Sparse ground-penetrating radar imaging method for off-the-grid target problem

Abstract

Abstract. Spatial sparsity of the target space in subsurface or through-the-wall imaging applications has been successfully used within the compressive-sensing framework to decrease the data acquisition load in practical systems, while also generating highresolution images. The developed techniques in this area mainly discretize the continuous target space into grid points and generate a dictionary of model data that is used in image-reconstructing optimization problems. However, for targets that do not coincide with the computation grid, imaging performance degrades considerably. This phenomenon is known as the off-grid problem. This paper presents a novel sparse ground-penetrating radar imaging method that is robust for off-grid targets. The proposed technique is an iterative orthogonal matching pursuit-based method that uses gradientbased steepest ascent-type iterations to locate the off-grid target. Simulations show that robust results with much smaller reconstruction errors are obtained for multiple off-grid targets compared to standard sparse reconstruction techniques.

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

“…Moreover, the signal recovery is robust to the mismatch in the sense that the recovery error grows with the mismatch level and is independent of the sparsity of the original signal. Thus, the sparse reconstruction performance of radar imaging degrades severely [3,9,[14][15][16][17].…”

“…One simple approach is to use multiresolution refinement strategy and decrease the grid size iteratively [18]. Nevertheless, a finer grid may enhance the coherence between the columns of dictionary and increase the computational complexity and numerical instability of reconstruction [17]. Modeling the off-grid as a multiplicative perturbation, the sparse total least squares (S-TLS) [19] and joint correlation-parameterization (CP) [3] algorithms are proposed.…”

Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning

“…The effect of general dictionary mismatch, which is the direct consequence of off-grid effect, is analyzed in [7][8][9][10]. This mismatch causes the performance of conventional sparse reconstruction methods to degrade considerably [3,6,7,[11][12][13][14]. An intuitive way to sidestep off-grid effect is to work directly on the continuous parameter space, that is, atomic norm minimization approach [15], continuous basis pursuit (CBP) [16].…”

“…One simple approach is to use refinement strategy and decrease the grid size [17]. Nevertheless, a finer grid may enhance the coherence between the columns of dictionary and increase the computational complexity and instability of reconstruction [14]. Modeling the off-grid problem as a multiplicative perturbation, the sparse total least squares (S-TLS) [18] and joint correlation-parameterization (JCP) [3] algorithms are proposed.…”

Sparse representation discretization errors in multi-sensor radar target motion estimation

“…In radar applications, such as collision avoidance radars (Azodi et al, 2014;Wächter et al, 2014), assuming a very fine discretization is not even practical as out-bound targets 1 considerably enlarge the solution domain. Targets, whose true motion states lie offside the grid points of the discretized solution domain, are commonly referred to as off-grid targets (Tan and Nehorai, 2014;Nielsen et al, 2012;Tang et al, 2012;Gurbuz et al, 2013). It is beneficial to analyze the impact of off-grid targets on the CS recovery process and to modify it, accordingly.…”