Off-Grid direction of arrival estimation in the presence of measurement noise and heavy cluttered environment

Abstract

In this paper, we focus on estimating Direction of Arrival (DOA) and removing heavy clutter embedded with measurement noise. A correlated Gaussian process is chosen to model destructive effects of clutter. Also, a white Gaussian process is selected to describe measurement noise caused by sensor array. After adding these distortions to the off-grid model, we utilize Sparse Bayesian Learning and principal component analysis (as a preprocessing stage) in order to remove these distortions as well as estimating of true DOAs. Finally, at the end we will show how ignorance of clutter from model or combine it with measurement noise degrade DOA estimation. This will be demonstrated by various numerical simulations.

Multi-Target Track Initiation in Heavy Clutter

“…In the heavy clutter and multi-target environment [22], the information capacity is large, the relationships among information are complicated [23,24], the number of the detected targets is unknown [25], and track initiation often has a high false alarm rate or missing alarm rate. Obviously, it is still a difficult task to get a high-quality track initiation in the limited measurement cycles [26][27][28].…”