Title :
Practical fusion of quantized measurements via particle filtering
Author :
Ruan, Yanhua ; Willett, Peter ; Marrs, Alan ; Palmieri, Francesco ; Marano, Stefano
Author_Institution :
Duos Technol., Inc., Jacksonville
fDate :
1/1/2008 12:00:00 AM
Abstract :
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 quantization directly of measurements, the communication needs could be considerably cut. However, several issues were not discussed. The first of these is that estimation with quantized measurements requires an update with a non-Gaussian distribution, reflecting the uncertainty within the quantization "bin."; In general this would be a difficult task for dynamic estimation, but Markov-chain Monte-Carlo (MCMC, and specifically here particle filtering) techniques appear quite appropriate since the resulting system is, in essence, a nonlinear filter. The second issue is that in a realistic sensor network it is to be expected that measurements should arrive out-of-sequence. Again, a particle filter is appropriate, since the recent literature has reported particle filter modifications that accommodate nonlinear-filter updates based on new past measurements, with the need to refilter obviated. 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. In dynamic estimation this is a problem since both quantizer and fuser are working with only partial information; if measurements arrive out-of-sequence the problem is compounded. We therefore suggest architectures, and comment on their suitabilities for the task. A scheme based on delta-modulation appears to be promising.
Keywords :
Markov processes; Monte Carlo methods; nonlinear filters; particle filtering (numerical methods); quantization (signal); Markov-chain Monte-Carlo methods; decentralized estimation; delta-modulation scheme; dynamic estimation; intelligent quantization; local track estimates; nonGaussian distribution; nonlinear filters; particle filtering; quantization rule; quantized measurement fusion; Filtering; Intelligent sensors; Nonlinear dynamical systems; Particle filters; Particle measurements; Quantization; Sensor arrays; Sensor fusion; Sensor systems; Surveillance;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
DOI :
10.1109/TAES.2008.4516986