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1 October 1990 Multiple target tracking using an extended Kalman filter
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The paper centers on the continued development of the symmetric measurement equation (SME) filter developed by Kamen' for track maintenance in multiple target tracking. In this approach there is no need to correctly associate measurements and targets before target state estimation can take place. Rather the data association problem is embedded in the process of target state estimation. The "first order" version of the SME filter is an extended Kalman filter (EKF), and thus the computational requirements for filter implementation are comparable to that for a standard Kalman filter. In addition, in contrast to probabilistic data association filters, the estimator does not rely on the computation of probabilities for correct measurement/target associations. The SME filter is based on a standard state model for the target state trajectories. However, in contrast to existing approaches, the measurements are defmed in terms of nonlinear symmetric functionals of the target positions, except for one of the measurements which is defmed to be a scaled sum of the target positions. The measurement functions are defmed so that in the noise-free case, the target position vector for each coordinate can be determined up to a permutation of elements from knowledge of the measurements. In this paper, we define the measurements in terms of sums of products of the target coordinate positions. The performance of the SME filter is investigated via a computer simulation of the six-target case.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
E. W. Kamen "Multiple target tracking using an extended Kalman filter", Proc. SPIE 1305, Signal and Data Processing of Small Targets 1990, (1 October 1990);

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