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Abstract-Matching pursuits is a well known technique for signal representation and has also been used as a feature extractor for some classification systems. However, applications that use matching pursuits (MP) algorithm in their feature extraction stage are quite problem domain specific, making their adaptation for other types of problems quite hard. In this paper we propose a matching pursuits based similarity measure that uses only the dictionary, coefficients and residual information provided by the MP algorithm while comparing two signals. Hence it is easily applicable to a variety of problems. We show that using the MP based similarity measure for competitive agglomerative fuzzy clustering leads to an interesting and novel update equation that combines the standard fuzzy prototype updating equation with a term involving the error between approximated signals and approximated prototypes. The potential value of the similarity measure is investigated using the fuzzy K-nearest prototype algorithm of Frigui for a two-class, signal classification problem. It is shown that the new similarity measure significantly outperforms the Euclidean distance.

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Handheld sensors are commonly used to assist in landmine location and removal. A number of computer systems aimed at assisting humans in discriminating between buried mines and other objects have been developed. Each such system requires some protocol that involves sweeping the sensor over a region of ground using some set of patterns to search for objects (detection) and determine the nature of those objects (discrimination). The work reported here is an effort to determine an acceptable sweep pattern for mine/nonmine discrimination that provides good performance while still being simple for the operator to use. The paper describes a series of data collections and case studies employing a combined radar and metal detection sensor. The system was evaluated first using a robotic operator and later human operators. We discuss the application of a supervised learning system discriminator to each data set, and evaluate discrimination performance. We found that using a relatively simple sweep pattern, computer algorithms can achieve better discrimination performance than an expert human operator, and that (at least up to ten sweeps) our computer algorithm performs better with more sweeps over target.