Optimization of Threshold Values for Estimators Based on Single-Bit Quantized Sensors Using Genetic Algorithms
The paper considers the problem of signal parameter estimation using a collection of distributed sensors called a sensor pack. Each sensor quantizes its data to one-bit information and sends it to a fusion processor for the estimation of the parameter. Estimation of a constant signal in additive noise is considered. Estimators are formulated based on one-bit sensor data and their mean squared error (MSE) performances are evaluated through simulation studies. It is shown that selecting certain threshold values for quantizing the sensor outputs can lower the MSE. Genetic algorithms are used to find the optimal threshold values for the sensors. Results from this study show that robust estimation of parameter is possible by using a moderately large number of one-bit quantized sensor data. This work has significance in applications that demand high reliability in sensor networks in spite of sensor failures, limited sensor dynamic range, resolution, bandwidth for data transmission or even data storage.
ASME 2002 International Mechanical Engineering Congress and Exposition
Unnikrishnan, Nishant; Mahajan, Ajay; Mengoulis, Antonios; and Viswanathan, R., "Optimization of Threshold Values for Estimators Based on Single-Bit Quantized Sensors Using Genetic Algorithms" (2002). Mechanical Engineering Faculty Research. 556.