Euclidean norm of a vector

Problem Formulation

Off-Grid Effects

Image Reconstruction Approach

Discussion and Numerical

Conclusion