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, semi-sequential 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-to-noise ratio of -10 dB, and for highly dynamic scenarios. An alternating projection method was used for contact state initialization and signal enumeration.