The Alpha-Beta Filter in Drag with Data Association
Abstract
The well-known alpha beta filter achieves its best performance when measurement outliers are rare. Two modifications of the alpha beta filter are presented for improving its performance when outliers are commonplace. The alpha beta mu filter uses a drag coefficient to ensure that the target motion model has a finite asymptotic velocity variance. Although the alpha beta filter mu is less sensitive to outliers than the alpha beta filter, it does not directly address the problem of large spurious measurements. The alpha beta mu sigma filter is a probabilistically based, nonlinear target estimation filter that is much more robust against outliers than the alpha beta filter. To facilitate understanding of the filter optimization problem and to enable computation of filter outputs crucial to the post-processing task of associating collections of tracking filters, it is recommended that the alpha beta filter, together with all the important earlier work that has made it useful in applications, be reinterpreted in the Kalman filter paradigm.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1999
- Accession Number
- ADA640114
Entities
People
- Roy L. Streit
Organizations
- Naval Undersea Warfare Center