COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Signal Processing and Communications Lab Seminars > Gaussian Particle Implementations of Probability Hypothesis Density Filters

## Gaussian Particle Implementations of Probability Hypothesis Density FiltersAdd to your list(s) Download to your calendar using vCal - Daniel Clark
- Thursday 22 February 2007, 13:00-14:00
- LR6, Engineering, Department of.
If you have a question about this talk, please contact Taylan Cemgil. The Probability Hypothesis Density (PHD) filter is a multiple-target filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic implementations of this technique have been developed. The first of which, known as the Particle PHD filter, requires clustering techniques to provide target state estimates which can lead to inaccurate estimates and is computationally expensive. The second algorithm, called the Gaussian Mixture PHD (GM-PHD) filter does not require clustering algorithms but is restricted to linear-Gaussian target dynamics, since it uses the Kalman filter to estimate the means and covariances of the Gaussians. Extensions for the GM-PHD filter allow for mildly non-linear dynamics using extended and Unscented Kalman filters. A new particle implementation of the PHD filter which does not require clustering to determine target states is presented. The PHD is approximated by a mixture of Gaussians, as in the GM-PHD filter but the transition density and likelihood function can be non-linear. The resulting filter no longer has a closed form solution so Monte Carlo integration is applied for approximating the prediction and update distributions. This is calculated using a bank of Gaussian particle filters, similar to the procedure used with the Gaussian sum particle filter. The new algorithm is derived and presented with simulated results. This talk is part of the Signal Processing and Communications Lab Seminars series. ## This talk is included in these lists:- All Talks (aka the CURE list)
- Cambridge Centre for Data-Driven Discovery (C2D3)
- Cambridge University Engineering Department Talks
- Cambridge talks
- Centre for Smart Infrastructure & Construction
- Chris Davis' list
- Featured lists
- Information Engineering Division seminar list
- Interested Talks
- LR6, Engineering, Department of
- School of Technology
- Signal Processing and Communications Lab Seminars
- Trust & Technology Initiative - interesting events
- bld31
- ndk22's list
- ob366-ai4er
- rp587
Note that ex-directory lists are not shown. |
## Other listsThinking Society MRC Centenary - Series of Public Lectures Nuclear Symposia## Other talksA polyfold lab report Plastics in the Ocean: Challenges and Solutions Assessment of data completeness in the National Cancer Registry and the impact on the production of Cancer Survival Statistics Highly Energy Efficient Key-value Store for In-network Computing UK 7T travelling-head study: pilot results Structural basis for human mitochondrial DNA replication, repair and antiviral drug toxicity Retinal mechanisms of non-image-forming vision A rose by any other name TBC The MHC ligandome of two contagious cancers within the Tasmanian devil population, Devil Facial Tumour 1 and Devil Facial Tumour 2 |