Multi-source Navigation Research Articles

A novel multi-source navigation algorithm based on factor graph in complex underwater environments of polar regions

Autonomous underwater vehicle (AUV) is an unmanned, cableless, self-propelled, autonomous navigation and control vehicle that maneuvers under unmanned or minimally monitored and intervened conditions. To solve the problem of increasing navigation accuracy error caused by time-varying measurement noise of AUV auxiliary sensors in complex underwater environments of polar regions, a multi-source navigation algorithm for AUV based on improved factor graph is proposed. The algorithm first adds a confidence function to the standard factor graph algorithm, which is used to adjust the covariance matrix of the navigation sensor to suppress the problem of time-varying measurement noise. Then, a sliding window is added to the factor graph algorithm and only nodes within the sliding window are optimized to ensure real-time performance. Finally, considering that inertial navigation has high accuracy in a short period of time, inertial navigation iterations are performed on nodes that are about to be marginalized within the sliding window to improve navigation accuracy. The analyses of the simulation experiment results show that the algorithm can effectively cope with the time-varying problem of measurement noise and ensure the accuracy and robustness of the AUV navigation system in the complex marine environment.

Real-Time Processing and High-Quality Imaging of Navigation Strip Data Using SSS Based on AUVs

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications.

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.

An Adaptive Federated Filter Based on Variational Bayes With Application to Multisource Navigation

Information fusion is one of the key technologies in airborne multi-source navigation, where federated filter is widely used due to its simple structure. However, the sensor measurement of multi-source navigation system will become unreliable in some interferential environment, which leads to the state and measurement noise covariance matrix change over time and estimate difficultly. Inaccurate covariance matrice cause the loss of positioning accuracy in traditional federated filter. To address the problems, this paper puts forward an adaptive federal Kalman filtering algorithm based on Variational Bayesian and applies it to the INS/GPS/Terrain/Geomagnetic multi-source navigation system. Simulation and experiment results show that the proposed filter outperforms the traditional federated filter at improving the positioning accuracy under the condition of time-varying or even unknown state and measurement noise covariance matrix, which plays a positive role in UAV multi-source navigation.

Resilient Multi-Source Integrated Navigation Method for Aerospace Vehicles Based on On-Line Evaluation of Redundant Information

Aerospace vehicle navigation systems are equipped with multi-source redundant navigation sensors. According to the characteristics of the above navigation system configuration, building a resilient navigation framework to improve the accuracy and robustness of the navigation system has become an urgent problem to be solved. In the existing integrated navigation methods, redundant information is only used for backup. So, it cannot use the redundant navigation information to improve the accuracy of the navigation system. In this paper, a resilient multi-source fusion integrated navigation method based on comprehensive information evaluation has been proposed by combining of qualitative analysis and quantitative analysis in information theory. Firstly, this paper proposes a multi-layer evaluation framework of redundant information and carries out quantitative analysis of redundant information with the information disorder analysis theory to improve the reliability of the navigation system. Secondly, a navigation output effectiveness evaluation system has been established to analyze the output of heterogeneous navigation subsystems qualitatively to improve the fusion accuracy. Finally, through the mutual correction of multi-level information evaluation results, the error decoupling between the output parameters of heterogeneous navigation sensors has been realized to improve the robustness of the system. The experimental results show that the method proposed in this paper can adaptively allocate and adjust the weight of navigation information at all levels, realize the “non-stop” work of the navigation system and enhance the resilient of the navigation architecture. The navigation accuracy is improved compared with the existing multi-source fusion algorithm, which reflects the reliability and robustness of this algorithm.

An Indoor and Outdoor Multi-Source Elastic Fusion Navigation and Positioning Algorithm Based on Particle Filters

In terms of indoor and outdoor positioning, in recent years, researchers at home and abroad have proposed some multisource integrated navigation and positioning methods, but these methods are navigation and positioning methods for a single scene. When it comes to the switching of indoor and outdoor complex scenes, these methods will cause the results of position with a marked jump and affect the accuracy of navigation and positioning. Aiming at the navigation and positioning problem in the case of indoor and outdoor complex scene switching, this paper proposes a multisource elastic navigation and positioning method based on particle filters, which makes full use of the redundant information of multisource sensors, constructs an elastic multisource fusion navigation and positioning model after eliminating abnormal data, elastically gives different particle weights to multisource sensors according to environmental changes and realizes the elastic fusion and positioning of multisource sensors through filtering. The test results show that this method has high navigation and positioning accuracy, the dynamic positioning accuracy is better than 0.7 m and there will be no jumping of positioning results in the process of scene switching, which improves the navigation and positioning accuracy and stability in complex and changeable indoor and outdoor environments.

Design of robust cubature fission particle filter algorithm in multi-source cooperative navigation

As a part of the multi-source cooperative navigation scheme, data fusion has a significant impact on the quality of state estimation. Particle filtering has gradually become the focus of many fusion methods due to its unique theoretical advantages in nonlinear non-Gaussian systems. However, the particle degradation and the resulting sample impoverishment restrict its application in complex engineering scenarios. In this paper, a robust cubature fission particle filter (RCFPF) is proposed to deal with these problems. First, in the framework of cubature rule, Huber function is used to combine the L2 norm and L1 norm to improve the importance density function(IDF), suppress the observation noise. Meanwhile, the proposed distribution(PD) is further optimized by combining the Gaussian distribution with Laplace distribution to alleviate particle degradation. Second, the particle swarm is fissioned before resampling, and the particle weight is reconstructed by fission of high weight particles and covering low weight particles to inhibit sample impoverishment. The vehicle experiments of multi-source cooperative navigation show that the proposed algorithm achieves better test results in accuracy and robustness than extended Kalman filter (EKF), strong tracking particle filter (STPF), and cubature particle filter (CPF).

Upper Stage Visual Inertial Integrated Navigation Method Based on Factor Graph

In the working process of the upper stage integrated navigation information fusion system, the multi-source navigation information fusion algorithm based on factor graph Bayesian estimation is used to fuse the information of inertial sensors, visual sensors and other sensors. The overall joint probability distribution of the system is described in the form of probability graph model with the dependence of local variables, so as to reduce the complexity of the system, adjust the data structure of information fusion to improve the efficiency of information fusion and smoothly switch the sensor configuration.

SINS/CNS/GNSS Integrated Navigation Based on an Improved Federated Sage–Husa Adaptive Filter

Among the methods of the multi-source navigation filter, as a distributed method, the federated filter has a small calculation amount with Gaussian state noise, and it is easy to achieve global optimization. However, when the state noise is time-varying or its initial estimation is not accurate, there will be a big difference with the true value in the result of the federated filter. For the systems with time-varying noise, adaptive filter is widely used for its remarkable advantages. Therefore, this paper proposes a federated Sage–Husa adaptive filter for multi-source navigation systems with time-varying or mis-estimated state noise. Because both the federated and the adaptive principles are different in updating the covariance of the state noise, it is required to weight the two updating methods to obtain a combined method with stability and adaptability. In addition, according to the characteristics of the system, the weighting coefficient is formed by the exponential function. This federated adaptive filter is applied to the SINS/CNS/GNSS integrated navigation, and the simulation results show that this method is effective.

Research on Adaptive Multi-Source Information Fault-Tolerant Navigation Method Based on No-Reference System Diagnosis.

In order to obtain accurate and optimized navigation sensor information, it is necessary to study information fusion and fault diagnosis with high reliability, high precision and high autonomy, and then to propose a rapid and accurate intelligent decision-making scheme based on multi-source and heterogeneous navigation information. In view of the existing fault-tolerant navigation federated filter structure, the method of assuming the reference system (inertial navigation system) to be fault-free and then diagnosing the measuring sensor fault is generally adopted. Considering that the structure of the filter can’t detect and isolate the faults of the inertial navigation system, the performance of the MEMS inertial navigation system declines due to complex environments resulting from vibrations and temperature changes; additionally, external interference may lead to the direct failure of the MEMS inertial device. Therefore, this paper studies a fault-tolerant navigation method based on a no-reference system. For the sensor sub-system of a custom micro air vehicle (MAV), a fault detection method based on a reference-free system is proposed. Based on the fault type analysis, some improvements have been made to the existing residual chi-square detection method, and an interactive residual fault detection method with distributed states is proposed. On this basis, aiming at the characteristics of a reference-free system, the weight distribution scheme of the reference system and the tested systems are studied, and a self-regulation filter fusion and fault detection method based on reference-free system is designed.

Research on shore-based intelligent vessel support system based on multi-source navigation sensors simulation

Under the guidance of industry 4.0, peoples pay more attention to the intelligent equipment and system. Intelligent vessel without crew on board has acquired growing attention worldwide over the years. The navigation condition of intelligent vessel without crew at sea should be monitored and controlled by the shore-based intelligent vessel support system. The navigation-related data collected by the multi-source navigation sensors should be presented on the shore-based support system in an efficient and friendly way. This article presents a simulation model for multi-source navigation sensors such as echo sounder and electronic position-fixing system. Furthermore, this work proposes an efficient architecture for the shore-based support system by way of multi-source navigation sensors simulation. The results show that the simulation model of multi-source navigation sensors is efficient for the simulation of echo sounder and electronic position-fixing system. The shore-based intelligent vessel support system based on multi-source navigation sensors simulation can present navigation-related data and monitor the condition of intelligent vessel at sea.

Navigation and location experiment and method analysis of multiple radiation sources detection

It is a completely new navigation and positioning method by using the particle ray emitted from known targets, which provides a new idea for the existing navigation and location. Therefore, a multi-source navigation and positioning method is proposed, and the principle of locating the detector by four radioactive sources of 22Na, 60Co, 137Cs and 252Cf is analyzed, and an analog positioning system consisting of a detector, an operational amplifier, a digitizer and a sampling server is set up. Based on the simulation positioning system, the positioning method is verified experimentally, and the whole positioning process using the positioning method is reported. The location of the detector coordinates is (152.5, 231.8,182), which verifies the feasibility of the positioning method. The location result and the true coordinates of the detector (100,200,150) have error and the image is in the shadow. The factors that affect the positioning error are analyzed, which provides a new idea and method for the study of pulsar navigation and positioning.

Fault detection for multi‐source integrated navigation system using fully convolutional neural network

An accurate fault detection method is critical in preventing the integrity of multi-source navigation system from the abnormal measurements which may occur any time. Here, a multi-channel single-dimensional fully convolutional neural network fault detection method is proposed, where the system measuring residuals sequence is used as the input, and the output is the system operating state, such as normal or fault types, in pointwise. The proposed technique extracts the features with various scales, which contain both the local and the general information of the signal sequence, for making a comprehensive and precise classification. To show the validity of the proposed method, computer simulations and trolley testing based on INS/GNSS/UWB integrated navigation system are carried out. The simulation and experimental results show that the proposed fault detection method is superior to the existing algorithms on the faults detection rate and false alarm rate, and thus, system reliability and navigation precision have been greatly improved.