Array processing for target DOA, localization, and classification based on AML and SVM algorithms in sensor networks

Authors:
L. Yip;K. Comanor;J. C. Chen;R. E. Hudson;K. Yao;L. Vandenberghe
Affiliations:
Electrical Engineering Department, University of California at Los Angeles, Los Angeles, CA;Electrical Engineering Department, University of California at Los Angeles, Los Angeles, CA;Electrical Engineering Department, University of California at Los Angeles, Los Angeles, CA;Electrical Engineering Department, University of California at Los Angeles, Los Angeles, CA;Electrical Engineering Department, University of California at Los Angeles, Los Angeles, CA;Electrical Engineering Department, University of California at Los Angeles, Los Angeles, CA
Venue:
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Year:
2003

Citing 5
Cited 5

Fundamentals of statistical signal processing: estimation theory

Fundamentals of statistical signal processing: estimation theory
The nature of statistical learning theory

The nature of statistical learning theory
Wireless integrated network sensors

Communications of the ACM
An Integrated Architecture for Cooperative Sensing Networks

Computer
Maximum-likelihood source localization and unknown sensor locationestimation for wideband signals in the near-field

IEEE Transactions on Signal Processing

Acoustic laptops as a research enabler

Proceedings of the 4th workshop on Embedded networked sensors
Fault-tolerant target localization in sensor networks

EURASIP Journal on Wireless Communications and Networking
A support vector machine with integer parameters

Neurocomputing
A comparative study of feature extraction and classification methods for military vehicle type recognition using acoustic and seismic signals

ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Multi-modal calibration of surveillance sensor networks

MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose to use the Approximate Maximum-Likelihood (AML) method to estimate the direction-of-arrival (DOA) of multiple targets from various spatially distributed sub-arrays, with each subarray having multiple acoustical/seismic sensors. Localization of the targets can with possibly some ambiguity be obtained from the cross bearings of the sub-arrays. Spectra from the AML-DOA estimation of the target can be used for classification as well as possibly to resolve the ambiguity in the localization process. We use the Support Vector Machine (SVM) supervised learning method to perform the target classification based on the estimated target spectra. The SVM method extends in a robust manner to the nonseparable data case. In the learning phase, classifier hyperplanes are generated off-line via a primal-dual interior point method using the training data of each target spectra obtained from a single acoustical/seismic sensor. In the application phase, the classification process can be performed in real-time involving only a simple inner product of the classifier hyperplane with the AML-DOA estimated target spectra vector. Analysis based on Cramér-Rao bound (CRB) and simulated and measured data is used to illustrate the effectiveness of AML and SVM algorithms for wideband acoustical/seismic target DOA, localization, and classification.