Now showing items 1-5 of 5
Quantization and Fusion for Multi-Sensor Discrimination from Dependent Observations
Schemes for quantization and fusion in multi-sensor systems used for discriminating between two sequences of dependent observations are introduced and analyzed. The observation sequences of each sensor under the two ...
Neural Networks for Sequential Discrimination of Radar Targets
In this paper, perceptron neural networks are applied to the problem of discriminating between two classes of radar returns. The perceptron neural networks are used as nonlinearities in two threshold sequential discriminators ...
Analysis of Compressive Receivers for the Optimal Interception of Frequency-Hopped Waveforms
This paper establishes that the compressive receiver is a practical interceptor of high performance. Given a signal of a particular duration, a compressive receiver can estimate simultaneously all frequency components ...
Multi-Sensor Correlation and Quantization in Distributed Detection Systems
Quantization and fusion schemes are derived for multi-sensor correlation in distributed K- sensor systems that are used for the detection of weak signals or general signal discrimination from dependent observations. The ...
Robust Sequential Tests for Memoryless Discrimination from Dependent Observations
The problem of robust sequential discrimination from two dependent observation sequences with uncertain statistics is addressed. As in Part I () of this study, which treated asymptotically optimal sequential discrimination ...