Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection

Sushil Kumar Joshi,Andre B. C. da Silva,Gerhard Krieger,Stefan V. Baumgartner

doi:10.3390/rs11111270

Sushil Kumar Joshi, Andre B. C. da Silva + Show 2 more

Open Access

https://doi.org/10.3390/rs11111270

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Journal: Remote sensing	Publication Date: May 28, 2019
Citations: 36	License type: CC BY 4.0

Affiliation: German Aerospace Center

Abstract

Ship detection is an essential maritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detection methods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppler domain using suitable distribution functions. Detection in range-Doppler has the advantage that principally even small ships with a low radar cross section (RCS) can be detected if they are moving fast enough so that the ship signals are shifted to the exo-clutter region. In order to determine a robust threshold, the ocean statistics have to be described accurately. Bright target peaks in the background ocean data bias the statistics and lead to an erroneous threshold. Therefore, an automatic ocean training data extraction procedure is proposed in the paper. It includes (1) a novel target pre-detection module that removes the bright peaks from the data already in time domain, (2) clutter normalization in the Doppler domain using the remaining samples, (3) ocean statistics estimation and (4) threshold computation. Various sea clutter models are investigated and analyzed in the paper for finding the most suitable models for the RC data. The robustness and applicability of the proposed method is validated using real linearly and circularly acquired radar data from DLR’s (Deutsches Zentrum für Luft- und Raumfahrt) airborne F-SAR system.

Highlights

Target detection and monitoring in the maritime environment is an imperative measure to ensure safety and security on the open sea
This paper proposes a constant false alarm rate (CFAR)-based ship detection processing chain principally suitable for real time applications
The major component of the proposed processing chain is the automatic training data selection approach. This approach includes a novel target pre-detection module used for successfully cancelling the outliers, i.e., spiky clutter peaks and strong ship signals, from the training data

Summary

Introduction

Target detection and monitoring in the maritime environment is an imperative measure to ensure safety and security on the open sea. The outputs of such activities are used by various agencies for the sustainable management of the ocean and its resources. One of the most popular maritime surveillance services is the automatic identification system (AIS) for the precise positioning of moving ships [1]. Most of the vessels and small boats are not equipped with the AIS transceivers. Some vessels turn off their receivers to execute illegal activities which make it harder to detect them. Sensed images acquired over the sea surface are able to provide wide area coverage and repetitive monitoring in such situations. Among the different imaging sensors, synthetic aperture radar (SAR) is considered to be one of the most effective due to its all-weather independent and day-night acquisition capabilities

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