학술논문
Optimal passive localization from a single sensor using multiple linear hypotheses
Document Type
Conference
Author
Source
ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '84.. 9:198-201 1984
Subject
Language
Abstract
Target localization from bearing measurements at a single sensor is subject to significant nonlinearity losses. Modified polar coordinates minimize the losses due to linearization about a single solution hypothesis for an extended Kalman filter (EKF). However, even the minimal linearization losses become significant at very long range and low signal-to-noise ratio (SNR). A new Multiple Linear Hypothesis Estimator (MLHE) effectively eliminates the linearization loss. Multiple linear bearing/bearing rate estimators are propagated for a deterministic set of inverse range and normalized range rate hypotheses, chosen to span the region of possible a priori solutions. The linear estimation solutions provide a basis for recursively updating the a posteriori probabilities of the multiple hypotheses. The resulting two-dimensional probability surface in hypothesis space, together with the linear estimation solutions, provide a sufficient statistic for optimal estimation.