Automatic Detection for MTI Processed Radar Signals

Abstract

In this thesis, methods for automatic detection for radar systems are investigated. The objective is to indicate the presence of targets in the midst of noise and clutter. One of the most efficent methods for doing this is to exploit the Doppler shift in reflections from moving targets. This is called Moving Target Indication (MTI), and it is used in many radar applications today. However, such functionality is not typical for radars employing a magnetron oscillator. The magnetron oscillator is widely used in civil marine radars, and MTI processing is of interest for such radars as well. In addition to MTI processing, automatic detection may be applied in order to make decisions on target presence. This may be achieved by employing Constant False Alarm Rate (CFAR) detection and pulse integration. The challenges with automatic detection are prediction of the clutter power, and handling of non-homogeneous environments. Ideally, clutter components will be removed by the MTI process, leaving receiver noise and reflection from targets at the output. However, this is not necessarily the case when applied to a magnetron radar. A particular automatic detector employing Ordered Statistics (OS) CFAR and binary pulse integration is investigated. This is a robust detector that may operate in the presence of multiple targets and non-Gaussian clutter. The binary integrator contributes in reducing the false alarm rate, such that acceptable performance may be achieved. In order to find suitable parameters for the detector, analysis of an MTI processed radar signal containing reflection from waves at sea and a small boat is carried out. Also, the performance of the detector has been measured in terms of false alarm probability, and target detection. Analysis shows that Weibull or K-distribution are suitable models for the sea clutter, and that the MTI signal exhibits spatial correlation between clutter samples. The correlation is concluded to be the reason for degradation in performance, as detection on clutter appears as targets. Also, optimum parameters for the detector is found, and it is shown that increasing the number of reference samples increases the number of target detections.