An Integrated Method to Detection, Data Association and Tracking of Multiple Broadband Signals

The present work explores a new method of integrated detection, localization, and tracking of multiple broadband signals directly from array data, without the requirement of distinct data association. The method is based on Maximum A-Posteriori probability concepts and combines Maximum Likelihood direction finding techniques with Kalman Filter theory. Implicit data association is given by a Nonlinear Programming scheme that simplifies the solution of a constrained optimization problem. Assuming Markov Motion and random Gaussian signals and noise, diverse kinematic scenarios for both synthetic and real data sets were investigated. Full data batch, semisequential and fully sequential variants were developed in element space, beamspace and windowed element space. The method was found to work well down to a signal-tonoise ratio of −10 dB, and for highly dynamic scenarios. An alternating projection method was used for contact state initialization and signal enumeration.