On some centralized and distributed parametric and nonparametric detection schemes.

摘要

The problem of detection of a constant signal in additive noise is addressed. In centralized detectors all observations are obtained and processed at the same place, whereas in distributed detectors observations on the same phenomenon is obtained at several geographically dispersed local sensors. The local sensors transmit a condensed signal to a global decision maker. In this dissertation we address some problems under both categories.; We present centralized truncated sequential nonparametric detectors that can be implemented with a hard-limiter and a dead-zone limiter. These detectors are based on approximations of the sequential sign and the sequential conditional sign detectors. The sequential tests are modelled as Markov chains for design and performance evaluations. By using truncation the possibility of excessively long tests is removed. The approximations allow mathematically tractable design for constant false alarm rate (CFAR) performance. In comparison to the sequential sign and conditional sign detectors, the proposed detectors have slightly higher average sample numbers (ASN) under no signal and nominal signal conditions, but considerably lower ASN for intermediate signal strengths.; A number of distributed detection schemes are considered. Firstly, the optimum decision policy for a sequential fusion center with fixed local sensors is studied. The sequential fusion rule is numerically studied using Markov chain modelling. We then focus on deriving optimum M level quantizers at the local sensors where {dollar}M > 2{dollar}. The solutions are obtained assuming a fixed fusion rule using the Bayesian and the locally optimum detection criteria. The receiver operating characteristics for different values of M and a number of different fusion rules are compared. The performance improves with increasing values of M; however the communication cost also increases. Finally, the concept of multilevel quantization is extended to distributed nonparametric detection. Two schemes are presented which employ Wilcoxon statistics and generate M-level signals from the local sensors. Design issues and numerical performance evaluations of the proposed detectors are presented.