Sensor Network Source Localization via Projection onto Convex Sets (POCS)

Hero, Alfred O.; Blatt, D.

doi:10.1109/icassp.2005.1415803

Cited by 76 publications

(60 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the energy decay model was verified by the field experiment in [10] and was also used in [4], [7], [12]. Each sensor node measures signal energy generated by a source located at an unknown location θ ∈ R 2 .…”

Section: Application To Acoustic Amplitude Sensor Casementioning

confidence: 99%

See 1 more Smart Citation

Region-Based Distributed Estimation Using Quantized Data

Kim

2014

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYIn this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators. key words: distributed estimation, maximum a posteriori (MAP), quantization, source localization, sensor networks

show abstract

Section: Application To Acoustic Amplitude Sensor Casementioning

confidence: 99%

“…The localization problem has been solved mostly through nonlinear least squares estimation, which is sensitive to local optima and saddle points. To overcome this drawback, alternative approaches that cast the problem as a convex feasibility problem have also been proposed [4].…”

Section: Introductionmentioning

confidence: 99%

Region-Based Distributed Estimation Using Quantized Data

Kim

2014

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Note that the energy decay model was verified by the field experiment in [8] and was also used in [3], [9]. Each node measures signal energy generated by a source located at an unknown location θ ∈ R 2 .…”

Section: Application To Acoustic Amplitude Sensor Casementioning

confidence: 99%

Quantizer Design Optimized for Distributed Estimation

Kim

2014

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYWe consider the problem of optimizing the quantizer design for distributed estimation systems where all nodes located at different sites collect measurements and transmit quantized data to a fusion node, which then produces an estimate of the parameter of interest. For this problem, the goal is to minimize the amount of information that the nodes have to transmit in order to attain a certain application accuracy. We propose an iterative quantizer design algorithm that seeks to find a non-regular mapping between quantization partitions and their codewords so as to minimize global distortion such as the estimation error. We apply the proposed algorithm to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our experiments demonstrate that a significant performance gain can be achieved by our technique as compared with standard typical designs and even with distributed novel designs recently published. key words: distributed estimation, quantizer design, non-regular quantizer, source localization, sensor networks

show abstract

“…To generate the signal energy measured at each node, we employ an energy decay model that was proposed and verified by a field experiment in [11] and was also used in [12][13][14]. Formally, the signal energy measurement at sensor i, denoted by z i , can be expressed as follows:…”

Section: Application To Source Localization In Acoustic Sensor Netmentioning

confidence: 99%

Cluster-Based Quantization and Estimation for Distributed Systems

Kim¹

2016

Journal of information and communication convergence engineerin

View full text Add to dashboard Cite

We consider a design of a combined quantizer and estimator for distributed systems wherein each node quantizes its measurement without any communication among the nodes and transmits it to a fusion node for estimation. Noting that the quantization partitions minimizing the estimation error are not independently encoded at nodes, we focus on the parameter regions created by the partitions and propose a cluster-based quantization algorithm that iteratively finds a given number of clusters of parameter regions with each region being closer to the corresponding codeword than to the other codewords. We introduce a new metric to determine the distance between codewords and parameter regions. We also discuss that the fusion node can perform an efficient estimation by finding the intersection of the clusters sent from the nodes. We demonstrate through experiments that the proposed design achieves a significant performance gain with a low complexity as compared to the previous designs.

show abstract

Sensor Network Source Localization via Projection onto Convex Sets (POCS)

Cited by 76 publications

References 10 publications

Region-Based Distributed Estimation Using Quantized Data

Region-Based Distributed Estimation Using Quantized Data

Quantizer Design Optimized for Distributed Estimation

Cluster-Based Quantization and Estimation for Distributed Systems

Contact Info

Product

Resources

About