Identification for temperature model of accelerometer based on proximal SVR and particle swarm optimization algorithms

The impact of temperature on accelerometer will directly influence the precision of the inertial navigation system (INS). To eliminate the measurement error of accelerometer, this paper proposes a proximal support vector regression (PSVR) algorithm for generating a linear or nonlinear regression which requires the solution to single system of linear equations. PSVR is used to identify the static temperature model of the accelerometer. In order to improve the identifying performance, the kernel parameters and penalty factors of PSVR are optimized by the canonical particle swarm optimization (CPSO). The experiments under different temperature conditions were conducted. The experimental results show that the proposed PSVR can correctly identify the static temperature model of quartz flexure accelerometer and is more efficient than those of the standard SVR and least square algorithm.