A Kalman filter merging CV and acceleration estimation model using mode probabilities

Masataka Hashirao, Tetsuya Kawase, Iwao Sasase

Research output: Contribution to journalConference article

Abstract

For multi-target tracking, the IMM (Interactive Multiple Model) algorithm has been proposed. The IMM is expected to reduce tracking errors for both non-maneuvering and maneuvering target. However, the IMM requires heavy computational burden, because it utilizes multiple Kalman filters in parallel. On the other hand, the Kalman filter with a turning acceleration estimator, which can adapt a maneuvering target, has been proposed. The Kalman filter with a turning acceleration estimator is a single two-stage type filter and has a problem of setting threshold for the maneuver detector. In this paper, we propose the hybrid filter with a constant-velocity (CV) filter and a turning acceleration estimation filter. The proposed filter does not require a maneuver detector, because it integrates the outputs of two filters using the likelihood of each filter. And its computational requirement is smaller than the IMM, since it consists of only two Kalman based filters. The proposed method can prevent deteriorating tracking accuracy by reducing the risk of maneuver misdetection when a target maneuvers. We evaluate the performance of the proposed filter by computer simulation, and show the effectiveness of the proposed filter, comparing with the conventional Kalman filter and the two-stage Kalman filter.

Original languageEnglish
Pages (from-to)334-338
Number of pages5
JournalIEE Conference Publication
Issue number490
Publication statusPublished - 2002 Dec 1
EventRADAR 2002 - Edinburgh, United Kingdom
Duration: 2002 Oct 152002 Oct 17

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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