Efficient Tracking Algorithms with Phased Array Radars in the Presence of Electronic Counter Measures

Abstract

In this dissertation, the problem of target tracking in the presence of electronic counter measures (ECM) with phased array radars is studied. This work focuses mainly on tracking airborne targets in the presence of strong interference. Three major challenging problems of target tracking in the ECM scenario are considered. Primarily waveform agile sensing approach is used as electronic counter counter measure (ECCM) technique to tackle the ECM for tracking benchmark target trajectories. In addition to ECM, other attributes such as multi-path, clutter and false alarms (FA) are considered. Three different types of frequency coded waveforms (linear frequency, Gaussian frequency and stepped frequency) are considered in the waveform bank. The next waveform that is to be transmitted is selected so as to reduce the tracking error. In addition, the present investigation is aimed to optimize radar resources (average power, radar time and energy). Further, the work is extended to multidimensional filtering approach. In this context, waveform agile sensing with space time adaptive processing (STAP) is proposed to improve the track performance for benchmark trajectories. This research also proposes novel data association techniques to improve the track performance in the presence of strong interference. The measurements obtained from sensors has to be allocated to a particular target precisely in the multi-target scenario, so as to track the targets accurately. Two soft and evolutionary computing based data association approaches (fuzzy particle swarm optimization (Fuzzy-PSO) and fuzzy genetic algorithm (Fuzzy-GA) ) are presented to enhance the performance. Fuzzy-GA based data association approach produced superior results as compared to joint probabilistic data association (JPDA), fuzzy clustering means (FCM) and Fuzzy-PSO in the presence of ECM. Further more, two computationally efficient fuzzy based data association algorithms ( all neighbor fuzzy relational and rough fuzzy) have been presented. Four different case studies are considered to validate these novel data association techniques. iiiThis thesis further deals with tracking closely spaced targets in the presence of ECM. An investigation is carried out to resolve the closely spaced targets using Stockwell transform based multiple signal classification (MUSIC) direction of arrival algorithm. This method yields improved performance compared to that obtained from other existing methods.