18 May 2012 Consistency of stochastic context-free grammars and application to stochastic parsing of GMTI tracker data
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Proceedings Volume 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI; 83920S (2012); doi: 10.1117/12.921155
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States
Abstract
Conventional trackers provide the human operator with estimated target tracks. It is desirable to make higher level inference of the target behaviour/intent (e.g., trajectory inference) in an automated manner. One such approach is to use stochastic context-free grammars and the Earley-Stoelcke parsing algorithm. The problem of inference is reformulated as one of parsing. In this paper, the consistency of stochastic context-free grammars is reviewed. Some examples illustrating the constraints on SCFGs due to consistency are presented, including a toy SCFG that has been used to successfully parse real GMTI radar data.
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Bhashyam Balaji, "Consistency of stochastic context-free grammars and application to stochastic parsing of GMTI tracker data", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920S (18 May 2012); doi: 10.1117/12.921155; https://doi.org/10.1117/12.921155
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KEYWORDS
Stochastic processes

Radar

Target detection

Signal processing

Lanthanum

Process modeling

Detection and tracking algorithms

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