Information limits in remote sensing and target tracking

Date of Completion

January 2005


Engineering, Electronics and Electrical




In this research we focus on information limits in the remote sensing and target tracking field. Three topics are studied: a dynamic Cramér-Rao bound for target tracking in clutter, target intervisibility, and monopulse radar detection and localization of multiple unresolved targets via joint bin processing. ^ We develop a Cramér-Rao lower bound (CRLB) for target tracking, that is, for the state estimates of dynamic systems in the presence of false alarms and missed detections. We show that the CRLB obeys a Riccati-like equation, with the exception that the measurement-noise covariance term is multiplied by an information reduction factor (IRF). The calculation of the IRF and the existence of efficient estimators are also addressed. ^ In the second topic we study intervisibility---the existence of an unobstructed line of sight (LOS) between two points---accounting for the vertical and horizontal errors in the estimated locations of both points as well as elevation errors in the database of the terrain that could obstruct the LOS between these points. This is a significant factor in limiting information gathering in real systems. The errors will first be simply treated as a "white" noise sequence: we assume no correlation between the intervisibility at two different times, and the probability of an instantaneous intervisibility event will be in this case developed. Consequently, we will present a second treatment in which the errors are stochastic processes of a certain bandwidth, and both the probability density function of an intervisibility interval and the average number of intervisibility intervals over a certain time period will be developed. ^ In a monopulse radar system, if several closely-spaced targets fall within the same radar beam and between two adjacent matched filter samples in range, the monopulse information from both of these samples can and should be used for estimation, both of angle and of range (i.e., estimation of the range to sub-bin accuracy). In our research, a model of closely spaced targets that fall between adjacent matched filter samples is established and a maximum likelihood (ML) extractor will be developed. The limits on the number of targets that can be estimated are given. A minimum description length (MDL) criterion is used to decide on the number of targets between the matched filter samples. ^