The Attitude Heading Reference System (AHRS) has been widely used to provide the position and orientation of a rigid body. A low cost MEMS based inertial sensor measurement unit (IMU) is a core device in AHRS. To improve the AHRS system performance, there is a need to develop (i) stochastic IMU error models and (ii) random noise minimization techniques. In this paper, we modify the Sage-Husa Adaptive Kalman Filter (SHAKF) to incorporate time-varying noise estimator and robustifier, termed as Modified Sage-Husa Adaptive Robust Kalman Filter (MSHARKF). In the proposed algorithm, a three segment approach is used to evaluate the adaptive scale factor followed by the learning statistics. The scale factor is iteratively updated in the MSHARKF equations. In addition, angle random walk (ARW) and bias instability (BI) errors are represented by state-space models. The proposed algorithm is applied to restrain the drift error and random noise in the MEMS IMUs signals. The performance of this algorithm is analyzed using Allan variance (AV) analysis for static signals whereas the Root Mean Square Error (RMSE) values are evaluated for dynamic signals. Experimental results demonstrate the effectiveness of MSHARKF in reducing the drift and random noise in static and dynamic conditions as compared with other existing algorithms. Finally, we present sufficient conditions for convergence proof of the MSHARKF algorithm.

MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman Filtering

Kalman filter for mobile-robot attitude estimation: Novel optimized and adaptive solutions

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

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FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

This paper introduces a solution for increasing the measurement quality of the Micro Electro Mechanical System (MEMS) accelerometers in practical applications. This solution consists of two divisions. The first division is a solid carrier with five planes on each of which, a 3-channel MEMS accelerometer is installed. The second division of the proposed solution is a novel but simple-structured software designed for achieving two crucial targets: removing spikes and noise from the output of the accelerometers without having any a priori information about the desired frequency bands. The second mission of the processing software is to participate all accelerometers according to their measurement quality for determining the solid case accelerations. The criterion for ranking the channels of the accelerometers is based on their correlation index with the most informative channels among all operating accelerometers. By some simulation studies, the accuracy of the proposed solution for correct identification of spikes is about 99.99%. Also, by adding randomly the random-walk noise and spikes to the channels of the embedded accelerometers and then by altering the amount of input noise, the relative error of the rigid body acceleration is obtained versus input acceleration quality. As an instance, by implementing the proposed noise and spike reduction algorithm to a measurement signal with the quality value about 0.90, the relative navigation error-rate is enhanced from 0.75 s −1 to 0.1 s −1 for linear positions and 1 s −1 to 0.12 s −1 for angular attitudes. Also, the full-scale error-rates associated with the aforementioned relative-error rates are 0.28 s −1 , 0.02 s −1 , 0.2 s −1 , 0.01 s −1 , respectively.

Cezary Kownacki

Papers

Optimization approach to adapt Kalman filters for the real-time application of accelerometer and gyroscope signals' filtering