Signal recovery and noise removal with memoryless sensors

Proposed in this paper is the design of a new generation of memoryless nonlinear sensors. One of the most important parameters required in most sensor designs is an essentially linear output. However, nonlinearity is present in one form or another in almost all real sensors and therefore it is very difficult to achieve a truly linear relationship. An approach that utilizes certain characteristics of nonlinear sensor functions in order to compensate nonlinear distortion and to remove sensor noise by a few step iterative process is advocated. A signal recovery algorithm that implements this idea is developed. Not having an accurate sensor will result in errors and it is shown that the error can be minimized with a proper choice of a convergence parameter whereby stability of the developed algorithm is established.