Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed or decentralized estimation has often been considered in the context of parametric models. However, the success of parametric methods is limited by the appropriateness of the strong statistical assumptions made by the models. In this paper, a more flexible nonparametric model for distributed regression is considered that is applicable in a variety of WSN applications including field estimation. Here, starting with the standard regularized kernel least-squares estimator, a message-passing algorithm for distributed estimation in WSNs is derived. The algorithm can be viewed as an instantiation of the successive orthogonal projection (SOP) algorithm. Various practical aspects of the algorithm are discussed and several numerical simulations validate the potential of the approach.
Proc. SPIE. 5910, Advanced Signal Processing Algorithms, Architectures, and Implementations XV
KEYWORDS: Signal to noise ratio, Detection and tracking algorithms, Sensors, Error analysis, Interference (communication), Sensor networks, Sensor performance, Signal processing, Computer engineering, Signal detection
The performance of Bayesian detection of Gaussian signals using noisy observations is investigated via the error exponent for the average error probability. Under unknown signal correlation structure or limited processing capability it is reasonable to use the simple quadratic detector that is optimal in the case of an independent and identically distributed (i.i.d.) signal. Using the large deviations principle, the performance of this detector (which is suboptimal for non-i.i.d. signals) is compared with that of the optimal detector for correlated signals via the asymptotic relative efficiency defined as the ratio between sample sizes of two detectors required for the same performance in the large-sample-size regime. The effects of SNR on the ARE are investigated. It is shown that the asymptotic efficiency of the simple quadratic detector relative to the optimal detector
converges to one as the SNR increases without bound for any bounded spectrum, and that the simple quadratic detector performs as well as the optimal detector for a wide range of the correlation values at high SNR.
Rotocraft safety, survivability, and mission effectiveness depend on the structural integrity of dynamic components. The need exists to develop an on-board, continuous vibration diagnostic system to detect and to prognosticate faults in these components prior to failure. This paper overviews a generic fault detection, isolation, and estimation (FDIE) architecture for condition-based machinery maintenance applications. Neural network-based fault pattern recognition is used to analyze normal and defect vibration signatures in helicopter transmissions. Data from nine seeded-fault test-rig experiments, each corresponding to one of six different fault/no fault conditions, were used to train and evaluate polynomial neural networks at pattern classification tasks. Features were generated using the amplitude spectra of the time-series vibration signatures. The Algorithm for Synthesis of Polynomial Networks for Classification (CLASS), a neural network software package that utilizes a constrained, minimum-logistic-loss criterion for multiclass problem, was used to perform the pattern recognition tasks. By employing a multiple-look post-processing strategy, perfect vibration signature classification was achieved.