An approximate CPHD filter for superpositional sensors
MetadataShow full item record
Most multitarget tracking algorithms, such as JPDA, MHT, and the PHD and CPHD filters, presume the following measurement model: (a) targets are point targets, (b) every target generates at most a single measurement, and (c) any measurement is generated by at most a single target. However, the most familiar sensors, such as surveillance and imaging radars, violate assumption (c). This is because they are actually superpositional-that is, any measurement is a sum of signals generated by all of the targets in the scene. At this conference in 2009, the first author derived exact formulas for PHD and CPHD filters that presume general superpositional measurement models. Unfortunately, these formulas are computationally intractable. In this paper, we modify and generalize a Gaussian approximation technique due to Thouin, Nannuru, and Coates to derive a computationally tractable superpositional-CPHD filter. Implementation requires sequential Monte Carlo (particle filter) techniques. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
Showing items related by title, author, creator and subject.
Papi, Francesco; Kim, Du Yong (2015)In this paper we present a general solution for multi-target tracking with superpositional measurements. Measurements that are functions of the sum of the contributions of the targets present in the surveillance area are ...
Papi, F.; Kim, Du Yong (2015)© 2015 EURASIP.In this paper we present a general solution for multi-target tracking problems with superpositional measurements. In a superpositional sensor model, the measurement collected by the sensor at each time step ...
Nannuru, S.; Coates, M.; Mahler, Ronald (2013)In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. ...