Share Email Print
cover

Proceedings Paper

A robust algorithm for automated target recognition using precomputed radar cross sections
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Passive radar is an emerging technology that offers a number of unique benefits, including covert operation. Many such systems are already capable of detecting and tracking aircraft. The goal of this work is to develop a robust algorithm for adding automated target recognition (ATR) capabilities to existing passive radar systems. In previous papers, we proposed conducting ATR by comparing the precomputed RCS of known targets to that of detected targets. To make the precomputed RCS as accurate as possible, a coordinated flight model is used to estimate aircraft orientation. Once the aircraft's position and orientation are known, it is possible to determine the incident and observed angles on the aircraft, relative to the transmitter and receiver. This makes it possible to extract the appropriate radar cross section (RCS) from our simulated database. This RCS is then scaled to account for propagation losses and the receiver's antenna gain. A Rician likelihood model compares these expected signals from different targets to the received target profile. We have previously employed Monte Carlo runs to gauge the probability of error in the ATR algorithm; however, generation of a statistically significant set of Monte Carlo runs is computationally intensive. As an alternative to Monte Carlo runs, we derive the relative entropy (also known as Kullback-Liebler distance) between two Rician distributions. Since the probability of Type II error in our hypothesis testing problem can be expressed as a function of the relative entropy via Stein's Lemma, this provides us with a computationally efficient method for determining an upper bound on our algorithm's performance. It also provides great insight into the types of classification errors we can expect from our algorithm. This paper compares the numerically approximated probability of Type II error with the results obtained from a set of Monte Carlo runs.

Paper Details

Date Published: 21 September 2004
PDF: 12 pages
Proc. SPIE 5426, Automatic Target Recognition XIV, (21 September 2004); doi: 10.1117/12.540344
Show Author Affiliations
Lisa M. Ehrman, Georgia Institute of Technology (United States)
Aaron D. Lanterman, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5426:
Automatic Target Recognition XIV
Firooz A. Sadjadi, Editor(s)

© SPIE. Terms of Use
Back to Top