Search

We propose computationally inexpensive and efficient solutions for signal activity detection of phase-shift keying (PSK) signals in additive white Gaussian noise. We consider the complex amplitude of the signal, as well as the information sequence, as the unknown parameters. In addition, the noise variance is assumed unknown. We derive the generalized likelihood ratio test (GLRT) and suggest a computationally efficient implementation thereof. Furthermore, we develop a new inexpensive detector for binary PSK signals, which we will refer to as the generalized energy detector. To evaluate the performance of these detectors, we attempt to derive a uniformly most powerful invariant (UMPI) test as an optimal detector. It turns out that the UMPI test exists only if the signal-to-noise ratio is known. We use this UMPI test in order to obtain an upper-bound performance for the evaluation of invariant detectors, such as the GLRT. Simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors. Index Terms--Additive Gaussian noise, binary detection problem, detection algorithms, generalized energy detector, generalized likelihood ratio (GLR), GLR detectors, invariances, invariant detectors, invariant hypothesis testing, matched filter detectors, maximal invariant statistic, maximum-likelihood (ML) detection, phase-shift keying (PSK), signal activity detection, signal classification, signal detection, uniformly most powerful invariant (UMPI) test.