Optimum distributed detection under the Neyman-Pearson criterion is considered for a general case with possibly dependent observations from sensor to sensor. The focus is on the parallel architecture. New necessary conditions are presented that relate the threshold used in the NP-optimum fusion rule to those used in the NP-optimum sensor rules. These results clearly illustrate that the necessar...

In-band spectrum sensing is an effective method of avoiding harmful interference with primary users in cognitive radio. The optimal NoQP Sensing is firstly discussed. Towards a general NoQP Sensing(In-band sensing method without quiet period) problem, we proved that the weighted energy detector is the optimal detector under Neyman-Pearson criterion. The performance of the general NoQP Sensing a...

Existing detection systems generally are operated using a fixed threshold and optimized to the Neyman–Pearson criterion. An alternative is Bayes detection, in which the threshold varies according to the ratio of prior probabilities. In a recursive target tracker such as the probabilistic data association filter (PDAF), such priors are available in the form of a predicted location and associated...

When the null or alternative hypothesis of a statistical testing problem is a composite of regions of varying dimensionality, the likelihood ratio test is statistically inappropriate. Its inappropriateness is revealed not by its performance under the Neyman-Pearson criterion but by the fact that it yields incorrect inferences in certain regions of the sample space due to its inability to adapt ...

The restricted Neyman-Pearson (NP) approach is studied for composite hypothesis-testing problems in the presence of uncertainty in the prior probability distribution under the alternative hypothesis. A restricted NP decision rule aims to maximize the average detection probability under the constraints on the worst-case detection and false-alarm probabilities, and adjusts the constraint on the w...

In this dissertation, we develop a novel cognitive radio (CR) architecture, referred to as the Radiobot [1], whose goals go beyond dynamic spectrum access (DSA) to achieve the main features of cognition, notably, self-learning and self-reconfiguration. The proposed CR architecture is based on a sequence of signal processing and machine learning techniques that enable the Radiobot to sense a wid...

Sensor networks have been shown to be useful in diverse applications. One of the important applications is the collaborative detection based on multiple sensors to increase the detection performance. To exploit the spectrum vacancies in cognitive radios, we consider the collaborative spectrum sensing by sensor networks in the likelihood ratio test (LRT) frameworks. In the LRT, the sensors make ...

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