Adaptive Kalman filter implementation by a neural network scheme for tracking maneuvering targets

Farid Amoozegar; Malur K. Sundareshan

doi:10.1117/12.213077

5 July 1995 Adaptive Kalman filter implementation by a neural network scheme for tracking maneuvering targets

Farid Amoozegar, Malur K. Sundareshan

Proceedings Volume 2485, Automatic Object Recognition V; (1995) https://doi.org/10.1117/12.213077
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Conventional target tracking algorithms based on linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias (e.g. jump) in the measurement sequence, which unless compensated, results in divergence of the Kalman filter that provides estimates of target position and velocity, in turn leading to a loss of track. Accurate compensation for the bias requires processing more samples of the input signals which adds to the computational complexity. The waiting time for more samples can also result in a total loss of track since the target can begin a new maneuver and if the target begins a new maneuver before the first one is compensated for, the filter would never converge. Most of the proposed algorithms in the current literature hence have the disadvantage of losing the target in short term accelerations, i.e., when the duration of acceleration is comparable to the time period between the measurements. The time lag for maneuver modelings, which have been based on Bayesian probability calculations and linear estimation shall propose a neural network scheme for the modeling of target maneuvers. The primary motivation for employing compensation. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

Citation Download Citation

Farid Amoozegar and Malur K. Sundareshan "Adaptive Kalman filter implementation by a neural network scheme for tracking maneuvering targets", Proc. SPIE 2485, Automatic Object Recognition V, (5 July 1995); https://doi.org/10.1117/12.213077

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KEYWORDS

Neural networks

Filtering (signal processing)

Error analysis

Radar

Detection and tracking algorithms

Motion models

Electronic filtering

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