Projects per year
Abstract / Description of output
Motivated by object tracking applications with networked sensors, we consider multi sensor state space models. Estimation of latent parameters in these models requires centralisation because the parameter likelihood depend on the measurement histories of all of the sensors. Consequently, joint processing of multiple histories pose difficulties in scaling with the number of sensors. We propose an approximation with a node-wise separable structure thereby removing the need for centralisation in likelihood computations. When leveraged with Markov random field models and message passing algorithms for inference, these likelihoods facilitate decentralised estimation in tracking networks as well as scalable computation schemes in centralised settings. We establish the connection between the approximation quality of the proposed separable likelihoods and the accuracy of state estimation based on individual sensor histories. We demonstrate this approach in a sensor network self-localisation example.
Original language | English |
---|---|
Number of pages | 6 |
Publication status | Published - 20 Mar 2016 |
Event | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - China, Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016 https://www2.securecms.com/ICASSP2016/Default.asp |
Conference
Conference | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 |
---|---|
Abbreviated title | ICASSP 2016 |
Country/Territory | China |
City | Shanghai |
Period | 20/03/16 → 25/03/16 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- sensor networks
- hidden Markov models
- Markov random fields
- pseudo-likelihood
- simultaneous localisation and tracking
Fingerprint
Dive into the research topics of 'Distributed estimation of latent parameters in state space models using separable likelihoods'. Together they form a unique fingerprint.Projects
- 2 Finished
-
Signal Processing in the Networked Battlespace
Mulgrew, B., Davies, M., Hopgood, J. & Thompson, J.
1/04/13 → 30/06/18
Project: Research
-
-
Latent parameter estimation in fusion networks using separable likelihoods
Uney, M., Mulgrew, B. & Clark, D., 12 Apr 2018, In: IEEE Transactions on Signal and Information Processing Over Networks. 4, 4, p. 752 - 768 16 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Detection of manoeuvring low SNR objects in receiver arrays
Kim, K., Uney, M. & Mulgrew, B., 22 Sept 2016, Proceedings of the SSPD Conference 2016. 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile -
Distributed localisation of sensors with partially overlapping field-of-views in fusion networks
Uney, M., Mulgrew, B. & Clark, D., 5 Jul 2016. 8 p.Research output: Contribution to conference › Paper › peer-review
Open AccessFile