Distributed sensor networks are a significant technology nowadays. Inexpensive, smart devices with multiple sensors provide opportunities for instrumenting, monitoring and controlling targeting systems. Such sensor nodes have capability for acquiring and embeddedprocessing of variety of data forms. Collaborative signal processing and fusion algorithms are needed to aggregate the distributed data from among the nodes in the network, including possibly multiple modalities of data within a sensor node, to make decisions in a reliable and efficient manner. One of the important sensor network applications is target classification in battlefields. This paper presents improved moving vehicle target classification performance using data obtained from sensor networks with collaboration both across nodes and within a node in terms of multimodal fusion. Results show that a 50% relative improvement in classification error can be obtained using collaboration both in the case of single vehicle target and those involving multi-vehicle convoys.from among the nodes in the network, including possibly multiple modalities of data within a sensor node, to make decisions in a reliable and efficient manner. Even though problems in the fields of array signal processing and data fusion have been studied for a number of years, advances in sensor technologies, especially those aimed at military applications, have created new scenarios for applying signal processing ideas. One of the important sensor network applications is target classification in battlefields e.g. identifying types of moving vehicles in a field. Recently, this specific application has been discussed in the context of single sensor node processing [4] of single vehicle targets. This paper presents improved performance of classification algorithms applied over the sensor network with collaboration within a node, in terms of multimodal fusion, and across nodes in two different approaches i.e. data sharing and statistical confidence boost techniques. Proposed classification algorithms are also applied to the multiple targets scenarios i.e., target involving multiple vehicles in a convoy. The emphasis is on experiments using real seismic and acoustic . data collected from the SITEXOO experiments performed as a part of the DARPA SensIT program [9].
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