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Maximum Likelihood (ML)-Based Quantizer Design for Distributed Systems
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 Title & Authors
Maximum Likelihood (ML)-Based Quantizer Design for Distributed Systems
Kim, Yoon Hak;
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 Abstract
We consider the problem of designing independently operating local quantizers at nodes in distributed estimation systems, where many spatially distributed sensor nodes measure a parameter of interest, quantize these measurements, and send the quantized data to a fusion node, which conducts the parameter estimation. Motivated by the discussion that the estimation accuracy can be improved by using the quantized data with a high probability of occurrence, we propose an iterative algorithm with a simple design rule that produces quantizers by searching boundary values with an increased likelihood. We prove that this design rule generates a considerably reduced interval for finding the next boundary values, yielding a low design complexity. We demonstrate through extensive simulations that the proposed algorithm achieves a significant performance gain with respect to traditional quantizer designs. A comparison with the recently published novel algorithms further illustrates the benefit of the proposed technique in terms of performance and design complexity.
 Keywords
Distributed compression;Generalized Lloyd algorithm;Maximum likelihood;Quantizer design;Sensor networks;Source localization;
 Language
English
 Cited by
 References
1.
W. A. Hashlamoun and P. K. Varshney, “Near-optimum quantization for signal detection,” IEEE Transactions on Communications, vol. 44, no. 3, pp. 294-297, 1996. crossref(new window)

2.
M. Longo, T. D. Lookabaugh, and R. M. Gray, “Quantization for decentralized hypothesis testing under communication constraints,” IEEE Transactions on Information Theory, vol. 36, no. 2, pp. 241-255, 1990. crossref(new window)

3.
Y. H. Kim, “Functional quantizer design for source localization in sensor networks,” EURASIP Journal on Advances in Signal Processing, vol. 2013, pp. 1-10, 2013. crossref(new window)

4.
Y. H. Kim, “Quantizer design optimized for distributed estimation,” IEICE Transactions on Information and Systems, vol. 97, no. 6, pp. 1639-1643, 2014. crossref(new window)

5.
Y. H. Kim, “Weighted distance-based quantization for distributed estimation,” Journal of Information and Communication Convergence Engineering, vol. 12, no. 4, pp. 215-220, 2014. crossref(new window)

6.
Y. H. Kim and A. Ortega, "Quantizer design for source localization in sensor networks," in Proceedings of IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP2005), Philadelphia, PA, pp. 857-860, 2005.

7.
Y. H. Kim and A. Ortega, “Quantizer design for energy-based source localization in sensor networks,” IEEE Transactions on Signal Processing, vol. 59, no. 11, pp. 5577-5588, 2011. crossref(new window)

8.
W. Lam and A. Reibman, “Design of quantizers for decentralized estimation systems,” IEEE Transactions on Communications, vol. 41, no. 11, pp. 1602-1605, 1993. crossref(new window)

9.
N. Wernersson, J. Karlsson, and M. Skoglund, “Distributed quantization over noisy channels,” IEEE Transactions on Communications, vol. 57, no. 6, pp. 1693-1700, 2009. crossref(new window)

10.
Y. H. Kim and A. Ortega, “Distributed encoding algorithms for source localization in sensor networks,” EURASIP Journal on Advances in Signal Processing, vol. 2010, article ID. 781720, pp. 1-13, 2010.

11.
D. Li and Y. H. Hu, “Energy-based collaborative source localization using acoustic microsensor array,” EURASIP Journal on Applied Signal Processing, vol. 2003, no. 4, pp. 321-337, 2003. crossref(new window)

12.
J. Liu, J. Reich, and F. Zhao, “Collaborative in-network processing for target tracking,” EURASIP Journal on Applied Signal Processing, vol. 2003, no. 4, pp. 378-391, 2003. crossref(new window)

13.
A. O. Hero and D. Blatt, "Sensor network source localization via projection onto convex sets (POCS)," in Proceedings of IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP2005), , pp. 689-692, 2005.

14.
Y. H. Kim and A. Ortega, "Maximum a posteriori (MAP)-based algorithm for distributed source localization using quantized acoustic sensor readings," in Proceedings of IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP 2006), Toulouse, France, 2006.