Trajectory prediction and passive sensor network data association
Date of Completion
January 2009
Keywords
Engineering, Electronics and Electrical
Degree
Ph.D.
Abstract
This dissertation investigates three problems in trajectory prediction and passive sensor network data association that are challenging and very relevant in today's world. ^ The first one is the impact point prediction (IPP) of ballistic objects such as mortar and howitzer projectiles. A multiple model procedure is presented to estimate the state of a ballistic object in the atmosphere and identify it using radar measurements from the initial part of the trajectory only, for the purpose of IPP for use in area defense. Measurements are taken during the first part of its trajectory up to the apogee and the final state estimate obtained by the multiple model estimator is then predicted to its impact point on earth. ^ The second problem considered is the problem of using passive (line-of-sight angle) observations of a surface-to-air or an air-to-air missile (pursuer) from an aircraft (evader) to infer whether the missile is aimed at the aircraft. The observations are assumed to be made only on an initial portion of the pursuer's trajectory. The approach is to model the trajectory of the missile with a number of kinematic and guidance parameters, estimate them and use statistical tools to infer whether the missile is guided toward the aircraft. The estimation/decision algorithm developed can be used for an aircraft to decide, in a timely manner, whether appropriate countermeasures are necessary. ^ The third area is feature-aided tracking of ground vehicles using acoustic signals obtained by a passive sensor network. Tracking of a moving ground target using acoustic signals obtained from a passive sensor network is a difficult problem as the signals are contaminated by wind noise and are hampered by road conditions and multipath, etc., and are not deterministic. Multiple target tracking becomes even more challenging, especially when some of the vehicles are wheeled (e.g., cars/trucks) and some are tracked (e.g., tanks), and are closely spaced. In such cases the stronger acoustic signals from the tracked vehicles can mask those from the wheeled vehicles, leading to poor detection of such targets. Acoustic sensor arrays obtain direction of arrival (DoA) angle estimates of such emitters from the received acoustic signals. The full position estimates of targets, obtained following the association of the DoA angle estimates of the same target from at least three sensor arrays, are used for target tracking. However, because of the particular challenges encountered in multiple ground vehicle tracking, this association is not always reliable and thus, target tracking using full position measurements only is difficult and it can lead to lost tracks. We developed a new feature-aided tracking algorithm in order to improve the tracking performance. ^
Recommended Citation
Ravindra, Vishal Cholapadi, "Trajectory prediction and passive sensor network data association" (2009). Doctoral Dissertations. AAI3388408.
https://digitalcommons.lib.uconn.edu/dissertations/AAI3388408