The introduction of direction finding in the Bluetooth standard enabled the use of antenna arrays for locating Bluetooth devices, using carrier phase measurements to estimate the direction from the array to a moving device. In this work, this feature is utilized for outdoor localization. We show how using repeated measurements from all array elements, instead of only the initial single-element reference samples as often suggested, can contribute to an improved estimate of the signal's unknown carrier frequency offset, thereby improving the direction estimation performance. To run the direction-of-arrival estimation in real-time with high angular resolution on an embedded computer we propose a pseudospectrum peak search strategy that combines a coarse search, where the resolution is decided based on the array's main lobe width, with a local nonlinear optimization for estimate refinement. We consider practical aspects relating to the phase sampling configuration and demonstrate direction estimation at up to 700m range with insignificant packet loss within 500m, and without significant loss of angular precision even when the received signal is near the receiver's signal strength sensitivity threshold. In an open outdoor environment, using a square antenna array with 12 elements, the azimuthal performance is found to be very consistent with range, with noise standard deviation typically around 1°. While the elevation is significantly affected by multipath at lower elevation angles, with visible disagreement between frequency channels, it is shown to be consistent with simulations of ground reflection multipath.
For outdoor navigation using Bluetooth direction finding, elevation angle estimate errors due to ground reflection multipath interference is a significant challenge at low elevation angles. One of the ways to reduce this issue is to increase the vertical dimension of the array. We consider the synchronization of measurements from two independent arrays stacked vertically to achieve the same effect without needing to construct a larger array, allowing hardware modularity using low-cost equipment. The measurement synchronization is itself affected by the multipath, and a method to handle this effect is proposed. Using measurements from a 15 cm x 15 cm array in field experiments, we demonstrate a reduction in elevation error of up to 10°at elevation angles in the 7°to 15°range, where the error was largest when using a single array.
This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.
Increasing use of UAVs in high-precision applications, such as georeferencing and photogrammetry, increases the requirements on the accuracy of the estimated position, velocity and attitude of the vehicle. Commercial systems that utilize magnetometers in the heading estimates are cheap, but are affected by disturbances from both the vehicle itself, nearby metal structures and variations in the Earth's magnetic field. On the other side, commercial dual-antenna satellite navigation systems can provide the required accuracy, but are expensive. This paper explores the use of a low-cost setup using two independent GNSS receivers, aiding an inertial navigation system by using pseudorange, Doppler frequency and carrier phase measurements from two longitudinally separated receivers on a fixed-wing UAV. The sensor integration was based on a multiplicative extended Kalman filter (MEKF). The main contribution of this paper is the derivation of measurement models for the raw GNSS measurements based on the MEKF error state, taking into account antenna lever arms and explicitly including the difference in measurement time between the receivers in the measurement model for double differenced carrier phase. The proposed method is verified using data collected from a UAV flight.
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