Range-Doppler imaging via sparse representation

Abstract

We pose the range-Doppler imaging problem as a two-dimensional sparse signal recovery problem with an overcomplete basis. The resulting optimization problem can be solved using both ℓ0 and ℓ1 norm minimization algorithms. Algorithm performance and estimation quality are illustrated using artificial data set, where targets are close to each other and target SNR is low. We show that accurate target location is achieved with high resolution. In particular, compared to other state-of-art algorithms, the proposed approach is shown to achieve more robustness in noisy environment with limited data.

Efficient two-dimensional compressive sensing in MIMO radar

“…Recently, a new signal model with two-dimensional (2D) sparse parameters, called 2D sparse signal model, has been introduced [17,18], and some algorithms for 2D sparse reconstruction have been proposed [19][20][21][22][23][24]. Specifically, in [19], the 2D version of IAA is derived in which its computational cost is drastically reduced in comparison with the one-dimensional (1D) IAA.…”

“…The other 2D sparse recovery algorithm is the smoothed L0 (SL0) algorithm that minimizes an approximated l 0 -norm function [20] and has much lower computational complexity than its 1D counterpart. In [21], a 2D sparse signal model for a radar is obtained and solved by 2D-SL0 algorithm with acceptable results. Also, 2D-SLIM [22], 2D Truncated Newton Interior Point Method (2D-TNIPM) [23], and 2D Sparse Bayesian Learning using Laplace Prior (2D-SBL-LP) [24] have been proposed for pulse Doppler MIMO radars.…”

“…The main goal of sparsity-based methods for MIMO radar systems ( [21][22][23][24]) is to achieve accurate estimates for target parameters, whereas in this paper, we mainly focus on the CS MIMO radar problem to reduce the sampling rate lower than the Nyquist criterion by designing a suitable measurement matrix. Therefore, the main difference between our 2D CS MIMO radar signal model and 2D sparse model in [21][22][23][24] is that we consider the measurement matrices in our model in which these measurement matrices can be applied on receivers and received pulses, separately.…”

“…Therefore, the main difference between our 2D CS MIMO radar signal model and 2D sparse model in [21][22][23][24] is that we consider the measurement matrices in our model in which these measurement matrices can be applied on receivers and received pulses, separately.…”

Two-dimensional SLIM with application to pulse Doppler MIMO radars

“…In this algorithm, a discontinuous l 0 -norm function is approximated by a continuous one and then a sparse solution is reached using the steepest ascent algorithm followed by a projection onto a feasible set [24][25][26][27]. In [28], this algorithm has been applied to pulse Doppler radars with a lot of advantages such as target velocity extraction and pulse integration. However, this algorithm has been presented by using an approximated l 0 -norm function for which we will later show that it achieves a lower performance in sparse signal recovery at low signal-to-noise ratios (SNRs) or for a small number of pulses compared to the SLIM algorithm.…”