Consistency of stochastic context-free grammars and application to stochastic parsing of GMTI tracker data

Bhashyam Balaji

doi:10.1117/12.921155

17 May 2012 Consistency of stochastic context-free grammars and application to stochastic parsing of GMTI tracker data

Bhashyam Balaji

Proceedings Volume 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI; 83920S (2012) https://doi.org/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.

Citation Download Citation

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 (17 May 2012); https://doi.org/10.1117/12.921155

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