Research work under Visvesvaraya YFRF

Abstract

This research work concentrates on developing new methodologies for weak signal detection, which are further used in the watermark detection application. The proposed detectors perform comparable to or better than most of the state-of-the-art techniques. Stochastic resonance plays a significant role in weak signal detection. Injection of precalculated noise is a big concern for us. Noises i.e., symmetric and asymmetric have been utilized for getting an improved version of the weak signal detector. With equality and non-equality constraints, we use the particle swarm optimization method to optimize the objective function. After that, we investigate a new detector based on the fractional operator. The noisy signal is convolved with the coefficients of fractional order filter. The proposed method has been tested with different standard performance parameters. The results of the proposed detector have been compared with state-of-the-art detectors such as a threshold detector (TD), neural network-based detector etc. The robustness of the proposed detector with respect to the parameters of signal and noise has also been explored. The problem of weak DC signal detection present in non-Gaussian noise is always a troublesome because of the non-linearity of the test statistic. We have used the concept of stochastic resonance in a conventional neural network-based detector. A predefined noise is injected during the back-propagation algorithm in order to minimize the error. The reduction in errors (in terms of enhancement in the probability of detection, $$P_D$$ at a constant value of the probability of false alarm, $$P_{FA}$$ ) and faster convergence give a right justification to use the concept of stochastic resonance in the neural network-based detector.