By Topic

Detection and estimation for multiple targets with two omnidirectional sensors in the presence of false measurements

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
H. M. Shertukde ; Dept of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA ; Y. Bar-Shalom

A track-before-detect methodology for target detection and estimation in the presence of false measurements is presented that uses two omnidirectional passive sensors. The estimation technique is based on maximum-likelihood estimation. The measurement model is nonlinear and includes false alarms. The algorithm is first developed for a single target and then extended to multiple targets. For multiple targets, unresolved measurements are also considered to provide a realistic analysis of targets crossing in the measurement space. The Cramer-Rao lower bound is derived for the target parameter estimation in the presence of false measurement. A detection mechanism that can validate the existence of a target corresponding to the estimated track is formulated. For a single target, it is shown that only the global maximum leads to the acceptance of the target hypothesis. The test for multiple targets is obtained by formulating a multiple-hypotheses problem. The theoretical performance predictions are validated via Monte Carlo simulations. The effect on the performance of the density of false measurements is illustrated in examples. The highest false-measurement density for which this technique works corresponds to SNR=2 dB

Published in:

IEEE Transactions on Acoustics, Speech, and Signal Processing  (Volume:38 ,  Issue: 5 )