Noise enhanced detection in restricted Neyman-Pearson framework
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Hypothesis tests frequently arise in many different engineering problems. Among the most frequently used tests are Bayesian, minimax, and Neyman-Pearson. Even though these tests are capable of addressing many real-life problems, they can be insufficient in certain scenarios. For this reason, developing new hypothesis tests is an important objective. One such developed test is the restricted NeymanPearson test, where one tries to maximize the average detection probability while keeping the worst-case detection and false-alarm probabilities bounded. Finding the best hypothesis testing approach for a problem-at-hand is an important point. Another important one is to employ a detector with an acceptable performance. In particular, if the employed detector is suboptimal, it is crucial that it meets the performance requirements. Previous research has proven that performance of some suboptimal detectors can be improved by adding noise to their inputs, which is known as noise enhancement. In this thesis we investigate noise enhancement according to the restricted Neyman-Pearson framework. To that aim, we formulate an optimization problem for optimal additive noise. Then, generic improvability and nonimprovability conditions are derived, which specify if additive noise can result in performance improvements. We then analyze the special case in which the parameter space is discrete and finite, and show that the optimal noise probability density function is discrete with a certain number of point masses. The improvability results are also extended and more precise conditions are derived. Finally, a numerical example is provided which illustrates the theoretical results and shows the benefits of applying noise enhancement to a suboptimal detector.