This paper presents an algorithm for calibrating erroneous tri-axis magnetometers in the magnetic field domain. Unlike existing algorithms, no simplification is made on the nature of errors to ease the estimation. A complete error model, including instrumentation errors (scale factors, nonorthogonality, and offsets) and magnetic deviations (soft and hard iron) on the host platform, is elaborated. An adaptive least squares estimator provides a consistent solution to the ellipsoid fitting problem and the magnetometer's calibration parameters are derived. The calibration is experimentally assessed with two artificial magnetic perturbations introduced close to the sensor on the host platform and without additional perturbation. In all configurations, the algorithm successfully converges to a good estimate of the said errors. Comparing the magnetically derived headings with a GNSS/INS reference, the results show a major improvement in terms of heading accuracy after the calibration.

Most portable systems like smart-phones are equipped with low cost consumer grade sensors, making them useful as Pedestrian Navigation Systems (PNS). Measurements of these sensors are severely contaminated by errors caused due to instrumentation and environmental issues rendering the unaided navigation solution with these sensors of limited use. The overall navigation error budget associated with pedestrian navigation can be categorized into position/displacement errors and attitude/orientation errors. Most of the research is conducted for tackling and reducing the displacement errors, which either utilize Pedestrian Dead Reckoning (PDR) or special constraints like Zero velocity UPdaTes (ZUPT) and Zero Angular Rate Updates (ZARU). This article targets the orientation/attitude errors encountered in pedestrian navigation and develops a novel sensor fusion technique to utilize the Earth’s magnetic field, even perturbed, for attitude and rate gyroscope error estimation in pedestrian navigation environments where it is assumed that Global Navigation Satellite System (GNSS) navigation is denied. As the Earth’s magnetic field undergoes severe degradations in pedestrian navigation environments, a novel Quasi-Static magnetic Field (QSF) based attitude and angular rate error estimation technique is developed to effectively use magnetic measurements in highly perturbed environments. The QSF scheme is then used for generating the desired measurements for the proposed Extended Kalman Filter (EKF) based attitude estimator. Results indicate that the QSF measurements are capable of effectively estimating attitude and gyroscope errors, reducing the overall navigation error budget by over 80% in urban canyon environment.

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Determining orientation with respect to a known reference plays an important role in almost all modes of navigation. As the sensors required for measuring magnetic field have found their way into portable navigation devices, researchers have started investigating their application to orientation estimation in different environments. Nevertheless, the success of these sensors for orientation estimation is conditioned by their capacity to sense Earth's magnetic field in environments full of magnetic anomalies like urban canyons and indoors. These artificial fields contaminate Earth's magnetic field measurements, making orientation estimation very difficult in heavily perturbed areas. To overcome the effect of magnetic anomalies, a perturbation mitigation technique is proposed that utilizes multiple magnetometers. This mitigation technique is then used for estimating Earth's magnetic field indoors thus providing users with better magnetic orientation estimates. Performance of the proposed mitigation technique is assessed for pedestrian navigation in a shopping mall.

In this paper we discuss ways to reduce the execution time of a software Global Navigation Satellite System (GNSS) receiver that is meant for offline operation in a cloud environment. Client devices record satellite signals they receive, and send them to the cloud, to be processed by this software. The goal of this project is for each client request to be processed as fast as possible, but also to increase total system throughput by making sure as many requests as possible are processed within a unit of time. The characteristics of our application provided both opportunities and challenges for increasing performance. We describe the speedups we obtained by enabling the software to exploit multi-core CPUs and GPGPUs. We mention which techniques worked for us and which did not. To increase throughput, we describe how we control the resources allocated to each invocation of the software to process a client request, such that multiple copies of the application can run at the same time. We use the notion of effective running time to measure the system's throughput when running multiple instances at the same time, and show how we can determine when the system's computing resources have been saturated.

Although the equations’ derivation in our paper published in Sensors 2011 [1] is correct, a typo has been found in the summarizing Equations (48) and (49). The dot on the B in the skew matrix should be removed. [...]