CFAR Detection from Noncoherent Radar Echoes Using Bayesian Theory

Hiroyuki Yamaguchi,Wataru Suganuma

doi:10.1155/2010/969751

Abstract

We propose a new constant false alarm rate (CFAR) detection method from noncoherent radar echoes, considering heterogeneous sea clutter. It applies the Bayesian theory for adaptive estimation of the local clutter statistical distribution in the cell under test. The detection technique can be readily implemented in existing noncoherent marine radar systems, which makes it particularly attractive for economical CFAR detection systems. Monte Carlo simulations were used to investigate the detection performance and demonstrated that the proposed technique provides a higher probability of detection than conventional techniques, such as cell averaging CFAR (CA-CFAR), especially with a small number of reference cells.

Highlights

Noncoherent radar systems are widely used in applications such as ship navigation, and radar signal detection from sea clutter has been the subject of intense research for a number of years
A constant false alarm rate (CFAR) detection technique is introduced for heterogeneous sea clutter with a noncoherent radar system, where the local clutter power in the cell under test (CUT) is estimated by the Bayesian theory
These results show that the proposed technique is superior to CFAR with maximum likelihood (ML), maximum a posterior (MAP), and minimum mean square error (MMSE) estimator, especially when the number of reference cells is small and the local clutter power spatial correlation is weak

Summary

Introduction

Noncoherent radar systems are widely used in applications such as ship navigation, and radar signal detection from sea clutter has been the subject of intense research for a number of years. Using CFAR detection against K distributed clutter, the maximum a posterior (MAP) or the minimum mean square error (MMSE), that is, Bayes risk minimization method, can be applied for the clutter power estimation [10, 11]. A CFAR detection technique is introduced for heterogeneous sea clutter with a noncoherent radar system, where the local clutter power in the CUT is estimated by the Bayesian theory. This requires sufficient prior information about the sea clutter to be incorporated in the estimation, or the estimation accuracy might be degraded, as with ML and MAP.

Detection Technique

Performance Analysis

Conclusions