Sensor networks are fast-emerging as a powerful technology for many distributed surveillance and monitoring applications.
The crux of these applications lies in being able to compute various global characteristics, like contour, trajectory,
direction, velocity etc., of mobile phenomena. In this paper, we present algorithms based on in-network aggregation
to efficiently compute these global characteristics of phenomena by representing them as functions amenable
to in-network processing, at a very low communication cost. We present two versions of these algorithms; one for
post-event querying and the other for real-time warnings and include simulation results for all our algorithms.
Sensor networks have uses ranging from personal use in homes to large-scale military applications. The ability of a sensor network is only as good as the intelligence used to control and integrate the sensors into a single entity. In this paper, we look at the marriage of agent organizations with sensor networks to create a sensor organization.
Sensors are used to monitor and interpret many different environments and phenomena. The capability of a
sensor array or network is constrained first by the sensors included and secondly by how the sensors are allowed
to communicate and cooperatively work together. In this paper, we show how the combination of sensors, with
embedded intelligent capability, and multiagent organization systems are integrated to create a highly adaptive,
scalable and viable architecture to interpret task domains, typically monitored by a lower-functioning sensor
This paper demonstrates an image matching methodology for application
in automatic target recognition systems. This method is based on chunking of an image and can be applied to any image matching system that uses templates to match against a given input image. Using information theoretical measures, templates are divided into sub-parts, called chunks. These chunks are scored individually against corresopnding parts of an input image. Sub-part scoring adds the ability to distinguish poorly matching areas of the target from those that match well. If a very small set of chunks score significantly worse than the other chunks then the poor-scoring chunks maybe discarded. This increases the scores of an input image that is of the same class but there is little or no effect on the score of an input image that is of another class.
Variability within a class of target vehicles seriously impacts the performance of a target recognition system. In this paper we present two main ideas about handling the intra- class variability. First, we develop a metric to quantify and understand the extent of variability within a class and second, we examine the class of T-72 tanks from the MSTAR public release data sets and attempt to make templates representative of the whole class.
A classifier based on a syntactic approach is developed for High range resolution (HRR) radar target recognition. An attribute grammar is used to represent the structure of an HRR signature and an error-correcting parsing mechanism is implemented to extract peaks in the HRR profile and suppress the extraneous spikes. In the training phase, an error correcting grammatical inference technique is employed for structural inference of HRR signatures using a positive sample set. Recognition is done using a minimum distance classifier where Levenshtein error measure is used as the distance metric. The error-correcting parsing procedure for peak extraction is used to perform both inference and recognition. Experiments performed using public release MSTAR database indicate that this approach has sufficient discrimination power to perform target detection in HRR signatures.
Target recognition with an HRR signature can be viewed as consisting of pre-discriminant, discriminant, and post discriminant phases. The large variability and feature uncertainty of HRR signatures can, to a good extent, be handled by detailed modeling of underlying physical and electro-magnetic phenomena. However, some signature and feature variabilities pass through and continue to exist in the post-discriminant phase. A decision about the class of a signature must account for this residual uncertainty. In this paper we demonstrate an evidence theory based method for post- discriminant decision-making phase that minimizes the effects of the signature variability.
In this paper we describe the results of our investigation into the intra-class variability of a vehicle class (T-72 Tanks) from the perspective of an Automatic Target Recognition system. We examine the performance of synthesized vehicle models for ATR systems and demonstrate that these models fall within the bounds of the vehicle class set by the intra-class variability of the vehicle. We then demonstrate the relevance of the mean-square-error between an image chip and a template as a useful measure of distance between the two vehicles. We also show that it is possible to constitute a superior class representative and classifier by combining chips from two different vehicles while constructing the templates.
Many automatic target recognition systems work by matching test profiles against profile templates from various parts of the targets. These templates are generally constructed for uniform-width sub-targets constructed out of the targets being considered. In this paper we present a heuristic search based algorithm for constructing optimal-sized sub- targets which may be of varying widths. We show the improvements that ar obtained by constructing templates for sub-targets identified by the search algorithm. The reduction in mean square errors is reported and also the performance of two classification test runs.