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In-flight alignment is the basis for projectile microstrap-down inertial navigation system (MSINS) to accurate navigation. Due to high dynamic and small space constraints of projectiles, MSINS with antihigh overload and low-precision is usually used, which makes it difficult to achieve initial alignment in flight. Hence, this article proposes a global navigation satellite system (GNSS) aided MSINS in-flight alignment method based on functional iteration. The Legendre polynomials integral recursive model is derived and functional iteration alignment scheme is constructed. The simulation results show that the alignment precision of the proposed method is improved by more than 18% compared with the traditional optimization-based alignment optimization-based alignment (OBA) method. The experiment results show that the alignment precision of the proposed method is improved by more than 10% compared with the traditional OBA method.

To the best of our knowledge, this paper represents the first attempt towards robust beamforming design for multi-user downlink communications in intelligent reflecting surface (IRS)-enhanced satellite (SAT) and high altitude platform (HAP) integrated network. Such network configuration is mainly composed of a single-antenna SAT, a multiple-antenna HAP, multiple single-antenna SAT and HAP terminals and an IRS. Although sharing the same spectrum resource with the SAT, the HAP suspended in the near space intends to offer temporary higher-speed and lower-delay multi-user communication connections than the SAT. Therefore, the power budget should be carefully tuned at the HAP. Towards this end, we first formally formulate the transmit power minimization problem at the HAP under the constraints of signal-to-interference-plus-noise-ratio-outage-probability (SINR-OP) at each SAT and HAP terminal by taking into account the imperfect channel state informations and their Gaussian channel estimation errors. Note that the complicated superposition of direct SAT/HAP links and cascaded IRS links, makes the optimization problem rather challenging. Specifically, we transform the probabilistic constraints into approximate deterministic ones, by performing rank relaxation. Based on this, we are able to solve the optimization problem by alternately optimizing the two ensuring subproblems. As verified by extensive simulation results, the proposed IRS-enhanced beamforming schemes can substantially diminish the transmit power at the HAP compared to the beamforming counterparts without IRS.

Advantages of Global Navigation Satellite System (GNSS) in precise and reliable localization can be exploited to enable low-cost and train-centric railway train control systems by reducing track-side facilities like Balises and track circuits. However, constrained observability of satellite signals in specific railway station areas leads to a great challenge to the continuity and availability of GNSS-based train positioning. The pseudolite (PL) technology has a great potential for seamless localization under GNSS-challenged or even signal-denied environments. In this paper, we consider the optimized solution of pseudolite constellation design for seamless train positioning in railway station environments. An integrated train positioning architecture is presented based on a combined GNSS/PL measurement model. Using the trackmap, the proposed solution firstly establishes a feature point set covering all tracks in the station by extracting key Points-of-interest (POIs) from track database. Based on that, a K-means-enhanced generalized center-guided firefly algorithm (KGFA) is proposed to improve the standard firefly algorithm (FA) for deriving an optimized pseudolite constellation solution. The performance indicator for each candidate pseudolite layout scheme is defined according to the scenario-based GNSS/PL constellation configuration. The capability of the KGFA-enabled solution is validated by comparisons with similar FA methods. Through a case study, performance of the optimized pseudolite constellation and its influence to GNSS/PL-based seamless train positioning have been demonstrated over the involved reference pseudolite layout strategies. It is noteworthy that the proposed solution enables the enhanced inherent capability of the local pseudolite network to achieve seamless train positioning over the conventional GNSS-alone train positioning mode.

Vehicle trajectory collection usually faces challenges such as inaccurate and incomplete trajectory data, mainly due to missing trajectories caused by Global Navigation Satellite System (GNSS) outages. In this paper, a novel ensemble transfer regression framework is proposed for urban environments with transfer learning as the primary solution for constructing a fine-grained trajectory dataset during GNSS outages. First, GNSS and motion information are fused for the training process. Then, a regression-to-classification (R2C) process is employed to implement incremental training to adapt to dynamically changing environments. Third, to account for GNSS outages, transfer learning is integrated to construct a data filtering strategy that minimizes negative sample weights during the current scenario. Finally, a more accurate classification-type loss function for ensemble learning is designed to obtain the ensemble transfer regression model. We utilize real-world datasets to verify the accuracy of the comparative methods and the proposed framework in trajectory interpolation prediction. The experimental results show that our framework is significantly superior to the comparative methods.

A receiver autonomous integrity monitoring (RAIM) framework for ground vehicle navigation using ambient cellular signals of opportunity (SOPs) and an inertial measurement unit (IMU) is developed. The proposed framework accounts for two types of errors that compromise the integrity of the navigation solution: (i) multipath and (ii) unmodeled biases in the cellular pseudorange measurements due to line-of-sight (LOS) signal blockage and high signal attenuation. This paper, first, characterizes the multipath in a cellular-based navigation framework. Next, a fault detection and exclusion technique for a cellular-based navigation framework is developed. Simulation and experimental results with real long-term evolution (LTE) signals are presented evaluating the efficacy of the proposed RAIM-based fault detection and exclusion technique on a ground vehicle navigating in a deep urban environment in the absence of global navigation satellite system (GNSS) signals. The experimental results on a ground vehicle traversing 825 m in an urban environment show that the proposed RAIM-based measurement exclusion technique reduces the position root mean-squared error (RMSE) by 66%.

Steering vector and covariance matrix estimation mismatch along with moving jammer/platform, can affect the performance of null-steering beamformer. In this paper, by utilizing null-widening, null-deepening and diagonal loading shrinkage in space–time adaptive processing (STAP) structure, two methods for nullifying GNSS (global navigation satellite system) jamming/interference in cruise moving platforms are introduced. The enhanced performance of the proposed methods is investigated through numerical simulations. The results indicate improvement in the number of acquired GNSS satellites, in heavy jamming (GNSS denied) scenario, by reconstructing and acquiring at least 87% of satellites.

Relative measurements are exploited to cooperatively detect and recover faults in the positioning of Mobile Agent (MA) Swarms (MASs). First, a network vertex fault detection method based on edge testing is proposed. For each edge, a property that has a functional relationship with the properties of its two vertices is measured and tested. Based on the edge testing results of the network, the maximum likelihood principle is used to identify the vertex fault sources. Second, an edge distance testing method based on the noncentral chi-square distribution is developed for detecting faults in the Global Navigation Satellite System (GNSS) positioning of MASs. Third, a recovery strategy for faults in the positioning of MASs based on distance measurement is provided. The effectiveness of the proposed methods is validated by a simulation case in which an MAS passes through a GNSS spoofing zone. The proposed methods are conducive to increasing the robustness of the positioning of MASs in complex environments. The main novelties include the following: (A) network vertex fault detection is based on concrete probability analysis rather than simple majority voting, and (B) the relation of detectability and recoverability of MAS positioning faults with the structure of the relative measurement network is first disclosed.

This correspondence discusses recognition of traffic modes and behavior patterns based on Global Navigation Satellite System (GNSS) data. The traffic modes (e.g., walk, car, train, etc.) are firstly inferred, and then their behavior patterns (e.g., left-turn, right-turn, turn-around, etc.) are further identified. Because both traffic modes and behavior patterns are strongly influenced by geographical circumstances, their recognitions are enhanced by geographical layer information (e.g., building, road, water, etc.). At one specific GNSS point, its surrounding area is uniformly sliced as grids, and the probabilities for grid centers belonging to six different geographical layers are calculated based on whether these centers lie inside or outside of the minimum rectangles containing polygons indicating different geographical objects. Finally, the six-dimensional probability matrix is processed and compressed as a geographical information vector by the convolutional neural network (CNN). The latter is then combined with kinematic metrics such as velocity, acceleration, and moving direction from GNSS data, and serially input into a long short-term memory (LSTM) network to predict traffic modes and behavior patterns. Experimental results validate that the geographical information does enhance the performances of two recognition tasks. The CNN+LSTM framework retains the powers of CNN and LSTM, and outperforms classical machine learning algorithms.

Global navigation satellite system (GNSS) plays a vital role in high-precision positioning whereas detecting and mitigating carrier phase multipath error is still a significant challenge. Multipath error is closely related to the users’ environment, so it is difficult to mitigate by traditional differencing techniques. In this paper, we discuss the possibility and potential performance of multipath detection and mitigation with low-cost antennas in real-time kinematic (RTK) positioning. We employ two low-cost antennas and assume that the baseline length is known. We employ the carrier phase, ephemeris, and baseline length to detect the observed and estimated baseline vectors’ consistency in real-time. A reasonable threshold is proposed to detect the satellite signals with the multipath effect, then we eliminate these signals and use the remaining satellite signals for RTK positioning to mitigate multipath. The experiments are carried out in the open environment without multipath and the environment with specular multipath. Experimental results show that the false alarm rate of this method is entirely consistent with the theoretical value. When the number of satellites is six and eight, this method can reduce the RMS (root mean square) of positioning error by approximately 64% and 49%, respectively. At the same time, this method can improve the ambiguity success rate to above 99%.

Location-based services have become more and more important with the development of the Internet of Things (IoT) technology in recent years. Global Navigation Satellite System (GNSS) is widely used for positioning outdoors while it is still challenging to realize accurate and universal 3D indoor localization in complex indoor environments. Crowdsourcing-based positioning method is proposed aiming at autonomously constructing the navigation database based on the pedestrians’ daily-life data. This paper proposes an autonomous 3D indoor localization algorithm using the combination of crowdsourced Wi-Fi fingerprinting and Micro-Electro-Mechanical System sensors (3D-CSWS). An enhanced complementary filter is applied to provide accurate attitude information by integrating multi-sensor data with the detection of external acceleration and quasi-static magnetic field. In addition, the gradient descent (GD) algorithm is proposed to optimize the forward pedestrian dead reckoning and the optimized trajectories are weighted fused to construct the final navigation database after quality evaluation. In the on-line phase, the adaptive particle filter is used to integrate the results of Wi-Fi fingerprinting and multiple sensors to provide accurate and concrete 3D indoor localization performance. The real-world experimental results demonstrate that the proposed 3D-CSWS is proved to achieve autonomous and precise 3D indoor localization performance among complex indoor environments.

In the application of integrity monitoring of Global Navigation Satellite System (GNSS)/Inertial Navigation System integrated navigation system in complex environments, such as vegetation covered areas, the Kalman filtering (KF) has the disadvantages of low positioning precision, poor performance of fault identification and error bounds. This study proposes an integrity monitoring method based on dynamic fading filter optimisation. According to the real‐time updates of filtering innovation, a fading filtering algorithm is designed, which can adjust the fading factor dynamically. The dynamic fading filtering algorithm is then combined with the fault identification algorithm, which integrates chi‐square and multiple solution separation. An optimisation strategy of setting a fading period to speed up the filter convergence is proposed. The simulation results show that compared with the KF, the dynamic fading filtering reduces the standard deviations of positioning errors by 26%, 39%, and 26%, respectively, in the east, north and up directions. By reducing the filter convergence period, the proposed dynamic fading filtering can enhance the accuracy of the identification results when the subsequent GNSS fault is minor. It can shorten the identification time to about 20% when the subsequent fault is large. The dynamic fading filtering can also limit the state errors below the bounds correctly.

In this paper, a novel technique for miniaturization of corrugated structures is presented and multi-folded corrugations (MFCs) are introduced. The miniaturization is achieved by implementing multiple folds or slits inside corrugations. The proposed miniaturization technique is modeled using the modal expansion method. An equivalent circuit is extracted which precisely models the surface impedance of the proposed MFCs. A time-efficient deign procedure is presented based on the equivalent circuit model and a dual-band double-folded substrate integrated choke ring (DFSICR) structure is designed to suppress the propagation of surface waves over the main global navigation satellite system (GNSS) frequency bands, L1 (1573-1587 MHz) and L2 (1215-1240 MHz). The DFSICR demonstrates significant multipath mitigation capabilities, close to that of a classic choke ring, while it eliminates the drawbacks of a conventional choke ring such as large size, heaviness, costly fabrication, and inability to integrate with printed circuit board (PCB) structures. An example design shows miniaturization of 85% in height and 38% in diameter compared to the conventional choke ring. A prototype is fabricated using FR4 laminates which demonstrates weight reduction of more than 90% compared to the classic choke ring. The measurement results confirm the validity of the proposed technique.

The tether deployment of a tethered satellite system involves the consideration of complex dynamic properties of the tether, such as large deformation, slack, and even rebound, and therefore, the dynamic modelling of the tether is necessary for performing a dynamic analysis of the system. For a variable-length tether element, the absolute nodal coordinate formulation (ANCF) in the framework of the arbitrary Lagrange-Euler (ALE) description was used to develop a precise dynamic model of a tethered satellite. The model considered the gravitational gradient force and Coriolis force in the orbital coordinate frame, and it was validated through numerical simulation. In the presence of dynamic constraints, a deployment velocity of the tether was obtained by an optimal procedure. In the simulation, rebound behavior of the tethered satellite system was observed when the ANCF-ALE model was employed. Notably, the rebound behavior cannot be predicted by the traditional dumbbell model. Furthermore, an improved optimal deployment velocity was developed. Simulation results indicated that the rebound phenomenon was eliminated, and smooth deployment as well as a stable state of the station-keeping process were achieved. Additionally, the swing amplitude in the station-keeping phase decreased when a deployment strategy based on the improved optimal deployment velocity was used.

For improved positioning in urban canyons, this study proposes an efficient dual‐filter method integrating multi‐constellation global navigation satellite system (GNSS), an inertial navigation system (INS), a barometric altimeter and an on‐board diagnostics (OBD) module. The proposed method consists of a position‐domain (PD) Hatch filter and velocity Kalman filter. The Hatch filter is operated as the main positioning filter and the Kalman filter is operated as a sub‐filter to aid the main filter for the occasional GNSS outages. The Hatch filter combines barometric altimeter measurements with GNSS pseudorange and carrier phase measurements. The Kalman filter integrates the inertial measurement unit (IMU) measurements with GNSS Doppler shift and OBD speed measurements. A semi‐simulation method is applied for the accurate performance evaluation of the proposed method compared with the several conventional methods under deficient satellite visibility, multipath errors and cycle slips caused by urban canyons. By the test results, it is demonstrated that the proposed method can bound the positioning errors effectively by utilising low‐cost all‐weather sensors. The RMSEs of the proposed Kalman–Hatch dual‐filter algorithm were shown as 0.05, 0.19 and 0.11 m, respectively, and the maximum errors were shown as 0.16, 0.26 and 0.40 m, respectively, in the east, north and upward directions.

Navigation of an unmanned aerial vehicle (UAV) to reach a desired waypoint in global navigation satellite system (GNSS)-denied environments is considered. The UAV is assumed to have an unknown initial state and the environment is assumed to possess multiple terrestrial signals of opportunity (SOPs) transmitters with unknown states and one anchor SOP whose states are known. The UAV makes pseudorange measurements to all SOPs to estimate its own states simultaneously with the states of the unknown SOPs. The problem is formulated as a multi-objective motion planning (MOMP) strategy, which guarantees the UAV gets to within a user-specified distance of the waypoint with a user-specified confidence. The MOMP strategy balances two objectives: (i) navigating to the waypoint and (ii) reducing UAVs position estimate uncertainty. It is demonstrated that in such an environment, formulating the waypoint navigation problem in a so-called naive fashion by heading directly to the waypoint results in failing to reach the waypoint due to poor estimability of the environment. In contrast, the MOMP strategy guarantees (in a probabilistic sense) reaching the waypoint. Simulation and experimental results are presented showing that the MOMP strategy achieves the desired objective while the naive strategy fails to do so.

Among the existing satellite types, Low Earth Orbit (LEO) satellites provide short round-trip delays and are becoming increasingly important. Due to its low orbital profile, the LEO satellites can provide high-speed, low-latency and no dead zone network services for ground users. However, as the number of satellites continues to increase, frequency bands as non-renewable resources will seriously restrict the future development of the Space-Earth integration network. In this paper, a flexible spectrum sharing and cooperative service method is proposed to address the co-linear interference issue caused by LEO satellites while passing through the coverage area of the GEO beam and allows the LEO satellites to provide services for multiple LEO ground users. In our proposed scheme, through continuous power allocation optimization, we ensure that the service of LEO satellites will not reduce the service quality of the GEO beam. At by taking full advantage of the cooperation between LEO satellites, the quality of their service can be significantly improved. Simulation results show that our proposed scheme converges quickly, the transmission efficiency and the stability of the system can all be guaranteed.

As an alternative to traditional meteorological methods, rain attenuation in satellite-to-Earth microwave communication signals has been used for rainfall reconstruction in recent years. In this article, the existing 2-D rain field reconstruction problem is extended to a 3-D scenario by leveraging the low Earth orbit satellite system. A compressive sensing approach is further proposed to solve the 3-D rain field reconstruction problem. The Starlink system is used as a reference, and two synthetic rain events near the Great Barrier Reef in Australia, which are generated from the weather research and forecasting model, are used to evaluate the reconstruction performance. Simulation results show that the compressive sensing approach performs better than both the traditional least squares and the least absolute shrinkage and selection operator approaches.

Global ionosphere model with high accuracy and resolution is of great importance for ionospheric research and global navigation satellite system (GNSS) precise positioning applications. In the last 20 years, the accuracy and reliability of global ionospheric model benefitted from the development of GNSS technology have been improved significantly, but its performance is still not good over many regions (e.g., oceans and polar region) due to the lack of ground GNSS stations. Fortunately, the rapid development of low earth orbit (LEO) satellite constellations provides a potential opportunity to address this issue. However, there is still no optimal approach to make use of the LEO-based ionospheric observations because of the different observation ranges compared with that of ground-based GNSS ionospheric observations. In this article, two approaches are proposed to combine GNSS and LEO observation data for ionosphere modeling, which are single-layer normalization (SLN) method and dual-layer superposition (DLS) method, respectively. The results exhibit a significant improvement of ionospheric model accuracy by combining LEO and GNSS observation data based on our proposed methods compared with that using GNSS data only, with a reduction in root mean square (rms) error of about 25% and 21% for SLN method and DLS method, respectively. The article also highlights the relations between the performance of ionospheric model estimated by the SLN method and LEO ionospheric observations with different observation accuracy and different satellite cut-off elevations. The results indicate that ionospheric model estimated by GNSS/LEO using SLN method improves at least 25% compared with that by GNSS only. The improvement of ionospheric model estimated with the cut-off elevation of 50° is the best, followed by 70°, and then 20°.