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22 October 1993 Comparison of different parallel filtering techniques
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Abstract
Tracking algorithms commonly use practical models of target motion to estimate the target's kinematic quantities such as the position, the velocity and in certain cases, the acceleration. When there is a maneuver, the tracking algorithm should detect the error created by this change and correct the situation to adapt itself to this new change or new tracking model. There are different approaches in the literature for handling maneuver detection using different filtering techniques. A thorough literature survey about different types of filtering techniques used for maneuver detection has been performed. The focus of this study has been the parallel filtering techniques. Some of those techniques given by different authors are summarized in this paper. This paper presents a parallel filter design using three linear Kalman filters with a simple switching algorithm for maneuver detection selected for the Multi Sensor Data Fusion (MSDF) for an anti-air warfare (AAW) surveillance radar. This design is relatively simple compared to other parallel Kalman filter techniques and requires modest computer resources. The parallel filter design has been compared with a single Kalman filter design previously used. The simulation results have shown a great deal of improvement with parallel filtering, particularly in speed estimations and in filtering stability when a target is maneuvering.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sylvain Bourassa, Pierre Fontaine, Elisa Shahbazian, and Marc-Alain Simard "Comparison of different parallel filtering techniques", Proc. SPIE 1954, Signal and Data Processing of Small Targets 1993, (22 October 1993); https://doi.org/10.1117/12.157772
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