Adaptive data association methods for pulse train analysis and deinterleaving

This research is concerned with the application of adaptive Kalman filter and data association methods to the pulse train analysis and deinterleaving problems. Important applications arise in signal analysis and identification tasks performed by passive radar intercept receivers. The receiver operates by measuring individual pulse parameters, and then performs pulse sorting to extract inter-pulse parameters. A key inter-pulse parameter is the pulse repetition interval (PRI). An estimate of the PRI is derived from the measured pulse time-of-arrival (TOA) values. PRI estimation is complicated by the presence of TOA jitter, false and missing TOA data, and simultaneous emitters.;The new techniques developed in this thesis are based on a state-space model of the pulse train evolution process. A Kalman filter is implemented to estimate and track state parameters of a single pulse train. The main thrust of the research is the development of methods to handle practical limitations associated with false/missing data and simultaneous emitters. The presence of both false and missing data imposes the need for a data association method. A Pulse Train Probabilistic Data Association Filter (PT-PDAF) is developed. This filter is shown to perform optimally even in the presence of significant corruption. For pulse train deinterleaving, the problem is cast in switching system framework. The Interacting Multiple Models (IMM) approach is implemented to simultaneously estimate the pulse train PRIs. A new adaptive technique is developed to make IMM robust to missing data, and a false-mode IMM is developed to handle missing data. A look-ahead PDA approach is developed to allow the filter to utilize future data while making current-pulse estimates. Simulations and real data are used to demonstrate the effectiveness of the PT-PDAF and the IMM implementations.