In this paper, we present an extended Kalman filter (EKF)-based solution for the estimation of the angular motion using a gyro-free inertial measurement unit (GF-IMU) built of twelve separate mono-axial accelerometers. Using such a GF-IMU produces a vector, which we call the angular information vector (AIV) that consists of 3D angular acceleration terms and six quadratic terms of angular velocities. We consider the multiple distributed orthogonal triads of accelerometers that consist of three nonplanar distributed triads equally spaced from a central triad as a specific case to solve. During research for the possible filter schemes, we derived equality constraints. Hence we incorporate the constraints in the filter to improve the accuracy of the angular motion estimation, which in turn improves the attitude accuracy (direction cosine matrix (DCM) or quaternion vector).

In this paper, we conduct a feasibility study of the wind energy potential in Gaza, which suffers from a severe shortage of energy supplies. Our calculated energy harvested from the wind is based on data for a typical meteorological year, which are fed into a small wind turbine of 5 kW power rating installable on the roof of residential buildings. The expected annual energy output at a height of 10 m amounts to 2695 kWh, but it can be increased by 35–125% at higher altitudes between 20 m and 70 m. The results also depict the great potential of wind energy to complement other renewable resources such as solar energy: the harvested energy of a wind system constitutes to up to 84% of the annual output of an equivalent power rating photovoltaic system and even outperforms the solar energy in the winter months. We also show that one wind turbine and one comparable photovoltaic system together could provide enough energy for 3.7 households. Hence, a combination of wind and solar energy could stabilize the decentralized energy production in Gaza. This is very important in a region where people seek to reach energy self-sufficient buildings due to the severe electricity shortage in the local grid.

Due to the complementary nature of inertial navigation system (INS) and Wi-Fi positioning principles, an INS/Wi-Fi integrated system is expected to form a low-cost and continuous indoor navigation solution with better performance than using the standalone systems. In this paper, we explore the integration of Wi-Fi measurements with data from microelectromechanical systems (MEMS) based inertial measurement unit (IMU) for indoor vehicle navigation. Two enhancements, which employ adaptive Kalman filtering (AKF) and vehicle constraints, for supporting the integrated system are presented. One field experiment has been conducted for estimating the trajectory of a mobile robot vehicle. The numerical results show that the enhanced integrated system provides higher navigation accuracy, compared to using standalone Wi-Fi positioning and conventional INS/Wi-Fi integration.

Because of the complementary nature of inertial measurement unit based pedestrian dead reckoning (PDR) and Wi-Fi positioning, the combination of both systems yields a synergetic effect resulting in higher navigation performance. Barometric sensors can provide height information for 2D/3D indoor navigation applications in multi-floor environments. In this paper, we explore the multi-sensor assisted pedestrian navigation. A PDR/Wi-Fi/barometer integrated system is presented. The adaptive Kalman filter is employed for sensor fusion, which can adapt dynamic noise statistics. One field experiment has been conducted in a multi-floor building. The numerical results are presented to show the navigation performance of the integrated system.

The integration of inertial sensors is a widely-adopted method for covering GPS outages. However, the position accuracy in vertical direction is often challenged in comparison with the one in the horizontal plane. This paper explores the possibility of using MEMS barometer as an augmentation in the GPS/INS integration in land application. A prototype system, composed of a commercial GPS receiver with low-cost MEMS IMU and barometer on a DSP platform, is implemented and described. In addition, the initial investigation of the MEMS barometer is addressed. With the field experiment data collected from a car-driving test in a hilly region, the benefit of barometer aiding is demonstrated.

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements’ produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters.