Detection and parameter estimation of a transient signal using order statistics

Abstract

Detection and parameter estimation of a transient signal in noise is a problem of many applications. It is characterized by the fact that some of the measurements consist of noise only. Modern statistical signal processing techniques are applied on a discrete version of the received data and are implemented by digital signal processing (DSP). In this correspondence, we show how order statistics (OS)-based signal processing, which is of a discrete nature, can be used for simultaneous detection and estimation of parameters (such as time of arrival and signal duration) of a sampled transient signal in white noise. We show that the resulting processors are more robust than the conventional processors, whereas their performance is about the same, at the cost of increased computational complexity.

Non‐cooperative burst detection and synchronisation in downlink TDMA‐based wireless communication networks

“…Transient signals are generally considered as short duration signals compared to the observation time. The aim of transient signal detector is to decide whether the observation consists of noise alone or the signal is embedded in noise [25–27]. Various detection schemes have been proposed for this purpose.…”

“…In [26], the performance of transient detection based on higher order moments is investigated and a detector based on the third‐order absolute moment is proposed. Order statistics (OS)‐based signal processing is also applied to transient detection, and it is shown that the duration of an unknown transient signal can be estimated by use of OS technique [27]. In [28], a method based on permutation entropy is introduced for recognition of starting time of a transient signal.…”

Optimal bayes joint decision and estimation

“…While JDE is an old problem and has been studied before, it has received increasing attention in recent years and efforts have been made to overcome the aforementioned drawbacks (see, e.g., [3], [10], [27]), especially for target inference (see, e.g., [1], [4]- [8], [11]- [17], [21]- [24], [26], [28]). In this paper, we propose an integrated approach to JDE based on a novel Bayes risk as a generalization of those for decision and estimation, respectively.…”