Nonlinear filtering with really bad data

Ronald P. S. Mahler; Richard P. Leavitt; J. Warner; R. Myre

doi:10.1117/12.357193

27 July 1999 Nonlinear filtering with really bad data

Ronald P. S. Mahler, Richard P. Leavitt, J. Warner, R. Myre

Proceedings Volume 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII; (1999) https://doi.org/10.1117/12.357193
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

In past presentations at this and other conferences and in the recent book `Mathematics of Data Fusion,' we have introduced `finite set statistics' or FISST (a direct generalization of conventional single-sensor, single-target statistics to the multisensor-multitarget realm). We have also shown how FISST provides a unified foundation for the following aspects of multisource-multitarget data fusion: detection, identification, tracking, multi-evidence accrual, sensor management, performance estimation, and decision- making. In this paper we illustrate the FISST approach by showing how conventional filtering and estimation theory can be generalized to tracking or target I.D. problems involving highly ambiguous evidence. In particular, we illustrate the FISST approach on three simple model problems involving (1) target I.D. with a very low-quality RWR (radar warning receiver) sensor, and (2) target tracking via fusion of (simulated) radar reports with (simulated) English-language reports.

Citation Download Citation

Ronald P. S. Mahler, Richard P. Leavitt, J. Warner, and R. Myre "Nonlinear filtering with really bad data", Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); https://doi.org/10.1117/12.357193

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