Changchang Liu

Sparse imaging for passive radar system based on digital video broadcasting satellites

This paper studies passive radar imaging system based on digital video broadcasting (DVB) satellites. Firstly, the 3-dimensional (3-D) imaging model which consists of multiple DVB satellites and one receiver is established. After analyzing in the wavenumber domain, we consider to exploit the sparsity of the target to realize high-resolution imaging from the undersampled wavenumber domain coverage. However, traditional compressive sensing (CS) radar imaging methods require the imaging scene to be pre-discretized into finite grids and all scatterers to be located exactly on the grid. And they are likely to be severely affected by the off-grid scatterers. Therefore, based on the theories of Xampling and the finite rate of innovation (FRI), we propose the analog sparse imaging (ASI) method to deal with arbitrarily-located scatterers, which utilizes the estimating signal parameters through rotational invariance techniques (ESPRIT) and the plane matching technique (PMT). Simulation results show the effectiveness of the proposed method and the related analysis.

Sparse self-calibration via iterative minimization against phase synchronization mismatch for MIMO radar imaging

We address the problem of three-dimensional (3-D) imaging for multiple-input multiple-output (MIMO) radar in the presence of phase synchronization mismatch between each transmitter-receiver pair. Although usually set to the default values (zero) in the popular practice, such inevitable errors often occur owing to imperfect knowledge of the local oscillators and could seriously deteriorate the imaging result. Hence in this paper, taking the sparse priori of target into account and motivated by the maximum likelihood estimation (MLE), we propose the sparse self-calibration method via iterative minimization (SSCIM) algorithm to provide better inversion performance against the phase synchronization mismatch at low signal-to-noise ratio (SNR), and finally we demonstrate the effectiveness of the proposed method through numerical simulations.

Analyzing the FD-MIMO sparse imaging under carrier frequency offsets from the perspective of point spread function

In this paper, we address the problem of the frequency diverse multiple-input-multiple-output (FD-MIMO) radar sparse imaging with imperfect carrier frequency synchronization. From the perspective of classical point spread function (PSF) in range-angle plane, we get to know that the different scatterers in the interested scene would no longer share the same PSF. Instead, the scatterers located in different range bins would have distinct PSFs. Furthermore, for different sources to produce carrier frequency offsets, we find that those who are relevant to the receivers would result in more severe impact on the PSF due to the caused cross-interference between the range-angle dimensions. We further present a non-rigorous threshold of the carrier frequency offsets to suggest the boundary beyond which the resulting PSF would be totally distorted. Correspondingly, we propose to iteratively compensate the effect of carrier frequency offsets after sparse reconstruction when those offsets are controllable. Simulations demonstrate the reasonable results inferred from the analytical derivation and verify the effectiveness of the proposed algorithm.

Moving target imaging for DVB satellites-based passive radar system

This paper studies the passive radar imaging system for moving target based on digital video broadcasting (DVB) satellites. Firstly, the 3-dimensional (3-D) imaging model consisting of multiple DVB satellites and multiple receivers is established. After designing the “receiving stop-go” scheme, we derive the echo in a product-form of the wavenumber domain sampling and the velocity mismatch phase. Through detailed analysis, we come to the conclusion that the whole imaging resolution is limited by the wavenumber domain coverage, and would be seriously influenced by the velocity mismatch phase. Therefore, to reduce the extent of deterioration, we propose the “stop-imaging-go-velocity estimation” (SIGVE) method, which simultaneously recovers the target and estimates its velocity. Simulation results verify the effectiveness of the proposed method.