Track-Before-Detect Procedures in a Multi-Target Environment

This paper addresses the problem of early detection of K >= 1 multiple moving targets in radar systems through the use of track-before-detect (TBD) techniques. At first, assuming prior knowledge of K, a binary generalized likelihood ratio test (GLRT) is derived, which shows that the multi-target TBD problem can be regarded as a K-path trellis search. Since optimal implementation of the GLRT has a nonlinear complexity either in the number of targets or in the number of integrated frames, suboptimum algorithms are investigated which allow to trade better estimation and tracking accuracy for a much lower implementation complexity. Next, the TBD problem with K unknown is discussed and a novel multi-hypothesis test strategy is derived as the solution to a constrained optimization problem, which subsumes the conventional binary GLRT as the special case of known K. Finally, numerical examples are provided to assess and compare the performances of the proposed TBD procedures.