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Forward Prediction-Based Approach to the Out-of-Sequence Measurement Problem with Correlated Noise
Abstract:
In target tracking systems, measurements from the same target can arrive out-of-sequence. Such OOSM arrivals can induce negative-time measurement update problem. In order to solve the l-step-lag OOSM problem with correlated process noise and measurement noise, a new algorithm based on forward prediction is proposed. A modified information filter is adopted so as to eliminate the correlated noise. By defining an equivalent measurement the l-step-lag OOSM problem is transformed into 1-step-lag problem. The new algorithm is independent with the discrete time model of the process noise. The storage requirements and calculations are reduced. The simulation results show its effectiveness.
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443-447
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Online since:
August 2013
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© 2013 Trans Tech Publications Ltd. All Rights Reserved
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