Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging

Abstract

We propose a sample selection method for multiple-input, multiple-output ultra-wideband noise radar imaging using compressive sensing. The proposed sample selection is based on comparing the norm values of candidates among the potential received signal and selecting the largest M samples among N per antenna to obtain selection diversity. Moreover, we propose an adaptive weighting allocation that improves reconstruction accuracy of compressive sensing by maximizing the mutual information between target echoes and transmitted signals. This weighting scheme is applicable to both sample selection schemes, a conventional random sampling and the proposed selection. Further, the weighting allocation with the knowledge of recovery error is proposed for more practical scenarios. Simulations show that the proposed selection and weighting allocation enhance multiple target detection probability and reduce normalized mean square error.

Statistical spatial resolution limit for ultrawideband MIMO noise radar

“…In this section, the SSRL for UWB MIMO noise radar, based on the hypothesis test (more precisely, GLRT), is derived based on the signal model in (2 The targets can be resolved easily and the probability of detection is higher when the spacing between the two targets is larger. In this sense, the probability of detection can be used to represent the resolvability of two closely spaced targets.…”

“…The UWB MIMO noise radar, with each transmit antenna radiating UWB noise waveform, is immune from detection, interference, jamming, and efficiently shares the frequency spectrum. Moreover, UWB MIMO noise radar is considered to be a promising technique for high resolution target detection and estimation [1] [2]. A topic of great interest in MIMO radar systems is the target localization performance improvement.…”