Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks

Majdi Mansouri, Ilham Ouachani, Hichem Snoussi, Cédric Richard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

This work deals with the problem of target tracking in wireless sensor networks where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the variational filtering (VF) by quantizing the data collected by the sensors to higher levels respecting the tradeoff between the information relevance of sensor measurements and the energy costs. In fact, VF has been shown to be suitable to the communication constraints of sensor networks. Its efficiency relies on the fact that the online update of the filtering distribution and its compression are simultaneously performed. But till now, it has been used only for binary sensor networks. In this paper, we propose an adaptive quantization algorithm taking benefit from the VF properties. At each sampling instant, by minimizing the Cramér-Rao bound, the adaptive quantization technique provides the optimal number of quantization bits per observation. The computation of this criteria is based on the target position predictive distribution provided by the VF algorithm. The simulation results show that the adaptive quantization algorithm, for the same sensor transmitting power, outperforms both the VF algorithm using a fixed optimal quantization level (minimizing the MSE) and the VF algorithm based on binary sensors.

Original languageEnglish
Title of host publication2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Pages693-696
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 - Cardiff, United Kingdom
Duration: 31 Aug 20093 Sep 2009

Other

Other2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
CountryUnited Kingdom
CityCardiff
Period31/8/093/9/09

Fingerprint

Cramér-Rao Bound
Cramer-Rao bounds
Target Tracking
Target tracking
Wireless Sensor Networks
Wireless sensor networks
Quantization
Filtering
Sensors
Sensor networks
Sensor
Sensor Networks
Binary
Predictive Distribution
State-space Model
Probabilistic Model
Sampling
Instant
Communication
Compression

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

Cite this

Mansouri, M., Ouachani, I., Snoussi, H., & Richard, C. (2009). Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks. In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 (pp. 693-696). [5278482] https://doi.org/10.1109/SSP.2009.5278482

Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks. / Mansouri, Majdi; Ouachani, Ilham; Snoussi, Hichem; Richard, Cédric.

2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09. 2009. p. 693-696 5278482.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mansouri, M, Ouachani, I, Snoussi, H & Richard, C 2009, Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks. in 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09., 5278482, pp. 693-696, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09, Cardiff, United Kingdom, 31/8/09. https://doi.org/10.1109/SSP.2009.5278482
Mansouri M, Ouachani I, Snoussi H, Richard C. Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks. In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09. 2009. p. 693-696. 5278482 https://doi.org/10.1109/SSP.2009.5278482
Mansouri, Majdi ; Ouachani, Ilham ; Snoussi, Hichem ; Richard, Cédric. / Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks. 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09. 2009. pp. 693-696
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