Improved target tracking using kinematic measurements and target orientation information

S. Sutharsan; R. Tharmarasa; T. Kirubarajan

doi:10.1117/12.819234

11 May 2009 Improved target tracking using kinematic measurements and target orientation information

S. Sutharsan, R. Tharmarasa, T. Kirubarajan

Proceedings Volume 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII; 733604 (2009) https://doi.org/10.1117/12.819234
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

This paper discusses a target tracking system that provides improved estimates of target states using target orientation information in addition to standard kinematic measurements. The objective is to improve state estimation of highly maneuverable targets with noisy kinematic measurements. One limiting factor in obtaining accurate state estimates of highly maneuvering targets is the high level of uncertainty in velocity and acceleration. The target orientation information is helpful in alleviating this problem to accurately determine the velocity and acceleration components. However, there is no sensor that explicitly measures target orientation. In this paper, the Observable Operator Model (OOM) is used together with multiple sensor information to estimate target orientation measurement. This is done by processing the sensor feature measurements from different aspect angles and the estimated target orientation measurement is used in conjunction with kinematic measurements to conclusively estimate target states. Simulation results show that the incorporation of target orientation can enhance the tracking performance in the presence of fast moving and/or maneuvering targets. In addition, the Posterior Cramer-Rao lower bound (PCRLB) that quantifies the achievable performance is derived. It is shown that the proposed estimator meets the PCRLB.

Citation Download Citation

S. Sutharsan, R. Tharmarasa, and T. Kirubarajan "Improved target tracking using kinematic measurements and target orientation information", Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 733604 (11 May 2009); https://doi.org/10.1117/12.819234

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KEYWORDS

Sensors

Kinematics

Detection and tracking algorithms

Monte Carlo methods

Motion models

Algorithm development

Missiles

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