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In this paper, we study the nonlinear regression problem in a network of nodes and introduce long short term memory (LSTM) based algorithms. In order to learn the parameters of the LSTM architecture in an online manner, we put the LSTM equations into a nonlinear state space form and then introduce our distributed particle filtering (DPF) based training algorithm. Our training algorithm asymptotically...
We propose a distributed particle filtering algorithm based on optimal fusion of local posterior estimates. We derive an optimal fusion rule from Bayesian statistics, and implement it in a distributed and iterative fashion via an average consensus algorithm. We approximate local posterior estimates as Gaussian mixtures, and fuse Gaussian mixtures through importance sampling. We prove that under certain...
Most distributed statistical signal processing methods assume conditionally uncorrelated sensor measurements although this assumption is often not satisfied. Here, we propose a distributed algorithm for decorrelating the sensor measurements in a wireless sensor network. The algorithm employs a matrix-valued Chebyshev approximation to achieve an approximate decorrelation using only local computations...
The paper proposes a near-optimal, distributed implementation of the particle filter for large scale dynamical systems with sparse measurements, as in wireless sensor networks (WSN) and power distribution systems. Compared to the centralized approach, the distributed implementation of the particle filter is computationally efficient and provides considerable transmission bandwidth savings due to a...
We present a distributed particle filtering scheme for time-space-sequential Bayesian state estimation in wireless sensor networks. Low-rate inter-sensor communications between neighboring sensors are achieved by transmitting Gaussian mixture (GM) representations instead of particles. The GM representations are calculated using a clustering algorithm. We also propose a ??look-ahead?? technique for...
Distributed PF (DPF) was used due to the limitation of nodespsila computing capacity inferring to the target tracking in a wireless sensor network (WSN). In this paper, a novel filtering method - DPF* in WSN is proposed. Instead of transferring value and weight of particles, Gaussian mixture model (GMM) is used to approximate the posteriori distribution, and only GMM parameters need to be transferred...
A distributed particle filtering approach is proposed for fault detection in dynamic systems, where an interacting multiple model particle filter is used at each sensor for joint discrete mode (denoting normal or faulty situations) and continuous state tracking. Adaptive approximations of local state posterior distributions by histograms are aggregated to obtain the final decision regarding the system...
Particle filtering is often applied to the problem of object tracking under non-Gaussian uncertainty: however, sensor networks frequently require that the implementation be local to the region of interest, eventually forcing the large, sample-based representation to be moved among power-constrained sensors. We consider the problem of successive approximation (i.e., lossy compression) of each sample-based...
This paper describes two methodologies for performing distributed particle filtering in a sensor network. It considers the scenario in which a set of sensor nodes make multiple, noisy measurements of an underlying, time-varying state that describes the monitored system. The goal of the proposed algorithms is to perform on-line, distributed estimation of the current state at multiple sensor nodes,...
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