Target tracking algorithm with range rate under the color measurement environment

Y. P. Dai; C. Z. Jin; J. L. Hu; K. Hirasawa; Z. Liu

Target tracking algorithm with range rate under the color measurement environment

Y. P. Dai, C. Z. Jin, J. L. Hu, K. Hirasawa, Z. Liu

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper a new observation model is presented to improve the state estimation and prediction in a target tracking problem. Comparing with conventional approaches, the following are distinguished points of the approach. First, the measurement equation is set up in the polar coordinate and even combines the range rate measurement with the usual position measurements (i.e. range, azimuth, elevation angle, range rate). Second, the observation noise of sensor data is considered as a colored one, so new linear state and observation equation can be obtained by incorporating the noise vector into the state vector, which satisfy the requirement of Kalman filter. As a result, the accuracy of both the measurement and the prediction will be increased.

Original language	English
Pages	1145-1148
Number of pages	4
Publication status	Published - 1999 Dec 1
Externally published	Yes
Event	Proceedings of the 1999 38th SICE Annual Conference - Morioka, Jpn Duration: 1999 Jul 28 → 1999 Jul 30

Other

Other	Proceedings of the 1999 38th SICE Annual Conference
City	Morioka, Jpn
Period	99/7/28 → 99/7/30

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

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Cite this

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title = "Target tracking algorithm with range rate under the color measurement environment",

abstract = "In this paper a new observation model is presented to improve the state estimation and prediction in a target tracking problem. Comparing with conventional approaches, the following are distinguished points of the approach. First, the measurement equation is set up in the polar coordinate and even combines the range rate measurement with the usual position measurements (i.e. range, azimuth, elevation angle, range rate). Second, the observation noise of sensor data is considered as a colored one, so new linear state and observation equation can be obtained by incorporating the noise vector into the state vector, which satisfy the requirement of Kalman filter. As a result, the accuracy of both the measurement and the prediction will be increased.",

author = "Dai, {Y. P.} and Jin, {C. Z.} and Hu, {J. L.} and K. Hirasawa and Z. Liu",

year = "1999",

month = dec,

day = "1",

language = "English",

pages = "1145--1148",

note = "Proceedings of the 1999 38th SICE Annual Conference ; Conference date: 28-07-1999 Through 30-07-1999",

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TY - CONF

T1 - Target tracking algorithm with range rate under the color measurement environment

AU - Dai, Y. P.

AU - Jin, C. Z.

AU - Hu, J. L.

AU - Hirasawa, K.

AU - Liu, Z.

PY - 1999/12/1

Y1 - 1999/12/1

N2 - In this paper a new observation model is presented to improve the state estimation and prediction in a target tracking problem. Comparing with conventional approaches, the following are distinguished points of the approach. First, the measurement equation is set up in the polar coordinate and even combines the range rate measurement with the usual position measurements (i.e. range, azimuth, elevation angle, range rate). Second, the observation noise of sensor data is considered as a colored one, so new linear state and observation equation can be obtained by incorporating the noise vector into the state vector, which satisfy the requirement of Kalman filter. As a result, the accuracy of both the measurement and the prediction will be increased.

AB - In this paper a new observation model is presented to improve the state estimation and prediction in a target tracking problem. Comparing with conventional approaches, the following are distinguished points of the approach. First, the measurement equation is set up in the polar coordinate and even combines the range rate measurement with the usual position measurements (i.e. range, azimuth, elevation angle, range rate). Second, the observation noise of sensor data is considered as a colored one, so new linear state and observation equation can be obtained by incorporating the noise vector into the state vector, which satisfy the requirement of Kalman filter. As a result, the accuracy of both the measurement and the prediction will be increased.

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T2 - Proceedings of the 1999 38th SICE Annual Conference

Y2 - 28 July 1999 through 30 July 1999

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