Multipath interference and non-line-of-sight (NLOS) reception are major error sources when using global navigation satellite systems (GNSS) in urban environments. A promising approach to minimize the effect of multipath interference and aid NLOS detection is vector tracking. Therefore the objective of this research is to assess vector tracking in a dense urban environment to determine its effect on multipath interference and NLOS reception. Here, a vector delay lock loop (VDLL) is implemented using an adaptive extended Kalman filter (EKF). This replaces the individual code-tracking loops and navigation filter but retains conventional carrier frequency tracking. The positioning and tracking performance of the conventional and vector tracking implementations with and without a strobe correlator are compared using intermediate frequency (IF) signals recorded in the Koto-Ku district of urban canyon Tokyo city environment. Both static and dynamic tests were performed. It is shown that vector tracking reduces the root mean square positioning error by about 30% compared to an equivalent conventional receiver in urban environments and is capable of detecting long-delay NLOS reception for a GPS receiver without any external aiding. Introduction Multipath interference and non-line of sight (NLOS) reception are major sources of error for

A development procedure for a low-cost attitude and heading reference system (AHRS) with a self-developed three-axis rotating platform has been proposed. The AHRS consists of one 3-axis accelerometer, three single-axis gyroscopes, and one 3-axis digital compass. Both the accelerometer and gyroscope triads are based on micro electro-mechanical system (MEMS) technology, and the digital compass is based on anisotropic-magnetoresistive (AMR) technology. The calibrations for each sensor triad are readily accomplished by using the scalar calibration and the least squares methods. The platform is suitable for the calibration and validation of the low-cost AHRS and it is affordable for most laboratories. With the calibrated parameters and data fusion algorithm for the orientation estimation, the self-developed AHRS demonstrates the capabilities of compensating for the sensor errors and outputting the estimated orientation in real-time. The validation results show that the estimated orientations of the developed AHRS are within the acceptable region. This verifies the practicability of the proposed development procedure.

The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging.

An attitude estimation method is presented for small unmanned aerial vehicles (UAVs) powered by a piston engine which is the major source of vibration. Vibration of the engine significantly degrades the accuracy of the inertial measurement unit, especially for low-cost sensors that are based on micro electro-mechanical system. Therefore, a vibration model for a small UAV is proposed in order to examine the influence of vibration on attitude estimation with different sensors. The model is derived based on spectrum analysis with short-time Fourier transform. The vibration is compared with the drift of the gyroscope in order to examine the impact on attitude estimation. An attitude estimation method that fuses the gyroscopes and single antenna global positioning system (GPS) is proposed to mitigate the influence of engine vibration and gyroscope drift. The quaternion-based extended Kalman filter is implemented to fuse the sensors. This filter fuses the angular rates measured by the gyroscopes and the pseudo-attitude derived from the GPS velocity to estimate the attitude of the UAV. Simulations and experiment results indicate that the proposed method performs well both in short-term and long-term accuracy even though the gyroscopes are affected by drift and vibration noise, while the pseudo-attitude contains severe noise.

The linear-quadratic-Gaussian (LQG) control synthesis has the advantage of dealing with the uncertain linear systems disturbed by additive white Gaussian noise while having incomplete system state information available for control-loop feedback. This paper hence explores the feasibility of designing and implementing a stability augmentation autopilot for fixed-wing unmanned air vehicles using the LQG approach. The autopilot is composed of two independently designed LQG controllers which control the longitudinal and lateral motions of the aircraft respectively. The corresponding linear models are obtained through a system identification routine which makes use of the combination of two well-established identification methods, namely the subspace method and prediction error method. The two identification methods complement each other well and this paper shows that the proposed system identification scheme is capable of attaining satisfactory state-space models. A complete autopilot design procedure is devised and it is shown that the design process is simple and effective. Resulting longitudinal and lateral controllers are successfully verified in computer simulations and actual flight tests. The flight test results are presented in the paper and they are found to be consistent with the simulation results.