Navigation Solution Research Articles

Phased Array Radio Navigation System on UAVs: In-Flight Calibration

Global Navigation Satellite Systems (GNSS) has been the primary positioning solution for Unmanned Aerial Vehicles (UAVs) due to their worldwide coverage, high precision and lightweight receivers. However, GNSS is prone to electromagnetic interference and malicious assaults, including jamming or spoofing because of its low signal-to-noise ratio (SNR). To ensure the continuity and protection of UAV operations, using redundant navigation systems is essential. In recent years, the phased array radio system (PARS) has established itself as a local navigation solution. PARS is robust towards malicious assaults because of an much higher SNR than GNSS regarding directed and encrypted transmission. An essential factor of PARS is that the orientation of the radio antenna at a ground station needs to be precisely determined to obtain the correct positioning of UAVs. This paper presents a method for extending a previously proposed calibration algorithm to estimate the ground antenna orientation with an inertial navigation system (INS) aided by redundant positioning sensors (GNSS, PARS or barometer) using a multiplicative extended Kalman filter (MEKF) so that the calibration can be activated during flights whenever GNSS is available. In other words, the proposed navigation system is essentially an aided-INS which switches between two modes depending on the availability of GNSS: calibration and GNSS aiding mode when GNSS is available (Mode 1) and PARS and barometer aiding mode when GNSS is unavailable (Mode 2). Considering that the navigation system needs to include the effect of Earth’s curvature for a long-distance flight, PARS horizontal measurement and the barometer measurement were treated independently, and the navigation equations were propagated in Earth Centred Earth Fixed (ECEF) frame. The independent treatment of barometer measurement, and the propagation in ECEF frame were also beneficial when using multiple ground antennas to have a common reference point and reference frame. The proposed method was validated using data (Inertial Measurement Unit (IMU), GNSS, PARS, Pixhawk autopilot (including barometer) measurements) collected during a field test. In the validation, GNSS was made available at the middle of the flight and the calibration mode was activated for 200s. The proposed navigation system successfully estimated the precise orientation of multiple ground antennas and the navigation solutions were verified using GNSS and Pixhawk autopilot solutions as ground truth.

Path Planning Technique for Mobile Robots: A Review

Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.

Pedestrian Inertial Positioning Method Based on Foot Quasi-Zero Velocity Observation Under Multiple Motion Modes

As the traditional zero velocity update (ZUPT) algorithm based on fixed threshold is difficult to solve the problem of effective positioning for pedestrians under multiple motion modes, a simpler, flexible and stable inertial positioning algorithm is proposed in this paper. A foot kinematic model based on rotating “L" shaped rigid body is proposed to describe the process from heel landing to heel off the ground. The quasi-zero velocity interval (QZVI) of foot motion is defined, and a method to determine the QZVI based on peak detection and gait cycle constraint is proposed to avoid the dependence of system on predetermined threshold. The proposed foot kinematic model is used to calculate the “near zero" velocity in the QZVI. Then difference between the calculated “near zero" velocity and the inertial navigation solution velocity is used as the observation of Kalman filter to estimate the position error, velocity error and attitude error of inertial navigation system (INS). Experiments show that the proposed algorithm has positioning error relative to the mileage of less than 2% under the mileage of 500m or less, effectively adapting to different motion modes such as fast walking, slow walking and running, so as to realize the accurate autonomous positioning under the condition of multiple motion modes.

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT.

Real-time navigation solutions of low-cost off-the-shelf GNSS receivers on board the Astrocast constellation satellites

With the ever-increasing number of small satellites launched into space, on-board orbit determination is becoming more and more important. Precise satellite positions are needed for constellation maintenance, formation flying, communication optimization, collision avoidance and Earth observation. In addition, accurate time information is required for the functioning of the instruments on board. Low-cost off-the-shelf Global Navigation Satellite System (GNSS) receivers and antennas, adapted to the small size and weight of the satellite, allow precise on-board orbit determination and time synchronization for even the smallest satellites.We analysed more than one year of on-board navigation solutions (NAVSOL) from 8 Astrocast CubeSats, which are equipped with our multi-GNSS single-frequency payload. Based on the residuals from a dynamic orbit fitting, we found that the quality of the NAVSOL from the Astrocast satellites varies strongly both over time and between satellites, with an RMS ranging from a few meters to several tens of meters.In this study, we focus on three main effects that influence the quality of the NAVSOL from the Astrocast satellites: (1) The orbit-fit residuals show a positive radial offset in the range of about 6 m to 15 m, which is caused by ionospheric refraction. Our data show that GNSS signals at negative elevations are very strongly affected by the ionospheric refraction and thus account for the major part of the radial offset. (2) Discrepancies between the NAVSOL positions and velocities, and simulations with a GNSS signal generator show a once-per-revolution periodicity in the out-of-plane component, with amplitudes of 2 m and 5 cm/s in position and velocity, respectively. This effect is probably due to an inappropriate dynamic model used in the real-time navigation algorithm of the GNSS receiver. (3) Depending on the location of the GNSS antenna on board the satellite, the noise level varies by a multiple. This is assumed to be due to the antenna orientations, interference of the GNSS signals with electromagnetic waves coming from the satellite platform, signal attenuation during the transmission from the antenna to the receiver and differences in the soldering or tuning of the individual GNSS antennas.These findings help us refine hardware configurations and firmware settings in preparation for future Astrocast satellites or other nanosatellite missions. By eliminating or minimizing the mentioned effects, we expect to consistently achieve an accuracy on the level of a few meters for the real-time navigation solution of commercial off-the-shelf single-frequency GNSS receivers in space.

Terrain-aware semantic mapping for cooperative subterranean exploration.

Navigation over torturous terrain such as those in natural subterranean environments presents a significant challenge to field robots. The diversity of hazards, from large boulders to muddy or even partially submerged Earth, eludes complete definition. The challenge is amplified if the presence and nature of these hazards must be shared among multiple agents that are operating in the same space. Furthermore, highly efficient mapping and robust navigation solutions are absolutely critical to operations such as semi-autonomous search and rescue. We propose an efficient and modular framework for semantic grid mapping of subterranean environments. Our approach encodes occupancy and traversability information, as well as the presence of stairways, into a grid map that is distributed amongst a robot fleet despite bandwidth constraints. We demonstrate that the mapping method enables safe and enduring exploration of subterranean environments. The performance of the system is showcased in high-fidelity simulations, physical experiments, and Team MARBLE's entry in the DARPA Subterranean Challenge which received third place.

Construction of features database using different platform navigation solutions for aiding PDR algorithm

Nowadays, navigation can be accomplished based on several technologies. Some of these technologies rely solely on camera vision-based algorithms, whereas others rely solely on motion sensors’ data. Furthermore, some solutions combine vision-based algorithms with data from motion sensors. In this study, three platform navigation algorithms (stereo visual odometry technique, combining the distance calculated from the visual optical flow with estimated heading from gyroscope sensor measurements, and combining the distance calculated from the visual optical flow with estimated heading from magnetometer sensor measurements) were compared to select the most accurate among them to be employed for features database construction. This features database, which contains various well-described features with known three-dimensional world coordinates, can be utilized to help any indoor pedestrian dead reckoning (PDR) navigation solution as well as mapping purposes. Tests' results demonstrated that vision-based technology (stereo visual odometry technique) is more accurate than the others however it should be aided with additional constraints to improve navigation positional accuracy. Afterward, this chosen technology was used to construct a well-described features database which was checked to assess its accuracy.

Efficient Transmitter Selection Strategies for Improved Information Gathering of Aerial Vehicle Navigation in GNSS-Denied Environments

Aerial vehicle navigation in global navigation satellite system (GNSS)-denied environments by utilizing pseudorange measurements from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> terrestrial signals of opportunity (SOPs) is considered. To this end, the aerial vehicle is tasked with choosing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> most informative terrestrial SOPs. Two computationally efficient, but sub-optimal, transmitter selection strategies are proposed. These selection strategies, termed opportunistic greedy selection (OGS) and one-shot selection (OSS), exploit the additive, iterative properties of the Fisher information matrix (FIM), where OGS selects the most informative transmitters in finite iterations, while the OSS selects in one-iteration. Monte Carlo simulation results are presented comparing the OGS and OSS strategies versus the optimal (exhaustive search) selection strategy, where it is concluded that OGS performs closely to the optimal selection, while executing in a fraction of the optimal selection's time. Experimental results are presented of a U.S. Air Force high-altitude aircraft navigating without GNSS signals in (i) a rural region and (ii) a semi-urban region. The performance of the aircraft's navigation solution with the selected SOP transmitters via optimal, OGS, OSS are compared over a flight segment where the selection remained valid. The position root mean-squared error (RMSE) with the optimal, OGS, and OSS were 4.53 m, 6.28 m, 7.13 m in the rural region; and 5.83 m, 6.08 m, 6.70 m in the semi-urban region for an aircraft traversing a trajectory of 1.48 km and 1.22 km, respectively.

Analysis of the precision of determination of aircraft coordinates using EGNOS+SDCM solution

This paper presents an algorithm for determining the precision parameter for aircraft position coordinates based on a combined GPS/EGNOS and GPS/SDCM solution. The proposed algorithm uses a weighted average model that com-bines a single GPS/EGNOS and GPS/SDCM position navigation solution to determine the resulting aircraft coordi-nates. The weighted mean model include the linear coefficients as a function of: the inverse of the number of tracked GPS satellites for which EGNOS and SDCM corrections have been generated, and the inverse of the geometric coeffi-cient of the PDOP (Position Dilution of Precision). The corrections between the single GPS/EGNOS and GPS/SDCM solution to the aircraft's resultant coordinates are then calculated on this basis. Finally, the standard deviation for the aircraft resultant BLh (B-Latitude, L-Longitude, h- ellipsoidal height) coordinates is calculated as a measure of preci-sion. The research experiment used recorded on-board GPS+SBAS data from two GNSS receivers mounted on a Dia-mond DA 20-C1 aircraft. The test flight was carried out on the Olsztyn-Suwaki-Olsztyn route. The calculations of aircraft position based on GPS/EGNOS and GPS/SDCM solution were performed in the RTKLIB v.2.4.3 program in the RTKPOST module. Next, aircraft resultant coordinates and standard deviations were computed in Scilab v.6.0.0 soft-ware package. Based on the tests performed, it was found that for the Trimble Alloy receiver, the standard deviation values for the ellipsoidal coordinates BLh of the aircraft do not exceed 1.77 m. However, for the Septentrio AsterRx2i receiver, the values of standard deviations for the aircraft's ellipsoidal BLh coordinates do not exceed 5.04 m. The use of linear coefficients as the inverse of the number of tracked GPS satellites with SBAS corrections in the GPS/EGNOS+GPS/SDCM positioning model resulted in a reduction in standard deviations of approximately 50-51% relative to the solution with linear coefficients calculated as the inverse of the PDOP parameter. In paper, the standard deviation was also obtained using arithmetic mean model. However the values of standard deviation from weighted mean model are lower than arithmetic mean model.

GNSS performance enhancement using measurement estimation in harsh environment.

Global navigation satellite systems (GNSSs) are commonly used to measure the position and time globally. A GNSS is convenient owing to its ability to measure accurate position relatively without using assistive tools for navigation by comparing with other sensors. Based on these benefits, the applicable area is expanding to commercial and social uses (e.g., vehicle navigation, smart grids, and smartphone apps). In the future, various services and technologies (e.g., the use of autonomous vehicles, unmanned delivery, and industrial field robots), which make Internet of Things (IOT) more active, will be used in our society. Conversely, the performance of GNSS can degrade in harsh environments, such as urban areas, owing to the property of GNSS, which calculates position and time via satellite signal reception. However, buildings in a city can block navigation satellite signals and generate multi-path errors. The blocked signals exacerbate the dilution of precision (DOP), which indicates the accuracy of the navigation solution and increases the navigation solution error. This study proposes methods to improve navigation performance by leveraging various techniques (e.g., range differences, receiver clock error hold, and virtual satellites). The methods were validated in harsh environments where visible satellites were reduced. In the simulation, each proposed method improved the navigation performance by creating an environment similar to a normal situation, despite the receiver entering a harsh environment. The results confirmed that the navigation performance deteriorated compared to the normal situation where the number of visible satellites decreased. However, the navigation performance was recovered gradually by applying the proposed techniques. Using the proposed methods, navigation performance can be maintained continuously even in situations where satellite signals are blocked.

Autonomous Navigation and Crop Row Detection in Vineyards Using Machine Vision with 2D Camera

In order to improve agriculture productivity, autonomous navigation algorithms are being developed so that robots can navigate along agricultural environments to automatize tasks that are currently performed by hand. This work uses machine vision techniques such as the Otsu’s method, blob detection, and pixel counting to detect the center of the row. Additionally, a commutable control is implemented to autonomously navigate a vineyard. Experimental trials were conducted in an actual vineyard to validate the algorithm. In these trials show that the algorithm can successfully guide the robot through the row without any collisions. This algorithm offers a computationally efficient solution for vineyard row navigation, employing a 2D camera and the Otsu’s thresholding technique to ensure collision-free operation.

Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement outliers. It has been proven that the sigma points-based Kalman filter (KF) performs better than an extended KF in cases where large prior uncertainty is present in the state estimation of a GNSS/INS. By modifying the sigma points directly, the resampling-free sigma point update framework (SUF) propagates more information excepting Gaussian moments of prescribed accuracy, based on which the resampling-free cubature Kalman filter (RCKF) was developed in our previous work. In order to enhance the adaptivity and robustness of the RCKF, the resampling-free SUF depending on dynamic prediction residue should be improved by suppressing the time-varying measurement outlier. In this paper, a robust observability-constrained RCKF (ROCRCKF) is proposed based on adaptive measurement noise covariance estimation and outlier detection, where the occurrence of measurement outliers is modelled by the Bernoulli variable and estimated with the state simultaneously. Experiments based on car-mounted GNSS/INSs are performed to verify the effectiveness of the ROCRCKF, and result indicates that the proposed algorithm outperforms the RCKF in the presence of measurement outliers, where the heading error and average root mean square error of the position are reduced from 1.96° and 6.38 m to 0.27° and 5.95 m, respectively. The ROCRCKF is robust against the measurement outliers and time-varying model uncertainty, making it suitable for the real-time implementation of GNSS/INSs in GNSS-challenged environments.

Factor Graph with Local Constraints: A Magnetic Field/Pedestrian Dead Reckoning Integrated Navigation Method Based on a Constrained Factor Graph

The method of multi-sensor integrated navigation improves navigation accuracy by fusing various sensor data. However, when a sensor is disturbed or malfunctions, incorrect measurement information will seriously affect the estimation of the trajectory, which will lead to a decrease in accuracy. Existing factor graph models based on weights can neither fully resist the influence of disturbances nor guarantee the local rationality of estimated trajectories. In this paper, a factor graph with local constraints model that fuses the magnetic field and pedestrian dead reckoning data is proposed to navigate complex curved trajectories. First, adding local constraints to the pedestrian dead reckoning measurement converts the navigation solution problem into a hard-constrained nonlinear least squares problem. Then, a mapping model is constructed to reconstruct the variable space and the Adam gradient algorithm is used to realize a fast calculation. The navigation accuracy of this algorithm is better than that of the state-of-the-art method in real-world experiments, with an average accuracy of 0.83 m.

A Visual Odometry Pipeline for Real-Time UAS Geopositioning

The state-of-the-art geopositioning is the Global Navigation Satellite System (GNSS), which operates based on the satellite constellation providing positioning, navigation, and timing services. While the Global Positioning System (GPS) is widely used to position an Unmanned Aerial System (UAS), it is not always available and can be jammed, introducing operational liabilities. When the GPS signal is degraded or denied, the UAS navigation solution cannot rely on incorrect positions GPS provides, resulting in potential loss of control. This paper presents a real-time pipeline for geopositioning functionality using a down-facing monocular camera. The proposed approach is deployable using only a few initialization parameters, the most important of which is the map of the area covered by the UAS flight plan. Our pipeline consists of an offline geospatial quad-tree generation for fast information retrieval, a choice from a selection of landmark detection and matching schemes, and an attitude control mechanism that improves reference to acquired image matching. To evaluate our method, we collected several image sequences using various flight patterns with seasonal changes. The experiments demonstrate high accuracy and robustness to seasonal changes.

Smartphone MEMS Accelerometer and Gyroscope Measurement Errors: Laboratory Testing and Analysis of the Effects on Positioning Performance.

Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other hand, smartphones are equipped with low-performance Micro Electro Mechanical Systems (MEMS) sensors to enhance their navigation performance in Global Navigation Satellite System (GNSS)-degraded or -denied environments. Compared with higher-grade inertial navigation systems (INS), MEMS-based INSs have a poor navigation performance due to large measurement errors. In this paper, we present laboratory test results on the stochastic and deterministic errors observed in MEMS inertial sensor measurements of five different smartphones from different manufacturers. Then, we describe and discuss the short-term effects of these errors on the pure inertial navigation performance and also on the navigation performance based on the tight coupling of INS with GNSS measurements using a smartphone.

INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments.

Traditionally, navigation systems have relied solely on global navigation satellite system (GNSS)/inertial navigation system (INS) integration. When a temporal loss of GNSS signal lock is encountered, these systems would rely on INS, which can sustain short bursts of outages, albeit drift significantly in prolonged outages. In this study, an extended Kalman filter (EKF) is proposed to develop an integrated INS/LiDAR/Stereo simultaneous localization and mapping (SLAM) navigation system. The first update stage of the filter integrates the INS with the LiDAR, after which the resultant navigation solution is integrated with the stereo SLAM solution, which yields the final integrated navigation solution. The system was tested for different driving scenarios in urban and rural environments using the raw Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset in the complete absence of the GNSS signal. In addition, the selected KITTI drives covered low and high driving speeds in feature-rich and feature-poor environments. It is shown that the proposed INS/LiDAR/Stereo SLAM navigation system yielded better position estimations in comparison to using the INS without any assistance from onboard sensors. The accuracy improvement was expressed as a reduction of the root-mean-square error (RMSE) by 83% and 82% in the horizontal and up directions, respectively. In addition, the proposed system outperformed the positioning accuracy of some of the state-of-the-art algorithms.

The Terprom® Digital Terrain System (DTS)

Developed specifically for tactical military aircraft operations, TERPROM® is the world leading Digital Terrain System, offering a range of capabilities that can provide both safety and tactical benefits to the aircraft. The system is an established and well-proven product that has been in production since 1991. To date it has been selected by fourteen Nations worldwide for use on numerous platforms. The number of military aircraft equipped with TERPROM® is now over 5000 and growing. TERPROM® provides the following functions: Terrain Referenced Navigation (TRN), Predictive Ground Collision Avoidance System (PGCAS), Advanced Terrain Avoidance Cueing (ATAC), Obstruction and Wires Warning and Cueing (OWWC), Database Terrain Following (DBTF), Air to Ground Ranging Functions, Terrain Awareness Display (TAD). At the core of the system is the Terrain Referenced Navigation (TRN) capability which navigates by combining aircraft navigation data (e.g. inertial and, or GPS) and radar altimeter measurements of height above ground level (AGI), with terrain heights alms from the stored map data, to produce a navigation solution referenced to the terrain map data. This navigation solution then allows the other TERPROM® functions to use the map data to provide their safety and tactical capabilities. Three variants of TERPROM® have been developed to satisfy the airborne platform market: - Fast Jet, Military Transport, Helicopter. TERPROM® is a living product that has and continues to receive significant internal investment to increase its capability, functionality and performance to support future opportunities. Current areas of development include: Use of higher resolution map data which will increase the performance of TERPROM® and offer enhanced capability to platforms flying nap of the earth, enhanced capability in urban areas and could also reduce any platform reliance on GPS. Integration with active forward looking sensors to provide additional information to the Navigation, Situational Awareness and Ranging functions and also to provide new capabilities such as real time mapping of unmapped terrain features and obstructions.

Deep learning‐based crop row detection for infield navigation of agri‐robots

AbstractAutonomous navigation in agricultural environments is challenged by varying field conditions that arise in arable fields. State‐of‐the‐art solutions for autonomous navigation in such environments require expensive hardware, such as Real‐Time Kinematic Global Navigation Satellite System. This paper presents a robust crop row detection algorithm that withstands such field variations using inexpensive cameras. Existing data sets for crop row detection do not represent all the possible field variations. A data set of sugar beet images was created representing 11 field variations comprised of multiple grow stages, light levels, varying weed densities, curved crop rows, and discontinuous crop rows. The proposed pipeline segments the crop rows using a deep learning‐based method and employs the predicted segmentation mask for extraction of the central crop using a novel central crop row selection algorithm. The novel crop row detection algorithm was tested for crop row detection performance and the capability of visual servoing along a crop row. The visual servoing‐based navigation was tested on a realistic simulation scenario with the real ground and plant textures. Our algorithm demonstrated robust vision‐based crop row detection in challenging field conditions outperforming the baseline.

Multi-level autonomous integrity monitoring method for multi-source PNT resilient fusion navigation

For the integrity monitoring of a multi-source PNT (Positioning, Navigation, and Timing) resilient fusion navigation system, a theoretical framework of multi-level autonomous integrity monitoring is proposed. According to the mode of multi-source fusion navigation, the framework adopts the top-down logic structure and establishes the navigation source fault detection model based on the multi-combination separation residual method to detect and isolate the fault source at the system level and subsystem level. For isolated non-redundant navigation sources, the system level recovery verification model is used. For the isolated multi-redundant navigation sources, the sensor fault detection model optimized with the dimension-expanding matrix is used to detect and isolate the fault sensors, and the isolated fault sensors are verified in real-time. Finally, according to the fault detection and verification results at each level, the observed information in the fusion navigation solution is dynamically adjusted. On this basis, the integrity risk dynamic monitoring tree is established to calculate the Protection Level (PL) and evaluate the integrity of the multi-source integrated navigation system. The autonomous integrity monitoring method proposed in this paper is tested using a multi-source navigation system integrated with Inertial Navigation System (INS), Global Navigation Satellite System (GNSS), Long Baseline Location (LBL), and Ultra Short Baseline Location (USBL). The test results show that the proposed method can effectively isolate the fault source within 5 s, and can quickly detect multiple faulty sensors, ensuring that the positioning accuracy of the fusion navigation system is within 5 m, effectively improving the resilience and reliability of the multi-source fusion navigation system.

Application of Pulsar-Based Navigation for Deep-Space CubeSats

This paper investigates the use of pulsar-based navigation for deep-space CubeSats. A novel approach for dealing with the onboard computation of navigational solutions and timekeeping capabilities of a spacecraft in a deep-space cruise is shown, and the related implementation and numerical simulations are discussed. The pulsar’s signal detection, processing, and exploitation are simulated for navigation onboard a spacecraft, thus showing the feasibility of autonomous state estimation in deep space even for miniaturized satellites.