Particle Filtering With Soft State Constraints for Target Tracking

Particle Filtering For Target Tracking

Particle filtering is a sequential Monte Carlo technique that recursively computes the posterior probability density function using the concept of “Importance Sampling”. This paper considers the application of particle filtering technique to a target tracking application, in which a radar sends a signal towards a target and estimates the state (position and velocity) of the target using the obs...

A New Modified Particle Filter With Application in Target Tracking

The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome th...

Multiple Target Tracking With Constrained Motion Using Particle Filtering Methods

Particle filtering has been successfully used in target tracking applications when the state and measurement models are nonlinear and the associated noise is non-Gaussian. Among the most difficult scenarios of target tracking, are multiple target tracking (MTT) problems that require the application of realistic and often multiple kinematic models. Recently algorithms such as the independent par...

Particle Filtering for Multi-target Tracking and Sensor Management

Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications

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