To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（...To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.展开更多
Aim Interactive multiple model(IMM) algorithm was introduced into two? stage estimation to improve the estimating accuracy for system position and velocity.Methods The state estimation was carried out in mixed coor...Aim Interactive multiple model(IMM) algorithm was introduced into two? stage estimation to improve the estimating accuracy for system position and velocity.Methods The state estimation was carried out in mixed coordinates according to the nonlinear measure equation, a generalized interactive acceleration compensation(IAC) algorithm in mixed coordinate was presented. Results Simulation result shows the estimation accuracy is improved through changing measure equation in polar coordinates. Conclusion The estimation accuracy for position and velocity estimation, has been improved greatly, and the proposed algorithm has the advantage of less calculating time comparing with other multiple model methods.展开更多
基金The National Natural Science Foundation of China(No.61273236)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1637),China Scholarship Council
文摘To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.
文摘Aim Interactive multiple model(IMM) algorithm was introduced into two? stage estimation to improve the estimating accuracy for system position and velocity.Methods The state estimation was carried out in mixed coordinates according to the nonlinear measure equation, a generalized interactive acceleration compensation(IAC) algorithm in mixed coordinate was presented. Results Simulation result shows the estimation accuracy is improved through changing measure equation in polar coordinates. Conclusion The estimation accuracy for position and velocity estimation, has been improved greatly, and the proposed algorithm has the advantage of less calculating time comparing with other multiple model methods.
