A Nonlinear Multiparameters Temperature Error Modeling and Compensation of POS Applied in Airborne Remote Sensing System

Abstract

The position and orientation system (POS) is a key equipment for airborne remote sensing systems, which provides high-precision position, velocity, and attitude information for various imaging payloads. Temperature error is the main source that affects the precision of POS. Traditional temperature error model is single temperature parameter linear function, which is not sufficient for the higher accuracy requirement of POS. The traditional compensation method based on neural network faces great problem in the repeatability error under different temperature conditions. In order to improve the precision and generalization ability of the temperature error compensation for POS, a nonlinear multiparameters temperature error modeling and compensation method based on Bayesian regularization neural network was proposed. The temperature error of POS was analyzed and a nonlinear multiparameters model was established. Bayesian regularization method was used as the evaluation criterion, which further optimized the coefficients of the temperature error. The experimental results show that the proposed method can improve temperature environmental adaptability and precision. The developed POS had been successfully applied in airborne TSMFTIS remote sensing system for the first time, which improved the accuracy of the reconstructed spectrum by 47.99%.

Method of temperature compensation to spin-valve sensor based on PGA

“…However, when applied in a large range of temperature variation, a temperature compensation is needed to calibrate the output of the spin-valve sensors, in order to complete high precision measurement, such as smart meters, security systems. A very common method of temperature compensation is software-based compensation method [5], which establishes mathematical model for the output data of sensors, and analyzes and processes through a dedicated algorithm, ultimately realizes temperature compensation, such as least square method [6], particle swarm optimal algorithm [7], BP neural network algorithm [8]. But these methods have complicated calculation steps and need a long time optimization.…”