Reliability Of Navigation System Research Articles

Observability-Aware Differential Dynamic Programming with Impulsive Maneuvers

In autonomous space systems, the reliability of navigation systems is essential. Observability in autonomous orbit determination techniques depends on the spacecraft’s orbital motion, making the design of autonomous navigation systems and orbital maneuvers a coupled process. This study develops a stable and efficient algorithm based on differential dynamic programming to design maneuver sequences that improve navigation performance. Our approach incorporates the Fisher information matrix into a cost function to quantify state observability and facilitates its convergence using a semi-analytic gradient and Hessian derived under impulsive maneuvers. Two numerical examples show the validity and effectiveness of our algorithm. The results indicate the stability and efficiency in determining maneuver sequences and the improvement of state estimation accuracy along an optimized trajectory. It is also applicable to other observability-aware optimal control problems because the algorithm is independent of specific systems.

A deep learning model employing Bi-LSTM architecture to predict Martian ionosphere electron density using data from the Mars Global Surveyor mission

Planetary scientists and space agencies are highly intrigued by the Martian ionosphere due to its significant impact on upcoming Mars missions. Given its potential influence on satellite communication and navigation systems, measuring the altitude distribution of electron density (ED) values in the Martian ionosphere becomes a vital parameter to consider. The parameter of electron density in the Martian ionosphere serves as a tool to represent and predict the impacts of severe space weather events and solar activity on the Martian ionosphere. This study presents the development of a Bi-directional Long Short-Term Memory (Bi-LSTM) model, a deep machine learning algorithm to predict the Mars ionospheric ED values. To train and evaluate the model’s performance, we employ data samples from the Mars Global Surveyor (MGS) mission. For the preparation of input variables in the Bi-LSTM model, approximately 5600 ED altitude distribution of electron density profiles logged by the MGS between the years 1998 to 2005, which corresponded to the major period of the 23rd solar cycle, are taken into consideration. The performance of the proposed model in predicting both quiet and disturbed times is evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The significant minimum values of MAE (0.56 × 109) and RMSE (0.67 × 109) for the quiet day considered and MAE (0.27 × 109) and RMSE (0.31 × 109) for the solar flare disturbed day considered indicate the fair performance of the Bi-LSTM based prediction model. This research’s findings are vital for upcoming Mars missions, as precise predictions of ED of Mars ionosphere are crucial for reliable navigation and communication systems.

Methodology of Spatial Data Acquisition and Development of High-Definition Map for Autonomous Vehicles – Case Study from Wrocław, Poland

Autonomous drive systems are a dynamically developed sector of the automotive industry. The key problem in such technological solutions is to provide a reliable navigation system, which is typically based on high-definition (HD) maps supporting the identification of the position of a maneuvering vehicle. HD maps should include possibly up-to-date and detailed information on traffic lanes and on the traffic rules and regulations on such lanes. An effective development of an HD map should be based on the geodetic measurement methods, which ensure efficient and accurate acquisition of spatial data. This article presents the results of an experiment consisting in the manipulation of data obtained with the use of the mobile laser scanning method and further in employing this data in the development of an HD map in an open-source environment. The applied measurement technology and the processing method allowed data of high resolution (frequently above 1000 points per m2) and of high accuracy (3D accuracy down to less than 5 cm). The obtained data were processed in the Vector Map Builder environment (which is accessible from the level of an internet browser) and the final product - HD map was created in the Lanelet2 open-source environment. The above-described experiments allowed two main conclusions. Most importantly, they demonstrate the importance of planning and performing in-field mobile laser scanning measurements. They also point to the important role of the human analyst who needs to manually vectorize the key elements of road infrastructure and to define traffic rules.

INS aided spoofing detection of high dynamic GNSS receiver for launch vehicle applications: A loosely coupled approach

Launch vehicle applications require highly accurate and reliable navigation systems that can withstand the high dynamic conditions of the launch and flight phases. GNSS-aided INS (Inertial Navigation System) is the most popular solution for launch vehicle navigation. However, the accuracy of Global Navigation Satellite System (GNSS) receivers can be compromised by malicious spoofing attacks, which can result in significant safety risks and mission failure. This paper presents a method for identifying and mitigating the spoofing attacks on Global Navigation Satellite System (GNSS) receivers using the independent and simultaneous measurements of the Inertial Navigation System (INS). A state space approach is attempted in this paper with an extended Kalman Filter for identifying the spoofed signals. The well-established method of integrating INS and GNSS data for navigation accuracy improvement is taken as the baseline of this algorithm. The proposed method is an extension of a loosely coupled integration algorithm already established for fusing INS and GNSS data. The extended Kalman filter estimated position and velocity of the receiver is used along with the satellite position and velocity computed from ephemeris to find out the range and range rate of each of the satellites to the receiver. The difference between the estimated range and range rate against the measured range and range rate is considered as filter residual. The filter residuals are taken as the parameter to identify the spoofing. The threshold of filter residuals for range and range rates is the critical parameter that decides the false isolation and missing detection. The threshold is decided based on the environment in which the receiver is operated, the dynamics expected, and the noise model. The algorithms envelopment and simulation studies are done. The simulation tests are completed using a GNSS constellation simulator with spoofed signals. The INS and GNSS error model, states selection, Kalman filter design, algorithm details, simulation tests, simulation results, advantages and disadvantages of the new algorithm, and future work are covered in this paper.

ENHANCING URBAN VEHICULAR NAVIGATION: IMPROVING CLASSICAL TOPOLOGICAL MAP MATCHING THROUGH RAY-CASTING

Abstract. Navigation is of paramount importance for land vehicles as it enables efficient and accurate movement from one location to another. Whether it is for personal navigation, commercial transportation, or emergency services, reliable navigation systems play a crucial role in ensuring safety, optimizing routes, and enhancing overall operational efficiency. This paper presents the integration of classical Topological Map Matching (TMM) with the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS), addressing the limitations of relying solely on road centerlines. A novel solution is proposed, leveraging the ray-casting algorithm to determine the predicted position’s area and employing a two-stage kinematic update process for enhanced positioning accuracy. The solution’s efficacy is evaluated through tests conducted on simulated GNSS outages within a road experiment conducted in the City of Toronto, demonstrating substantial improvements compared to the classical TMM approach. Notably, the proposed method achieved a considerable 82.33% reduction in RMS positioning error and a 33.71% improvement in maximum positioning error during the longest GNSS outage. By overcoming the limitations of classical TMM algorithms, this research contributes to the advancement of navigation and tracking systems, with future work focusing on practical implementations and optimization for diverse navigation scenarios.

Enhancing the reliability of shipborne INS/GNSS integrated navigation system during abnormal sampling periods using Bi-LSTM and robust CKF

Developing a reliable navigation system is a crucial challenge for intelligent ships. Integrating Inertial Navigation Systems (INS) with shipborne Global Navigation Satellite Systems (GNSS) provides a viable solution. However, the system's reliability heavily relies on GNSS availability. This paper proposes a method to enhance the reliability of shipborne INS/GNSS integrated navigation systems during GNSS abnormal sampling periods. The proposed method consists of two parts. Firstly, using specially designed input and output parameters, a pseudo-GNSS prediction model based on a Bidirectional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM) is constructed to generate predicted pseudo-GNSS measurements during periods of GNSS loss, effectively filling in the gaps left by the absence of actual GNSS data. Secondly, Huber's M-estimation-based Robust Cubature Kalman filter (RCKF) is integrated into the data fusion algorithm, and a switching mechanism between RCKF and the Cubature Kalman filter (CKF) is established based on the source of GNSS data utilized for measurement update, to mitigate the impact of non-Gaussian distribution prediction noise introduced by pseudo-GNSS prediction model. Experimental validation using real ship navigation data in a natural marine environment confirms the proposed method's capability to maintain navigation accuracy during GNSS loss for shipborne INS/GNSS integrated navigation systems. The algorithm's superiority becomes more evident as the duration of GNSS loss increases, underscoring its effectiveness in enhancing shipborne integrated navigation system reliability.

The Use of Augmented Reality Technologies and Georeferencing Data for the Construction of Water Supply Systems

The use of mobile devices, especially smartphones and mobile technology has increased significantly. Accordingly, various applications have been developed to meet the needs of mobile users, which include navigation. The integration of these applications with maps and various localization technologies such as augmented reality (AR) will improve the reliability of navigation systems for wayfinding under certain constraints. AR allows the user to experience an interactive environment by observing the real world augmented by digital objects. However, this technology cannot store and control data. A geospatial information system (GIS) provides storage, visualization, and analysis of geospatial data that can be used to address the challenges and shortcomings of AR. In addition, the capabilities offered by GIS can be useful for achieving sustainability goals in areas such as effective planning and resource allocation. Traditional GIS, especially two-dimensional maps, provide a weak intuitive experience for users, leading to difficulties in interpreting the map and reaching conclusions. Therefore, AR can be used as a bridge between 2D and 3D visualization. The involvement of GIS in human activities and the transition of AR use from traditional terrain outreach to industrial applications makes it worthwhile to integrate GIS with AR to benefit from the two technologies simultaneously. The simultaneous visualization of real and virtual geospatial information leads to an interactive approach. The paper analyzes the capabilities of augmented reality and geo-referenced data approaches for the construction of water systems, describes their weaknesses and strengths, notes the main characteristics of the logistic regression algorithm and considers the tasks performed by this algorithm. A comparative analysis of the algorithms is carried out.

Calibration of the three-axis accelerometer navigation unit in operation on low precision equipment

A method for calibrating a three-axis accelerometer unit of the navigation accuracy class is proposed. It is based on measuring the modulus of the gravity acceleration vector. The method provides the determination of twelve passport coefficients (including six separate values of non-orthogonality angles) of the linear metrological model of a stationary accelerometer unit under operating conditions. The developed method makes it possible to calculate the acceleration value with an acceptable error using non-precision equipment for calibration. The problem is solved by forming a system of linear nonhomogeneous algebraic equations for the desired differences of unknown actual (real) values and the passport values of an accelerometer unit's metrological model coefficients. The passport coefficients are considered to be determined during bench calibration at the manufacturer. The system of equations is formed by placing the accelerometer in at least nine calibration positions relative to the gravity vector. The solution of this system, with consideration of the limitations formed by the six additional calibration positions of the accelerometer unit, allows determining the actual values of the metrological coefficients of the accelerometer unit and using them as passport values. By reducing the reliance on expensive and complex calibration equipment, the developed method offers a cost-effective solution for maintaining the long-term accuracy and performance of the accelerometer unit over its operational lifetime. The versatility of this calibration approach enables its integration into existing manufacturing processes, ensuring consistent and reliable performance of accelerometer units across different batches, thereby enhancing the overall quality and reliability of navigation systems and related technologies.

An integrated adaptive Kalman filter for improving the reliability of navigation systems

Abstract Integrated GPS/INS using Kalman filter is the best technique for improving navigation accuracy. Assuming that the covariance matrices are known and constant, a conventional Kalman filter (CKF) is usually used, however, when they are unknown and time-varying, several adaptive estimation approaches have to be developed to estimate the statistical information of the measurement (R), process (Q), and state (P) covariance matrices. In many situations, blunders/faults in the measurement model and/or sudden changes in the dynamic model may occur during the navigation period. Therefore, the CKF, as well as the adaptive Kalman filter (AKF) will exhibit abnormal behavior and may lead the filter to be suboptimal or even diverge. In this study, the Sage-Husa adaptive Kalman filter (SHAKF) and innovation-based adaptive Kalman filter (IAKF) approaches are employed for adapting the measurement covariance matrix(R). In the case of abrupt changes in the dynamic model, the state covariance matrix (P) is adapted using the strong tracking filter (STF). The performance of these adaptive approaches is evaluated before and after simulating a fault of different sizes in the measurement and dynamic models. The results show that with a large window width, the SHAKF outperforms the CKF and IAKF. However, when the system encounters any fault either in the measurement or dynamic model, the SHAKF loses its optimality and diverges. The sensitivity of the SHAKF to the fault is because the R matrix accumulates with the propagation of the recursive noise estimator. On the other hand, the IAKF and STF provide better performance than both the CKF and SHAKF because the gain matrix is adaptively adjusted to mitigate the influence of the fault, and therefore, they behave normally when a fault of any size occurs in the measurement and/or dynamic model.

ПРОГРАММНО-АППАРАТНОЕ МОДЕЛИРОВАНИЕ КОМПЛЕКСИРОВАННОЙ ИНЕРЦИАЛЬНОЙ НАВИГАЦИОННОЙ СИСТЕМЫ НАЗЕМНОГО ОБЪЕКТА

The purpose of this paper is creation of an efficient software and hardware model of a groundmobile object navigation system. Simulation process is one of the key instruments for engineering solutionsdevelopment at all stages of a complex technical system lifecycle. Navigation system model describedin this paper is software-hardware by nature and is implemented as software modules that supporthardware interaction with each other. The described system of ground object navigation systemsimulation has been developed in the course of several design projects and scientific research works.The developed model of a ground mobile object navigation system includes several programming units.Each of these units simulates the operation process as close as possible to the original. Internal andexternal interaction interfaces and navigation system communication protocols are also simulated withthe account for real object operation cyclograms. It should be noted that one of the main advantages ofa software simulation over a developmental testing or breadboarding is the opportunity to perform aprofound analysis of operation algorithms without the use of any additional resources. At the same time,it is often necessary to perform firmware design in order to make the simulation process identical withthe real operation. The process of navigation system architecture simulation allows performing a comparativeanalysis of several base components sets and their interaction patterns. Such comparison resultsin an optimal system structure providing the information user with navigation data in the mostefficient way. The paper determines basic simulation process tasks. The simulation stage may becomecrucial for complex systems design as it may aid achieving the best technical result. The proposed structure,key elements of a ground navigation simulation system and approaches to their interaction arrangementhave been approved in the course of several research and development works. At the sametime, it was found that the validity of developed models and their compliance to simulated units allowsperforming a highly reliable navigation system analysis. The developed software-hardware model allowedprocessing telemetry records obtained under different product operation conditions, performingtheir analysis and developing engineering solutions that increase the efficiency of navigation systems.

Review of pose estimation within European ship-automation projects using GNSS-based navigation

Abstract Accurate and reliable navigation systems are necessary to deploy autonomous shipping. For this purpose, georeferenced vehicle-pose information is often generated using sensor data fusion consisting of GNSS data, inertial measurement, and other applicable aiding sensors. This technical overview summarizes current European projects and international work in the field of GNSS-based maritime navigation and introduces and discusses the sensors and methods involved. The review focuses on approaches with proprioceptive sensor technology. Therefore, as a limitation, environment sensing algorithms (e. g., SLAM) are explicitly not included. It is concluded that highly accurate and reliable sensor fusion should include complementary and redundant onboard sensors, which are tightly coupled.

Reliable SINS/SAR/GNSS Integrated Navigation System for the Supersonic Vehicle

In the GNSS-challenged/denied environments, compared with other aided navigation systems, synthetic aperture radar- (SAR-) aided navigation systems can achieve all-weather, all-day, and global positioning. This paper is aimed at designing a reliable navigation system for the supersonic vehicle in the whole flight phase. Considering the SAR is of great significance for the reliable and precise navigation of the supersonic vehicle, we introduce it to the terminal phase to cope with the interference of the global navigation satellite system (GNSS). At the same time, to accelerate the speed of SAR positioning, a positioning method based on the area feature and stack sequential decoding algorithm (SSDA) is designed. Then, applying the SAR positioning under the framework of the Kalman filter (KF), we propose a tightly coupled strapdown inertial navigation system (SINS)/SAR/GNSS integration algorithm, which can achieve positioning without a complicated fusion method between the front phase and terminal phase. The flight experiment results show that the proposed method can correct the average position error to 1.7 m within 1 cycle.

Image‐based locating and guiding for unmanned aerial vehicles using scale invariant feature transform, speeded‐up robust features, and oriented fast and rotated brief algorithms

AbstractThis study provides a method for determining the location information from a photograph taken by an Unmanned Aerial Vehicle (UAV). In today's defense industry, the goal is to design a reliable navigation system that can operate even when the Internet and GPS systems are unavailable. In the proposed system, it is expected that there is an image database in which each image has its own metadata in JavaScript Object Notation format that defines the geolocation information for the corresponding image. In real‐time, UAV flies captures an image and uses a matching algorithm to compare it with each and every photo in the database. The image with the highest match rate in the database and its metadata is returned. The UAV extracts its own location information from the metadata. For image matching, Scale Invariant Feature Transform, Speeded‐Up Robust Features, and Oriented FAST and Rotated BRIEF algorithms are used. The proposed system is tested and evaluated by using a real‐world dataset obtained from Flickr's city images. The proposed approach is proved to work with a success rate of 97.14%.

Подход и модель определения координат морских судов на основе данных АИС

Ensuring the safety of vessel traffic is of high priority for the development of maritime transport. One of the key elements of the maritime security system is the navigation system, which provides real-time information about the location of vessels, their loading status, speed and the risk of collisions with other vessels. A promising platform for creating such a navigation system is the automatic identification system (AIS). However, to turn the AIS into a reliable navigation system, it is necessary to solve a number of problems associated with the development of algorithms for synchronizing the AIS with the global navigation system, automatic processing of signals transmitted by the AIS and assessing the existing risks. To solve the above problems, this article proposes a model for developing an algorithm for determining the position of sea vessels based on AIS data and making decisions on the need for maneuvering in conditions of close proximity of vessels. It is shown that it is convenient to use the operator for ranking the conflicts of ships to predict and automatically warn ships about potential risks. At the same time, the data transmitted by the AIS on the sizes, mutual speed and orientation of approaching vessels, as well as the size of the ship domain, are used as the initial parameters for assessing the risks.

Underwater navigation with 2D forward looking SONAR: An adaptive unscented Kalman filter‐based strategy for AUVs

AbstractOne of the most significant challenges in the underwater domain is to retrieve the autonomous underwater vehicle (AUV) position within the surrounding environment. Indeed, reliable navigation systems are fundamental to perform complex tasks and missions. Most of the navigation filters for AUVs are based on Bayesian estimators such as the linear Kalman Filter (KF), the extended KF, the unscented KF, or the particle filter where, usually, different instruments including a Doppler velocity log (DVL) contribute to the localization task. The usage of forward‐looking SONARs (FLS) in navigation‐aiding is, most of the time, devoted to limiting the navigation drift of the AUV by using simultaneous localization and mapping methods. Therefore, these devices are commonly employed with a standard navigation sensors set comprising an attitude heading reference system and a DVL. In this contribution, the authors propose a novel navigation strategy specifically tailored to AUVs based on an adaptive unscented KF, where linear speed estimations are obtained with a 2D FLS instead of with a DVL and therefore promoting the employment of FLSs as an aid for underwater navigation. The marine robotics community could gain significant benefits from reliable navigation achieved with an FLS‐based navigation architecture. Most importantly, a single FLS can be used for imaging‐related applications (i.e., sonograms acquisition) and navigation, where, instead, different dedicated devices are currently employed for the two tasks. Smaller AUVs usually possess reduced payload carrying capabilities; thus, multitasking use of onboard sensors, which leads to compactness, is a desirable feature. Navigation data obtained during sea trials performed in La Spezia (Italy) at the NATO STO Centre for Maritime Research and Experimentation has been used for offline validation. Afterward, the online results of real autonomous underwater missions undertaken in La Spezia (Italy) and at Vulcano Island, Messina (Italy), are reported.

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter.

In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.

Autonomous navigation control based on improved adaptive filtering for agricultural robot

Under the complex agricultural operation environment, reliable navigation system is the basic guarantee to realize the agricultural robot automated operation. This study focuses on improving navigation accuracy and control accuracy and conducts related research on autonomous navigation control of agricultural robots. This article discusses the advantages of using strict convergence criteria and combining Sage–Husa adaptive filtering with strong tracking Kalman filtering and then proposes an improved adaptive Kalman filter algorithm. The new algorithm can effectively suppress the filter divergence, improve the dynamic performance of the filter, and ensure its better filtering accuracy and strong adaptive ability to improve navigation accuracy of GPS. Further variable structure switching method is used to prevent proportional integral differential (PID) controller integral saturation phenomenon, which effectively solves the controller over-saturation problem. And combining this method with an improved adaptive filtering algorithm not only can effectively inhibit control interference but also achieve the anti-saturation effect, thereby enhancing the stability and accuracy of the control system. Finally, the simulation and experiment of the new method show that the proposed method greatly improves the ability of the filter to suppress divergence and control precision.

A forward-looking SONAR and dynamic model-based AUV navigation strategy: Preliminary validation with FeelHippo AUV

Reliable navigation systems are fundamental for Autonomous Underwater Vehicles (AUVs) to perform complex tasks and missions. It is well known that the Global Positioning System (GPS) cannot be employed in underwater scenarios; thus, during missions below the sea's surface the real-time position is usually obtained with expensive sensors, such as the Doppler Velocity Log (DVL), integrated within a navigation filter such as an Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), or Dead Reckoning (DR) strategies. The goal of this work is to develop an underwater navigation system that does not rely on a DVL and where linear speed estimations are obtained exploiting data from a Forward-Looking SONAR (FLS) or, in its absence, taking advantage of a dynamic model that presents a reduced set of parameters. The proposed solution is validated through the use of navigation data obtained during sea trials undertaken in July 2018 with FeelHippo AUV at La Spezia (Italy), at the NATO Science and Technology Organization Center for Maritime Research and Experimentation (CMRE).

Simultaneous Underwater Navigation and Mapping

The use of underwater autonomous vehicles has been growing, allowing the performance of tasks that cause inherent risks to Human, namely in inspection processes near to structures. With growth in usage of systems with autonomous navigation, visual acquisition methods have also gotten more developed because, they have appealing cost and they also show interesting results when operate at a short distance. It is possible to improve the quality of navigation through visual SLAM techniques which can map and locate simultaneously and its key aspect is the detection of revisited areas. These techniques are not usually applied to underwater scenarios and, therefore, its performance in environment is unknown. The paper presents a more reliable navigation system for underwater vehicles, resorting to some visual SLAM techniques from literature. The results, conducted in a realistic scenario, demonstrated the ability of the system to be applied to underwater environment.

Applying Markov decision process to adaptive dynamic route selection model

Routing technologies have long been available in many automobiles and smart phones, but the nearly random nature of traffic on road networks has always encouraged further efforts to improve the reliability of navigation systems. Given the networks' uncertainty, an adaptive dynamic route selection model based on reinforcement learning is proposed. In the proposed method, the Markov decision process (MDP) is used to train simulated agents in a network so that they are able to make independent decisions under random conditions and, accordingly, determine the set of routes with the shortest travel time. The aim of the research was to integrate the MDP with a multi-nomial logit model (a widely used stochastic discrete-choice model) to improve finding the stochastic shortest path by computing the probability of selecting an arc from several interconnected arcs based on observations made at the arc location. The proposed model, tested with real data from part of the road network in Isfahan, Iran, and the results obtained demonstrated its good performance under 100 randomly applied stochastic scenarios.