Solving the association problem for a multistatic range-only radar target tracker

  • Authors:

  • Michail N. Petsios;Emmanouil G. Alivizatos;Nikolaos K. Uzunoglu

  • Affiliations:

  • Microwave and Fiber Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, Zografos 15773, Athens, Greece;Microwave and Fiber Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, Zografos 15773, Athens, Greece;Microwave and Fiber Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, Zografos 15773, Athens, Greece

  • Venue:
  • Signal Processing
  • Year:
  • 2008

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Abstract

This paper investigates the data association problem for a multiple target tracker, in dense clutter environment, using multiple bistatic range and range-rate measurements from a number of bistatic radars, composing a multistatic radar. Special emphasis is given to the initialization procedures, where a multiple hypothesis logic is adopted to resolve 'ghost'-targets, frequently generated by range-only multisensor systems. The contribution of the paper is twofold. First, the development and implementation of the multiple target tracker algorithm is presented, including filtering, association and track management. The proposed algorithm includes: (i) global nearest neighbor (GNN) based on auction algorithm combined with a heuristic multiple hypothesis logic for new track initialization; (ii) interacting multiple model algorithm combined with iterated unscented Kalman filter (IMM-I-UKF); (iii) and a rule-based track management. Second, a set of performance metrics is chosen to evaluate the performance of the algorithm presented. Monte Carlo simulation demonstrates the track accuracy, probability of correct association, robustness and computational complexity for numerous scenarios of multiple targets (crossing track patterns, closely spaced or manoeuvring targets), for different values of clutter density and probability of detection. Finally, comparisons with other widely used methods prove the superiority of the proposed algorithm.