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Proceedings Paper

IMM filtering on parametric data for multi-sensor fusion
Author(s): Scott Shafer; Mark W. Owen
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Paper Abstract

In tracking, many types of sensor data can be obtained and utilized to distinguish a particular target. Commonly, kinematic information is used for tracking, but this can be combined with identification attributes and parametric information passively collected from the targets emitters. Along with the standard tracking process (predict, associate, score, update, and initiate) that operates in all kinematic trackers, parametric data can also be utilized to perform these steps and provide a means for feature fusion. Feature fusion, utilizing parametrics from multiple sources, yields a rich data set providing many degrees of freedom to separate and correlate data into appropriate tracks. Parametric radar data can take on many dynamics to include: stable, agile, jitter, and others. By utilizing a running sample mean and sample variance a good estimate of radar parametrics is achieved. However, when dynamics are involved, a severe lag can occur and a non-optimal estimate is achieved. This estimate can yield incorrect associations in feature space and cause track fragmentation or miscorrelation.
In this paper we investigate the accuracy of the interacting multiple model (IMM) filter at estimating the first and second moments of radar parametrics. The algorithm is assessed by Monte Carlo simulation and compared against a running sample mean/variance technique. We find that the IMM approach yields a better result due to its ability to quickly adapt to dynamical systems with the proper model and tuning.

Paper Details

Date Published: 13 June 2014
PDF: 13 pages
Proc. SPIE 9092, Signal and Data Processing of Small Targets 2014, 90920G (13 June 2014); doi: 10.1117/12.2050720
Show Author Affiliations
Scott Shafer, Space and Naval Warfare Systems Ctr. Pacific (United States)
Mark W. Owen, Space and Naval Warfare Systems Ctr. Pacific (United States)

Published in SPIE Proceedings Vol. 9092:
Signal and Data Processing of Small Targets 2014
Oliver E. Drummond, Editor(s)

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