Abstract:
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to the Track-before-detect (TkBD) problem. It has been shown to giv...Show MoreMetadata
Abstract:
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to the Track-before-detect (TkBD) problem. It has been shown to give performance close to numerical approximations of the optimal Bayesian filter at a fraction of the computation cost. This paper will consider an implementation of the H-PMHT for non-linear non-Gaussian TkBD problems using a dynamic programming fixed-grid approximation through application of the Viterbi algorithm. This alternate H-PMHT implementation is compared with Kalman Filter and Particle Filter H-PMHT implementations via simulated single target scenarios.
Published in: 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)
Date of Conference: 03-05 December 2012
Date Added to IEEE Xplore: 17 January 2013
ISBN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Dynamic Programming ,
- Nonlinear Problem ,
- Simulation Scenarios ,
- Kalman Filter ,
- Single Target ,
- Particle Filter ,
- Bayesian Filtering ,
- Discretion ,
- State Space ,
- Measurement Points ,
- General Case ,
- Hidden Markov Model ,
- Average Probability ,
- Nonlinear Method ,
- Image Sensor ,
- Unit Variance ,
- Point Spread Function ,
- Target State ,
- Sequence Of States ,
- Nonlinear Estimation ,
- Maximum A Posteriori ,
- Completing The Square ,
- Auxiliary Function ,
- Extended Kalman Filter ,
- Synthetic Measure ,
- Linear Gaussian ,
- Initialization Procedure ,
- Algorithmic Components ,
- Local Plane ,
- Optimal Path
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Dynamic Programming ,
- Nonlinear Problem ,
- Simulation Scenarios ,
- Kalman Filter ,
- Single Target ,
- Particle Filter ,
- Bayesian Filtering ,
- Discretion ,
- State Space ,
- Measurement Points ,
- General Case ,
- Hidden Markov Model ,
- Average Probability ,
- Nonlinear Method ,
- Image Sensor ,
- Unit Variance ,
- Point Spread Function ,
- Target State ,
- Sequence Of States ,
- Nonlinear Estimation ,
- Maximum A Posteriori ,
- Completing The Square ,
- Auxiliary Function ,
- Extended Kalman Filter ,
- Synthetic Measure ,
- Linear Gaussian ,
- Initialization Procedure ,
- Algorithmic Components ,
- Local Plane ,
- Optimal Path
