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With increasing demand of localization service in challenging environments where Global Navigation Satellite Systems (GNSS) signals are considerably weak, a powerful approach, the collective detection (CD), has been developed. However, traditional CD techniques are computationally intense due to the large clock bias search space. Therefore, the purpose of this paper is to develop a new scheme of CD with less computational burden, in order to accelerate the detection and location process.

This paper proposes a new scheme of CD. It reformulates the problem of GNSS signal detection as an optimization problem, and solves it with the aid of an improved Pigeon-Inspired Optimization (PIO). With the improved PIO algorithm adopted, the positioning algorithm arrives to evaluate only a part of the points in the search space, avoiding the problems of grid-search method which is universally adopted.

Faced with the complex optimization problem, the improved PIO algorithm proves to have good performance. In the acquisition of simulated and real signals, the proposed scheme of CD with the improved PIO algorithm also have better efficiency, precision and stability than traditional CD algorithm. Besides, the improved PIO algorithm also proves to be a better candidate to be integrated into the proposed scheme than particle swarm optimization, differential evolution and PIO.

The novelty associated with this paper is the proposition of the new scheme of CD and the improvement of PIO algorithm. Thus, this paper introduces another possibility to ameliorate the traditional CD.

The main aim of this study is to elaborately examine the error correction technology for global navigation satellite system (GNSS) navigation messages and to draw a conceptual decision support framework related to the modernization of the GNSS and other systems.

The extensive simulation model developed in Matrix Laboratory (MATLAB) is used to evaluate the performance of forward error correction (FEC) codes such as Hamming, Bose–Chaudhuri–Hocquenghem, convolutional, turbo, low-density parity check (LDPC) and polar codes under different levels of noise.

The performance and robustness of the aforementioned algorithms are compared based on the bit length, complexity and execution time of the GNSS navigation message. In terms of bit error rate, LDPC coding exhibits more ability in the robustness of the navigation message, while polar code gives better results according to the execution time.

In view of future new GNSS signals and message design, the findings of this paper may provide significant insight into navigation message modernization and design as an important part of GNSS modernization.

To the best of the authors’ knowledge, this is the first study that conducts a direct comparison of various FEC algorithms on GNSS navigation message performance against noise, taking into consideration turbo and newly developed polar codes.

In this study, an indoor sensor information fusion positioning system of the quadrotor unmanned aerial vehicle (UAV) was investigated to solve the problem of unstable indoor flight positioning.

The presented system was built on Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) and LiDAR-Lite devices. Based on this, one can obtain the aircraft's current attitude and the position vector relative to the target and control the attitudes and positions of the UAV to reach the specified target positions. While building a UAV positioning model relative to the target for indoor positioning scenarios under limited Global Navigation Satellite Systems (GNSS), the system detects the environment through the NVIDIA Jetson TX2 (Transmit Data) peripheral sensor, obtains the current attitude and the position vector of the UAV, packs the data in the format and delivers it to the flight controller. Then the flight controller controls the UAV by calculating the posture to reach the specified target position.

The authors used two systems in the experiment. The first is the proposed UAV, and the other is the Vicon system, our reference system for comparison purposes. Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

The current technique used to measure construction is the conventional total station method. However, the conventional method is time-consuming and could not be used to create a photo-realistic three-dimensional (3D) model of an object. Furthermore, the Canseleri building is located at a slope. The paper aims to discuss this issue.

The aim of this study is to assess the geometric accuracy of a 3D model using unmanned aerial vehicle (UAV) images. There are two objectives in this study. The first is to construct a 3D model of the Canseleri building using UAV images. The second objective is to validate the 3D model of the Canseleri building based on actual measurements.

The close-range photogrammetry method, using the UAV platform, was able to produce a 3D building model. The results show that the errors between the actual measurement and the generated 3D model were less than 4 cm. The accuracy of the 3D model achieved in this study was about 0.015 m, compared to total station measurements.

Accuracy assessment was done by comparing the estimated measurement of the 3D model with the direct measurement. The differences between the measured values with actual values could be compared. Based on this study, the 3D building model gave a reliable accuracy for specific applications.

Presents an overview of how satellite‐based positioning techniques could be used to develop novel navigational methods for use on mobile robotic platforms. Details are given of the major terrestrial techniques, both internal and external to the robot, which have been traditionally used to meet positioning requirements. A descriptive summary of the global positioning system of navigation satellites (GPS) is followed by an introduction to Galileo, the European project on the development of a comparable system. A small number of examples, either near to market or in use now, are used to illustrate the use of robotic systems that use GPS as a source of 3D absolute position information, but also velocity, attitude and time. Concludes that GPS is likely to become the universal positioning standard for outdoor applications, with future augmentations and developments enhancing the reliability, integrity and accuracy of the system. Nevertheless, in most cases it will still be necessary to use GPS in combination with alternative positioning sensors.

Precise vehicle localization is a basic and critical technique for various intelligent transportation system (ITS) applications. It also needs to adapt to the complex road environments in real-time. The global positioning system and the strap-down inertial navigation system are two common techniques in the field of vehicle localization. However, the localization accuracy, reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding, vision enhancement and automatic parking. Aiming at the problems above, this paper aims to propose a precise vehicle ego-localization method based on image matching.

This study included three steps, Step 1, extraction of feature points. After getting the image, the local features in the pavement images were extracted using an improved speeded up robust features algorithm. Step 2, eliminate mismatch points. Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust. Step 3, matching of feature points and trajectory generation.

Through the matching and validation of the extracted local feature points, the relative translation and rotation offsets between two consecutive pavement images were calculated, eventually, the trajectory of the vehicle was generated.

The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.

The purpose of this paper is to present a novel scheme of high‐dynamic global positioning system (GPS) software receiver in order to improve the capturing speed and trading accuracy of GPS receiver.

First, the beginning of C/A code can be found through the delay and multiply approach. To solve the problems of estimating a certain satellite's Doppler shift from the signals of several visible satellites, the “delay and accumulation unit” is put forward, and besides, performance of inertial navigation system‐assisted tracking loop in high‐dynamic circumstance is analysed by means of mathematical modelling and simulation experiments, whose results verified the validity of the proposed tracking scheme.

In this paper, the two‐dimension searching process in conventional acquisition scheme is transformed into two one‐dimension searching processes, thus improving the capturing speed.

This software receiver has only been verified by means of mathematical simulation, and the validity in hardware receiver is still obscured.

This paper presents a novel high‐dynamic GPS software receiver scheme, which can be seen as a reference of engineering application and simulation research.