Two-Level Clustering-Based Target Detection Through Sensor Deployment and Data Fusion

Document Type

Conference Proceeding

Publication Date

9-5-2018

Abstract

Target detection is one fundamental problem in many sensor network-based applications, and is typically tackled in two separate stages for sensor deployment and data fusion. We propose an integrated solution, referred to as SSEM, which combines 2-level clustering-based sensor deployment and Source Strength Estimate Map-based data fusion for the detection of a single static or moving target. SSEM conducts the first level of clustering to determine a sensor deployment scheme and the second level of clustering to divide the deployed sensors into multiple subsets. For each sensor, the source strength is estimated at each grid point of the entire region based on a signal attenuation model, and for each subset of sensors, the target location is estimated using a strength distribution map-based statistical analysis method. A final detection decision is made by thresholding the clustering degree of the target location estimates computed by all subsets of sensors. Compared with traditional grid-based target detection methods, SSEM significantly reduces the computation complexity and improves the detection performance through an integrated optimization strategy. Extensive simulation results show the performance superiority of the proposed solution over several well-known methods for target detection.

Identifier

85054103170 (Scopus)

ISBN

[9780996452762]

Publication Title

2018 21st International Conference on Information Fusion Fusion 2018

External Full Text Location

https://doi.org/10.23919/ICIF.2018.8455623

First Page

2376

Last Page

2383

Grant

IAA HSHQDC-13-X-B0002

Fund Ref

U.S. Department of Homeland Security

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