Estimation and data association algorithms for multisensor-multitarget tracking
Date of Completion
Engineering, Electronics and Electrical
In this dissertation we analyze three different estimation and data association problems arising in multisensor-multitarget tracking.^ The first part of this dissertation deals with the design and development of a robust estimation and data association system for use in multisensor air traffic surveillance. The result of this work is the development of a versatile estimation and data association algorithm to simultaneously track a number of aircraft using detections from multiple radars. Some of the the novel aspects of the proposed solution include: an Interacting Multiple Model filter for the estimation of the aircraft states and the development of the likelihood function for the Interacting Multiple Model estimator so that it can be used in conjunction with the assignment algorithm.^ The second part of the dissertation addresses the problem of estimating the trajectory of a ballistic missile in the exo-atmospheric (mid-course) phase, using line of sight measurements from one or more moving platforms. We focus both on the theoretical aspects of this problem and on the development of an algorithm to obtain the maximum likelihood estimate of the state of the ballistic object. The major results of this work include: (i) a robust estimation algorithm that provides reliable estimates of the target trajectory; (ii) the incorporation of additional target motion information, such as bounds on its altitude and speed, which yield significant improvement in the estimation errors; and (iii) the derivation of theoretical lower bounds on the estimate covariance matrix (the lowest mean square error achievable) which can be used to gauge the performance of the estimators as well as to evaluate a particular target-sensor geometry in terms of the achievable accuracy.^ Finally, in the third part of this dissertation, we address the problem of tracking a ballistic object during the boost phase. The estimation of the trajectory during the boost phase and the associated error covariance play an important role in the defense against tactical ballistic missiles, which have a relatively short range and hence, time is a very critical factor. The results from the second and third parts of this thesis constitute a robust estimation procedure capable of tracking ballistic missiles from launch to re-entry (or acquisition by a surface-based radar). ^
Yeddanapudi, Muralidhar Krishna, "Estimation and data association algorithms for multisensor-multitarget tracking" (1996). Doctoral Dissertations. AAI9705031.