Showing papers in "Journal of Global Positioning Systems in 2016"
TL;DR: In this article, the performance of different beamforming techniques to mitigate multipath signals through the processing and analysis of simulated and actual data is investigated, and the main novelty is the investigation of multipath mitigation performance of practically realizable antenna array based GNSS receivers when the beamforming process is completely integrated into the tracking module after de-spreading.
Abstract: Multipath affects the shape of the correlation function and results in biased pseudorange measurements and erroneous navigation solutions. Antenna array processing, which uses signal spatial characteristics, is an effective method to mitigate various types of interference signals. However, the performance of most of the distortionless beamforming techniques degrades in multipath conditions due to the correlation between the desired Line of Sight (LOS) signal and multipath signals. This paper characterizes the performance of different beamforming techniques to mitigate multipath signals through the processing and analysis of simulated and actual data. The main novelty is the investigation of multipath mitigation performance of practically realizable antenna array-based GNSS receivers when the beamforming process is completely integrated into the tracking module after de-spreading. Beamforming techniques such as Delay And Sum (DAS) beamforming, Minimum Power Distortionless Response (MPDR) with and without spatial smoothing are considered. A novel multi-antenna simulator test-bed is developed to generate multipath signals for a multi-antenna platform. A software multi-antenna GPS receiver incorporating different beamforming techniques is then developed to generate pseudorange measurements and position solutions. Carrier-to-Noise ratio (C/N0), pseudorange errors and position solutions before and after beamforming are compared to show the effectiveness of different beamforming techniques to mitigate multipath. Results with simulated and actual GPS signals show improved performance using the MPDR beamformer with spatial smoothing. The utilization of spatial processing results in a pseudorange error reduction of up to 60 % and a position error reduction of up to 30 %.
24 citations
TL;DR: A generic method for the stochastic model tuning about the random errors in IMU measurements together with other sensors is proposed and the success of the VCE algorithm is shown through a real dataset involving GPS and inertial sensors.
Abstract: Improving a priori stochastic models of the process and measurement noise vectors in Kalman Filer (KF) has always been a challenge. As one preferable technique to address this challenge, the variance component estimation (VCE) applied on the Kalman Filter’s process and measurement noise covariance matrix (Q & R) has been proved in plenty of applications. Unsurprisingly, VCE was expected to re-establish the stochastic model about the random errors in the IMU’s measurements in a multisensor integrated positioning and navigation system applying Kalman Filter. However, in the conventional error states-based GPS aided inertial navigation system (GPS/INS), the stochastic model tuning is difficult for the IMU’s measurements due to the amalgamation of the observables from inertial sensor and other aiding sensors. This paper proposes a generic method for the stochastic model tuning about the random errors in IMU measurements together with other sensors. The core of this novel approach is based on an innovative multisensor integration strategy which deploys upon the vehicle’s generic kinematic model and takes the IMU’s output as raw measurements in Kalman Filter (IMU/GNSS Kalman Filter). As a result, the statistical orthogonality between random error vectors of any two sensors enables the separate but parallel statistics collection of each individual random error source. Given these independent statistics corresponding to each error source, the VCE technique iteratively tunes all stochastic model of the process and measurement noise vectors. The success of the VCE algorithm is shown through a real dataset involving GPS and inertial sensors.
14 citations
TL;DR: This paper proposed a positioning algorithm by combining vehicle Global Navigation Satellite System (GNSS) and mobile RFID readers that can be used in the outdoor positioning with a rapid and accurate locating of the target objects, which is very helpful for the specific objects positioning and change detection in daily urban management and regulations.
Abstract: Radio frequency identification (RFID) technology has become one of the most prosperous positioning technologies due to its advantages in non-contact and non-line-of-sight sensing. Most of the current positioning applications using RFID were implemented by setting a few RFID readers in some known locations, which were only suitable for the indoor or small area of outdoor positioning. This paper proposed a positioning algorithm by combining vehicle Global Navigation Satellite System (GNSS) and mobile RFID readers. The positioning accuracy is better than ±5 m with an identification distance of 160 m. This method can be used in the outdoor positioning with a rapid and accurate locating of the target objects, which is very helpful for the specific objects positioning and change detection in daily urban management and regulations.
4 citations
TL;DR: The results show that the proposed SRE method requires less computation resources and is able to achieve the same or better accuracy level than the traditional COL.
Abstract: This paper presents a novel two-step camera calibration method in a GPS/INS/Stereo Camera multi-sensor kinematic positioning and navigation system. A camera auto-calibration is first performed to obtain for lens distortion parameters, up-to-scale baseline length and the relative orientation between the stereo cameras. Then, the system calibration is introduced to recover the camera lever-arms, and the bore-sight angles with respect to the IMU, and the absolute scale of the camera using the GPS/INS solution. The auto-calibration algorithm employs the three-view scale-restraint equations (SRE). In comparison with the collinearity equations (COL), it is free from landmark parameters and ground control points (GCPs). Therefore, the proposed method is computationally more efficient. The results and the comparison between the SRE and COL methods are presented using the simulated and road test data. The results show that the proposed SRE method requires less computation resources and is able to achieve the same or better accuracy level than the traditional COL.
4 citations
TL;DR: Geometric analysis shows that three images have the ability to provide enough global redundancy for reality based 3D mapping and mapping simulation in the indoor environment shows that the number of images is the key factor that influences Minimum Detectable Bias (MDB) and Minimum Separable B bias (MSB).
Abstract: Vision based mapping has become an important way to provide geospatial information for vision based navigation especially when satellite signals are not available. When acting as an independent source for navigation, its quality will affect that of navigation directly. However, geometry is one key component that affects the quality of vision-based mapping including reliability, separability and accuracy. Analysing the geometry provides a reference for users to design and judge the mapping strategy to meet the requirement in quality. This paper aims to explore the geometry’s influence on accuracy, reliability and separability in reality based indoor 3D mapping. Firstly, an analytical analysis based on the global redundancy number is conducted. Secondly, the geometric strength between the camera and ground control points (GCPs) quantified by Dilution of Precision (DoP) is analysed under different indoor mapping scenarios. Thirdly, the relationship between two geometric components including overlapping percentage and intersection angle and quality including reliability and separability is analysed based on a simulation environment. Geometric analysis shows that three images have the ability to provide enough global redundancy for reality based 3D mapping. GCPs with a good coverage of the image and a shorter distance between the camera and the object will contribute to good geometry. Besides, mapping simulation in the indoor environment based on two selected functional models shows that the number of images is the key factor that influences Minimum Detectable Bias (MDB) and Minimum Separable Bias (MSB).
2 citations