Accession Number:

ADA399478

Title:

A Multiple Model Filter Without Markov Switching

Descriptive Note:

Conference Proceedings

Corporate Author:

NAVAL SURFACE WARFARE CENTER DAHLGREN DIV VA

Personal Author(s):

Report Date:

1998-03-01

Pagination or Media Count:

21.0

Abstract:

When tracking with multi-sensor systems, the set of sensors used may be asynchronous and there may be communications delays between sensor platforms and the fusion center. Despite these conditions, it is desirable that each sensor maintains an accurate track. It has been recognized for some time that the use of a multiple model filter is superior to the use of a single model filter for tracking maneuvering targets. However, existing multiple model tracking algorithms use Markov switching, assuming that the likelihoods of the target state switching between kinematic models are known. The objectives of this paper are twofold. First, it will present a Multiple Model MM tracking algorithm, called the ARMM algorithm, that does not assume a priori knowledge of the target transition probability matrix. This work attempts to relax some of the assumptions found in the most widely used MM tracking algorithm. Second, it will be shown that the ARMM algorithm can also be used as the second, and final, stage in a logical process for fusing asynchronous tracks from multiple sensors that use different kinematic models in their individual track filters.

Subject Categories:

  • Computer Programming and Software
  • Computer Systems
  • Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE