Due to copyright restrictions, the access to the full text of this article is only available via subscription.Nakagami-m distribution is well known for its ability to model a number of probability density functions, be it symmetric or asymmetric. Many Maximum Likelihood parameter estimation techniques for this distribution have been proposed that use estimated higher order moments of the data. However, the required large amount of data may not always be available. This is a drawback of using moments based approaches. In this work we propose a Maximum Likelihood parameter estimation technique for Nakagami-m distribution by giving a closed form expression for it. We demonstrate the performance of the proposed approach using certain test cases and compare the same to conventional algorithms using moments. We show that the new algorithm can model those pdfs better which may be deviating slightly/morderately from Gaussian shape and hence alleviating the need for extra mixture components
The development of a Passive Infra-Red (PIR) sens ing based intrusion detection system is presented here having the ability to reject vegetative clutter and distinguish between human and animal intrusions. This has potential application to reducing human-animal conflicts in the vicinity of a wildlife park. The system takes on the form of a sensor-tower platform (STP) and was developed in-house. It employs a sensor array that endows the platform with a spatial-resolution capability.Given the difficulty of collecting data involving animal motion, a simulation tool was created with the aid of Blender and OpenGL software that is capable of quickly generating streams of human and animal-intrusion data. The generated data was then examined to identify a suitable collection of features that are useful in classification. The features selected corresponded to parameters that model the received signal as the super imposition of a fixed number of chirplets, an energy signature and a cross-correlation parameter. The resultant feature vector was then passed on to a Support Vector Machine (SVM) for classification. This approach to classification was validated by making use of real-world data collected by the STP which showed both STP design as well as classification technique employed to be quite effective. The average classification accuracy with both real and simulated data was in excess of 94%.
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