Accuracy-optimized quantization for high-dimensional data fusion

Summary form only given. Decentralized estimation is an essential problem for a number of data fusion applications. The accuracy can be defined in terms of the mean square quantization error, MSQE. In this work, a computationally efficient and robust algorithm was developed for high-dimensional and high-rate decentralized estimation scenarios. Experiments were performed on a 2-source 21-dimensional problem. The proposed algorithm is compared with standard vector quantization (VQ), due to lack of alternative high-rate and high-dimensional decentralized estimation algorithms. The results showed that the proposed algorithm was consistently more accurate than standard VQ and that the difference increased with increase in number of codewords and size of the data set.