Frequency estimation is a common problem in a variety of applications. In recent years, adaptive notch filtering methods have been widely adopted for solving frequency estimation problems. For frequency estimation per...
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Frequency estimation is a common problem in a variety of applications. In recent years, adaptive notch filtering methods have been widely adopted for solving frequency estimation problems. For frequency estimation performed by a single node, the sampling time may not be long enough, the sampling rate may not be high enough, and the noise effect may be serious. In such situations, most existing algorithms for frequency estimation can not produce accurate enough results. Here, we propose using wireless sensor networks for sampling data distributedly, and using distributed notch filtering method for dealing with this problem. In particular, we propose two distributed algorithms over sensor network, a least mean square-based distributed notch filtering (dNF) algorithm and a total least square-based dNF algorithm. The communication cost of the new proposed algorithms is low, as each node exchanges only with its neighbors the estimates other than the original data. The proposed algorithms are applied to both synthetic and real examples. Simulation results demonstrate the effectiveness of the new proposed algorithms.
Non-negative matrix factorization (NMF) has found use in fields such as remote sensing and computer vision where the signals of interest are usually non-negative. Data dimensions in these applications can be huge and ...
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ISBN:
(纸本)9781479999897
Non-negative matrix factorization (NMF) has found use in fields such as remote sensing and computer vision where the signals of interest are usually non-negative. Data dimensions in these applications can be huge and traditional algorithms break down due to unachievable memory demands. One is then compelled to consider distributed algorithms. In this paper, we develop for the first time a distributed version of NMF using the alternating direction method of multipliers (ADMM) algorithm and dyadic cyclic descent. The algorithm is compared to well established variants of NMF using simulated data, and is also evaluated using real remote sensing hyperspectral data.
This paper introduces the vector sparse matrix transform (vector SMT), a new decorrelating transform suitable for performing distributedprocessing of high-dimensional signals in sensor networks. We assume that each s...
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This paper introduces the vector sparse matrix transform (vector SMT), a new decorrelating transform suitable for performing distributedprocessing of high-dimensional signals in sensor networks. We assume that each sensor in the network encodes its measurements into vector outputs instead of scalar ones. The proposed transform decorrelates a sequence of pairs of vector outputs, until these vectors are decorrelated. In our experiments, we simulate distributed anomaly detection by a network of cameras, monitoring a spatial region. Each camera records an image of the monitored environment from its particular viewpoint and outputs a vector encoding the image. Our results, with both artificial and real data, show that the proposed vector SMT transform effectively decorrelates image measurements from the multiple cameras in the network while maintaining low overall communication energy consumption. Since it enables joint processing of the multiple vector outputs, our method provides significant improvements to anomaly detection accuracy when compared with the baseline case when the images are processed independently.
The paper considers the problem of dynamic sensor scheduling for non-linear tracking problems in distributed sensor/agent networks (AN/SN), where channel limitations restrict how many sensors can simultaneously partic...
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The paper considers the problem of dynamic sensor scheduling for non-linear tracking problems in distributed sensor/agent networks (AN/SN), where channel limitations restrict how many sensors can simultaneously participate in the estimation mechanism. Commonly referred to as sensor selection, the basic objective is to select a subset of sensors from available sensors that minimises the estimation error. The posterior Cramer-Rao lower bound (PCRLB) has recently been proposed as an effective sensor selection criteria. Nevertheless, existing PCRLB-based selectors are limited to centralised and hierarchical networks, and when extended to distributed architectures use approximate expressions. First, the non-conditional distributed PCRLB (dPCRLB) that considers observations and state variables to be random is used to derive the non-conditional sensor-selector. The dPCRLB expressions we derive are optimal. Unlike the non-conditional PCRLB, its conditional counterpart is a function of the past history of observations and is a more accurate representation of the system's performance. Second, the paper generalises the non-conditional dPCRLB sensor-selector to its conditional dPCRLB version. Both dPCRLB sensor-selectors use raw observations adding significant communication overhead. Third, the paper extends the conditional dPCRLB framework to quantised observations and develops the quantised version of the conditional dPCRLB sensor-selector. Numerical simulations verify the efficiency of our distributed dynamic sensor-selectors. (C) 2014 Elsevier B.V. All rights reserved.
In this letter, we present an eavesdropping-based gossip algorithm (EBGA). In the novel algorithm, when a node unicasts its values to a randomly selected neighboring node, all other nodes, which eavesdrop these values...
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In this letter, we present an eavesdropping-based gossip algorithm (EBGA). In the novel algorithm, when a node unicasts its values to a randomly selected neighboring node, all other nodes, which eavesdrop these values, simultaneously update their state values. By exploiting the broadcast nature of wireless communications, this novel algorithm has similar performance to broadcast gossip algorithms. Although broadcast gossip algorithms have the fastest rate of convergence among all gossip algorithms, they either converge to a random value rather than the average consensus, or need out-degree information available for each node to guarantee convergence to the average consensus. Utilizing non-negative matrix theory and ergodicity coefficient, we have proved that this novel algorithm can converge to the average consensus without any assumption which is difficult to be realized in real networks.
In this paper, we address the problem of estimating the cross-correlation function between two microphone signals recorded in different nodes of an ad-hoc microphone array or wireless acoustic sensor network, where th...
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ISBN:
(纸本)9780992862633
In this paper, we address the problem of estimating the cross-correlation function between two microphone signals recorded in different nodes of an ad-hoc microphone array or wireless acoustic sensor network, where the transmission of the entire microphone signal from one node to another is undesirable due to power and/or bandwidth constraints. We show that instead of directly computing the cross-correlation function, it can be estimated as the solution to a deconvolution problem. This deconvolution problem can be separated into two subproblems, each of which depends on one microphone signal and an auxiliary signal derived from the other microphone signal. Three different strategies for solving this deconvolution problem are proposed, in which the two subproblems are solved jointly (symmetric deconvolution), separately (asymmetric deconvolution) or in a consensus framework (consensus deconvolution). Simulation results illustrate the performance difference in terms of estimation accuracy, noise robustness, and transmission requirements.
The distributed subspace pursuit (DSP) algorithm has been proposed for sparse signal detection with a sensor network. The experimental investigation on the detection probability of DSP has been provided, but the theor...
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ISBN:
(纸本)9781479919482
The distributed subspace pursuit (DSP) algorithm has been proposed for sparse signal detection with a sensor network. The experimental investigation on the detection probability of DSP has been provided, but the theoretical analysis on this issue is not clear yet. In this paper, a lower bound of the detection probability of DSP is theoretically analyzed. Experimental evaluations show that our theoretical results are reasonable.
We examine the design of self-organizing mobile adaptive networks with multiple targets in which the network nodes form distinct clusters to learn about and purse multiple targets, all while moving in a cohesive colli...
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This paper studies a Quantized Gossip-based Interactive Kalman Filtering (QGIKF) algorithm implemented in a wireless sensor network, where the sensors exchange their quantized states with neighbors via inter-sensor co...
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distributed algorithms allow wireless acoustic sensor networks (WASNs) to divide the computational load of signalprocessing tasks, such as speech enhancement, among the sensor nodes. However, current algorithms focus...
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ISBN:
(纸本)9780992862633
distributed algorithms allow wireless acoustic sensor networks (WASNs) to divide the computational load of signalprocessing tasks, such as speech enhancement, among the sensor nodes. However, current algorithms focus on performance optimality, oblivious to the energy constraints that battery-powered sensor nodes usually face. To extend the lifetime of the network, nodes should be able to dynamically scale down their energy consumption when decreases in performance are tolerated. In this paper we study the relationship between energy and performance in the DANSE algorithm applied to speech enhancement. We propose two strategies that introduce flexibility to adjust the energy consumption and the desired performance. To analyze the impact. of these strategies we combine an energy model with simulations. Results show that the energy consumption can be substantially reduced depending on the tolerated decrease in performance. This shows significant potential for extending the network lifetime using dynamic system reconfiguration.
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