Signal Conditioning With Memory-Less Nonlinear Sensors

Abstract

Proposed in this paper is an off-line signal conditioning scheme for memoryless nonlinear sensors. In most sensor designs, a linear input-output response is desired. However, nonlinearity is present in one form or another in almost all real sensors and therefore it is very difficult if not impossible to achieve a truly linear relationship. Often sensor nonlinearity is considered a disadvantage in sensory systems because it introduces distortion into the system. Due to the lack of efficient techniques to deal with the issues of sensor nonlinearity, primarily nonlinear sensors tend to be ignored. In this paper, it is shown that there are certain advantages of using nonlinear sensors and nonlinear distortion caused by sensor nonlinearity may be effectively compensated. A recursive algorithm utilizing certain characteristics of nonlinear sensor functions is proposed for the compensation of nonlinear distortion and sensor noise removal. A signal recovery algorithm that implements this idea is developed. Not having an accurate sensor model will result in errors and it is shown that the error can be minimized with a proper choice of a convergence accelerator whereby stability of the developed algorithm is established.