Practical fusion of quantized measurements via particle filtering

Most treatments of decentralized estimation rely on some form of track fusion, in which local track estimates and their associated covariances are communicated. This implies a great deal of communication; and it was recently proposed that, by an intelligent direct quantization of measurements, the communication needs could be considerably cut. However, several issues were not discussed. The first of these, estimation with quantized measurements, would be a difficult task for dynamic estimation, but Markov-chain Monte-Carlo (MCMC), and specifically particle filtering techniques appear quite appropriate since the resulting system is, in essence, a nonlinear filter. For the second issue, out-of-sequence. arrival of measurements, a particle filter is again appropriate. We show results that indicate a compander/particle-filter combination is a natural fit, and, specifically, that quite good performance is achievable with only 2-3 bits per dimension per observation. The third issue is that intelligent quantization requires that both sensor and fuser share an understanding of the quantization rule, but in dynamic estimation, both quantizer and fuser are working with only partial information; the problem is worse if measurements arrive out of sequence. We therefore suggest architectures, and comment on their suitabilities for the task. A scheme based on Δ-modulation appears to be promising.