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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Measures for more precise processing of primary information from micromechanical sensors in an integrated navigation system are considered. Algorithms are proposed for responding to errors of inertial sensors. Analysis shows that multistep filtration of inertial measurements permits improvement in the accuracy of the raw measurements by an order of magnitude. The use of such algorithms permits more prolonged autonomous operation, with the retention of a stable navigation assessment even when using inexpensive commercial equipment. [ABSTRACT FROM AUTHOR]

This paper presents a novel nonlinear adaptive sensor fusion method for integrated navigation systems with varying noise parameters. The innovation is utilizing deep neural networks to mine the noise-related patterns of specific sensors and combining it with conventional nonlinear filters. This hybrid approach improves the feasibility and robustness of adaptive filtering by achieving an effective estimation of the originally weakly observable noise parameters. The specific sensors are defined as α -type sensors whose errors are entirely generated by themselves. The mathematical model for analyzing α -type sensors output sequence and the deep neural network for mining the patterns of interest are established. All adaptive filtering systems using α -type sensors can benefit from this paper. Specifically, it is applied to inertial and satellite integrated navigation system. The numerical experiments indicate that the proposed filter achieves promising accuracy and robustness improvement as compared to conventional nonlinear filters. And the comparisons between different nonlinear approximation algorithms indicate that the first-order approximation is accurate enough for our application. [ABSTRACT FROM AUTHOR]

Currently, integrated navigation systems with the inertial navigation system (INS)/geomagnetic navigation system (GNS) have been widely used in underwater navigation of autonomous underwater vehicle (AUV). Restricting AUV to navigate in the geomagnetic suitability areas (GSA) as far as possible can effectively improve the accuracy of integrated navigation systems. In order to improve the classification accuracy of GSA, a new optimal classification method based on principal component analysis (PCA) and improved back propagation (BP) neural network is proposed. PCA is used to extract the independent characteristic parameters containing the main components. Then, considering similarity coefficient, the initial weights and thresholds of BP neural network is optimized by improved adaptive genetic algorithm (IAGA). Finally, the correspondence between the geomagnetic characteristic parameters and matching performance is established based on PCA and improved adaptive genetic algorithm and back propagation (IAGA-BP) neural network for the automatic recognition of GSA. Simulated experiments based on PCA and IAGA-BP neural network shows high classification accuracy and reliability in the GSA selection. The method could provide important support for AUV path planning, which is an effective guarantee for AUV high precision and long voyage autonomous navigation. [ABSTRACT FROM AUTHOR]

Summary: An adaptive integrated navigation system, consisting of barometric altimeter (Baro) and GPS, is presented. The integration is achieved by using an adaptive data fusion algorithm with the filter gain correction. Proposed adaptive data fusion algorithm utilizes the measurement noise scale factor in order to reduce the effects of the incorrect barometric altimeter measurements on the estimation procedure. By using the algorithm, incorrect baro measurements are corrected by the system without any impact on the good estimation behavior. Performance of the proposed Baro/GPS integrated system is examined by simulations for altitude estimation of an aircraft. [ABSTRACT FROM AUTHOR]

Navigation of land or self-driving vehicles is essential for safe and accurate travel. The global navigation satellite systems (GNSSs), such as global positioning system (GPS) are the primary sources of navigation information for such purpose. However, high-rise buildings in urban canyons block the GPS satellites signals. Alternatively, inertial navigation system (INS) is typically working as a backup. A reduced inertial sensor system (RISS) is used instead of the full INS to achieve the same purpose in land vehicles navigation with fewer sensors and computations. Unfortunately, the RISS solution drifts over time, but this can be mitigated when integrated with the GPS. However, the integration solution drifts in the case of GPS signal loss (outages). Therefore, the position errors grow especially during extended periods of GPS outages. Azimuth/heading angle is critical to keep the vehicle on the route. In this paper, an azimuth update estimated from a calibrated magnetometer is introduced to improve the accuracy of the overall system. A new approach is proposed for pre-processing the magnetometer data utilizing a discrete-cosine-transform (DCT)-based pre-filtering stage. The obtained azimuth is utilized in updating the RISS system during the whole trajectory and mainly during GPS outage periods. The proposed approach significantly decreases both the azimuth error and the position error growth rate when driving in urban canyons where the GPS signals are blocked. Finally, the proposed system was tested on a real road trajectory data for a metropolitan area. The results demonstrate that the accuracy of the whole system improved, especially during the GPS outage periods. [ABSTRACT FROM AUTHOR]

Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors (inertial measurement unit—IMU). The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea. [ABSTRACT FROM AUTHOR]

Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors (inertial measurement unit—IMU). The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea.

Gaussian process regression (GPR) is used in a Spare-grid Quadrature Kalman filter (SGQKF) for Strap-down Inertial Navigation System (SINS)/odometer integrated navigation to bridge uncertain observation outages and maintain an estimate of the evolving SINS biases. The SGQKF uses nonlinearized dynamic models with complex stochastic nonlinearities so the performance degrades significantly during observation outages owing to the uncertainties and noise. The GPR calculates the residual output after factoring in the contributions of the parametric model that is used as a nonlinear SINS error predictor integrated into the SGQKF. The sensor measurements and SINS output deviations from the odometer are collected in a data set during observation availability. The GPR is then applied to predict SINS deviations from the odometer and then the predicted SINS deviations are fed to the SGQKF as an actual update to estimate all SINS biases during observation outages. We demonstrate our method's effectiveness in bridging uncertain observation outages in simulations and in real road tests. The results agree with the theoretical analysis, which demonstrate that SGQKF using GPR can maintain an estimate of the evolving SINS biases during signal outages. [ABSTRACT FROM AUTHOR]

Abstract: The fault tolerant integrated navigation system, consisting of radar and inertial altimeters, is presented. In the open loop system, the inertial altimeter is the main source of information, and radar altimeter provides discrete aiding data to support the estimations. The integration is achieved by using a Nonlinear Robust Adaptive Kalman Filter with the filter gain correction based on the evaluation of the posterior probability of the normal operation of altimeter, given for current measurement. This probability is proposed to calculate via the posterior probability density of the normalized innovation sequence at the current estimation step. In the proposed filtration algorithm, the filter gain is corrected by multiplying with the mentioned posterior probability, which plays the role of the weight coefficients to the innovation vector. As a result, faults in the estimation system are corrected by the system, without affecting the good estimation behavior. The performance of the proposed fault tolerant integrated radar/inertial altimeter is tested for the different types of measurement faults; instantaneous abnormal measurements, continuous bias at measurements, measurement noise increment and fault of zero output. [Copyright &y& Elsevier]

This paper deals with the problem of designing a flexible and accurate integrated navigation system (INS) for marine craft. The proposed INS is based on the integration of a global positioning system (GPS) with a compass and a speed log. After introducing the scopes and functions of the proposed INS, mathematical models of its main components are presented. Then the development of a new multisensor data fusion algorithm for carrying out an accurate estimation of the main state variables is presented. The theoretical background for the sensor fusion is based on the classical Kalman filter theory, which allows to update an a priori position estimate, given by a dead-reckoning system, with the information supplied by a GPS positioning system. Finally the filtering algorithm is extended in the framework of interval analysis and fuzzy set theory in order to improve the reliability and robustness of the estimation algorithm. The validity of the proposed approach is demonstrated by simulation examples applied to a container ship navigating in realistic conditions. [ABSTRACT FROM AUTHOR]

Three algorithms for fusing local estimates are compared. The first one (algorithm A) is the well known Federated filtering algorithm proposed by Carlson [Federated filter for fault-tolerant integrated navigation systems, in: Proceedings of IEEE Position, Location and Navigation Symposium, Oriando, FL, 1988 pp. 110–119; IEEE Trans. Aerospace and Electronic System 26 (3) (1990) 517–525], which needs an Upper Bound technique to eliminate the correlation between local estimates, and a reset procedure to make the global estimate optimal. The second one (algorithm B) proposed by Hong Jin and Hong Yue Zhang directly calculates the optimal global estimate as a weighted sum of correlated local estimates using general weighting matrices [Fusion algorithm of correlated local estimates for federated filter, in: Proceedings of the 3rd Asian Control Conference, Shanghai, 2000, pp. 1428–1433]. In this paper a simplified algorithm (algorithm C) is derived, which uses diagonal weighting matrices. The simplification leads to less computation as compared to that of algorithm B, but the global estimate is sub-optimal. Comparison between these three algorithms is conducted by theoretical analysis and extensive simulations as well. The comparison reveals that the algorithm C has moderate calculation load, strong fault tolerance and little loss in estimation accuracy. And the sensitivities to the values of covariance matrices of noises are similar for the three algorithms. [Copyright &y& Elsevier]