A forward-backward probability hypothesis density (PHD) smoother involving forward filtering followed by backward smoothing is proposed. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using finite set statistics (FISST) and standard point process theory. Unlike the forward PHD recursion, the proposed backward PHD recursion is exact and does not require the previous iterate to be Poisson. In addition, assuming the previous iterate is Poisson, the cardinality distribution and all moments of the backward-smoothed multi-target density are derived. It is also shown that PHD smoothing alone does not necessarily improve cardinality estimation. Using an appropriate particle implementation we present a number of experiments to investigate the ability of the proposed multi-target smoother to correct state as well as cardinality errors.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :A Linear-Quadratic Optimal Control Problem of Forward-Backward Stochastic Differential Equations With Partial InformationABSTRACT:This paper studies a linear-quadratic optimal control downside derived by forward-backward
PROJECT TITLE : Multi-Core Embedded Wireless Sensor Networks Architecture and Applications - 2014 ABSTRACT: Technological advancements in the silicon industry, as predicted by Moore's law, have enabled integration of billions
PROJECT TITLE : Joint Routing and Medium Access Control in Fixed Random Access Wireless Multihop Networks - 2014 ABSTRACT: We study cross-layer design in random-access-based fixed wireless multihop networks under a physical
PROJECT TITLE :A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data - 2013ABSTRACT:Feature selection involves identifying a subset of the most useful features that produces compatible results as

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry