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The problem of detecting target signals in clutter arises in various applications, such as radar, sonar, communication, active or passive electro-optical sensors, etc. In many instances, the signals or objects are dim or partially obscured in a severe clutter environment that can vary widely. The inherent difficulties of such a detection process are the limited prior information about the target signal and the statistical properties of the clutter. In this paper, the signal detection problem is reduced to the problem of detecting a change point in a sequence of the GLRT statistics. A change point is defined to be an index (tau) in a sequence x1, x2, ..., xT of the GLRT statistics such that x1, x2, ..., x(tau ) have a common distribution F0(x) and x(tau +1), ..., xT have a common distribution F1(x), where F0(x)does not equal F1(x). Note that there is no change point if (tau) equals T. Many authors have presented approaches to solving the above problem. These include tests for a change in mean level, likelihood ratio tests, Bayesian approaches to inference about (tau) , and distribution-free approaches. In order to solve the change point problem, i.e., to determine whether or not a change point exists in a sequence of the GLRT statistics, we use a method which makes no assumption about F0(x) and F1(x). Essentially, there are two problems associated with change point detection: detecting the change and making inferences about the change point. For solving these problems, a non-parametric technique is proposed. The test for testing the null hypothesis of 'no change' (clutter alone) against the alternative of 'change' (signal present) is based on a version of the Waerden statistic. Estimating the change point is based on a version of the Mann-Whitney statistic. The proposed procedure can be used for segmentation of non- stationary signals into 'homogeneous' parts. The problem of segmenting the homogeneous parts of a digital signal, or detecting abrupt changes in a signal, is a key point which frequently arises in various application areas where modeling and processing of non-stationary digital signals is required. The results of computer simulations confirm the validity of the theoretical predictions of performance of the suggested technique.
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