MEMS sensor assisted terrestrial vehicular navigation on portable devices

We present a GNSS sensor blending approach employing MEMS sensors for terrestrial vehicular navigation using portable devices. GNSS systems perform poorly in poor satellite visibility conditions, and in such scenarios, sensor augmentation can help in improving the overall positioning accuracy and in reducing outages [1, 2, 3]. First, we address the task of providing sensor assistance to a GNSS solution using an Inertial Measurement Unit (IMU) which is docked to the vehicle in a fixed but arbitrary unknown orientation, and propose techniques for estimating the IMU attitude (orientation) even when the vehicle is in motion, with or without GNSS aiding. Next, we address the task of calibrating and blending information from low-cost (hence, low-grade) MEMS sensors which are common on portable devices. We propose a cross-coupled formulation of blending, wherein sensor calibration is performed external to the GNSS Position Engine (PE), in a dedicated Sensor Engine Extended Kalman Filter (SE EKF). The SE uses an unfiltered version of the fixes from PE for calibration, and the calibrated sensor assistance is in turn provided to the PE as a control input. This arrangement allows for a more generic and simplified abstraction of the interface for assistance information going into the PE, without burdening the PE with the additional task of tracking sensor errors, as is the case with other common approaches to blending, such as tight coupling [8]. In the context of sensor blending, we also address the issue of gravity leakage: road inclination changes resulting in a time-varying bias component on the drive-axis of the vehicle, making the accelerometer calibration unreliable when calibration is sparse. We propose an EKF-based strategy to selectively weaken the accelerometer blending while leaving the gyroscope blending undiluted, in order to strongly steer the evolution of the direction (heading) of the GNSS PE's velocity state while not as strongly influencing its magnitude (speed). We present performance results from field trials, demonstrating the efficacy of the proposed algorithms.