Fundamental Properties and Optimal Gains of a Steady-State Velocity Measured α-β Tracking Filter - Advances in Remote Sensing

ARS > Vol.3 No.2, June 2014

Advances in Remote Sensing

Volume 3, Issue 2 (June 2014)

ISSN Print: 2169-267X ISSN Online: 2169-2688

Google-based Impact Factor: 1.5 Citations

Fundamental Properties and Optimal Gains of a Steady-State Velocity Measured α-β Tracking Filter ()

HTML

Download as PDF (Size: 2544KB) PP. 61-76

DOI: 10.4236/ars.2014.32006 3,311 Downloads 5,667 Views Citations

Author(s)

Kenshi Saho

Affiliation(s)

Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu, Japan.

ABSTRACT

This paper clarifies the steady-state properties and performance of an α-β filter for moving target tracking using both position and velocity measurements. We call this filter velocity measured α-β (VM-α-β) filter. We first derive the stability condition and steady-state predicted errors as fundamental properties of the VM-α-β filter. The optimal gains for representative motion models are then derived from the Kalman filter equations. Theoretical and numerical analyses verify that VM-α-β filters with these optimal gains realize more accurate tracking than conventional α-β filters when the filter gains are relatively large. Our study reveals the conditions under which the predicted errors of the VM-α-β filters are less than those of conventional α-β filters. Moreover, numerical simulations clarify that the variance of the tracking error of the VM-α-β filters is approximately 3/4 of that of the conventional α-β filters in realistic situations, even when the accuracy of the position/velocity measurements is the same.

KEYWORDS

α-β Filter, Moving Target Tracking, Velocity Measurement, Kalman Filter, Optimal Gain

Share and Cite:

Saho, K. (2014) Fundamental Properties and Optimal Gains of a Steady-State Velocity Measured α-β Tracking Filter. Advances in Remote Sensing, 3, 61-76. doi: 10.4236/ars.2014.32006.

Cited by

[1]	An Adaptive Kinematic Filter Extended by the Additionally Measured Velocity or Acceleration
	2020

[2]	Review of the Kalman Filter and Related Filters
	2020

[3]	Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter
	Applied Sciences, 2017

[4]	Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy
	Medical Physics, 2016

[5]	Temporal regularization of ultrasound‐based liver motion estimation for image‐guided radiation therapy
	Medical physics, 2016

[6]	Automatic Parameter Setting Method for an Accurate Kalman Filter Tracker Using an Analytical Steady-State Performance Index.
	2015

[7]	Automatic Parameter Setting Method for an Accurate Kalman Filter Tracker Using an Analytical Steady-State Performance Index
	Access, IEEE, 2015

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journals Menu

Home

About SCIRP

Service

Policies