A Collision Avoidance Expert System for Integrated Navigation System and Its Brush-up

An Integrated Navigation System (INS) is expected to serve as a tool helping in reduction of maritime casualties and ensuring the safety of navigation. Such an INS should provide a maneuvering support system and integrate the information delivered to human operators aiming at the improvement of the navigation safety. The authors have developed a supporting system in INS for collision avoidance aiming at reduction of the operators’ work load and supporting the decision-making for navigators. 1)2) The prototypes of INS were set in the shiphandling simulators and simulation studies were carried out in order to improve and evaluate the system. Many kinds of supporting methods have been tested. Finally, several methods have been proposed for assisting the decision-making during the collision avoidance. These supporting methods use data provided by ship position detection and guidance systems, which are basic technologies of INS. The authors discuss the effectiveness of their method. The authors have also developed the collision avoidance expert system that solves the collision avoidance problem and advise a desirable collision avoidance action to the operator. Some experiments on the intelligent support of decision-making in INS were conducted at the ship handling simulator equipped with INS and collision avoidance expert system.

Celestial navigation system (CNS) is a completely autonomous navigation technology with strong anti-interference ability and high navigation accuracy, which has a great application prospect. Aim at defect of traditional integrated navigation system using attitude angle measurement celestial navigation system and strapdown inertial navigation system (SINS), which can only correct of strapdown inertial navigation system’s gyro drift. This paper proposes an improved integrated navigation system that add double-star positioning celestial navigation to traditional SINS/CNS integrated navigation system, meanwhile, introduces the fault detection technology based on statistical distance to solve the problem that inaccuracy of measurement information leads to the integrated navigation system’s performance degradation. By analyzing the simulation results, the improved navigation system can inhibit divergence of system and obtain high accurate and stable navigation information.

In this article, a prediction method based on a radial basis function neural network and an improved unscented Kalman filter, is proposed to improve the accuracy of position and velocity of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system, especially in the presence of GPS signal outages. The improved unscented Kalman filter based on the adaptive theory is adopted to enhance the positioning accuracy of the GPS/SINS integrated navigation system when a GPS signal is available. A radial basis function neural network and a non-stationary time series analysis are used to predict and compensate for the positioning error of the GPS/SINS integration in the presence of GPS signal outages to improve the reliability of the navigation system. The effectiveness of the proposed method is verified by the simulation experiment and vehicle test. The simulation and test results show that the proposed method can improve the position accuracy and velocity accuracy in different GPS outage time periods.

This paper presents the concept and method of InSAR aided INS navigation. InSAR interferometric phase is sensitive to the attitude of the platform and the terrain height, so it can be used to improve the precision of navigation substantially. InSAR/INS integrated navigation system can be used on the flight vehicle navigation and it can satisfy complicated topography and long-distance accurate autonomous navigation. The technical route of InSAR/INS integrated navigation system is discussed in detail in this article. We can obtain the location shift of azimuth and range by matching two interference fringes, one is produced by InSAR system and another is generated by reference path and digital elevation database. And the simulation results indicate the precise and efficient of the matching algorithm. With the aid of the location shift we can inverse the attitude and position information of the platform. The location shift and the IMU information are set to the all-state combined filter to obtain the navigation output.

Due to the influence of the complex underwater environment, the initial alignment method for Doppler velocity log (DVL)-aided strap-down inertial navigation systems (SINS) often suffer from performance degradation, especially when DVL measurements are contaminated by outliers. In this paper, an outlier-resistant Initial Alignment method with interference suppression for SINS/DVL integrated navigation system is proposed, by which, by constructing an improved Mahalanobis distance anomalous detection criterion, the anomaly of the residual vector composed of observation vectors is judged, and an adaptive weighting factor is introduced into the observation matrix to suppress the abnormal interference in the alignment process. Simulation and experimental results show that, compared with existing initial alignment methods, the proposed method achieves higher alignment accuracy in the presence of outliers, which is more suitable for the SINS/DVL integrated navigation system.

The GPS/INS Integrated Navigation System is nonlinear in nature. To deal with the accuracy of GPS/INS navigation under nonlinear, strong tracking cubature Kalman flter (STCKF) is applied to the system. The heart of the CKF is a cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. STCKF is presented for simulation. Simulation results show the superior performance of this approach when compared with clasaical suboptimal techniques such as extended Kalman filter (EKF). The results of simulation demonstrate the improved performance of the STCKF over conventional nonlinear filters. The research provides theoretical support for engineering design and modification. STCKF has the advantages of high reliability, low sensitivity, strong robustness, strong stability and convergence.

The history of manav goes back fourteen years to 1967 when Esso placed a contract with the Ministry of Technology (mintech) to carry out work to develop a system to assist in ensuring the safe operation of its Very Large Crude Carriers, which were then coming into service. The 1967 era saw much public, governmental and company concern about the stopping and manoeuvring ability of large tankers, particularly when close to shore in estuaries or when berthing.manav was therefore one of a number of developments or studies initiated by the introduction of VLCCs. Some years were to pass before coordination of the various study results proved possible.The Racal-Decca (or Decca as it then was) involvement began in 1969 when Esso and mintech decided that an industrial partner should be sought. From the firms approached, the Decca Navigator Company was contracted to undertake a design study. The cost of this study, which started in January 1971, was equally shared between Decca and mintech. This said, it is remarkable how even with a wealth of supporting data having been collected in the intervening fourteen years, the objectives for manav, as set by Decca and mintech, have not radically changed.The present Department of Industry involvement in funding the sea trial evaluation of manav has its roots in yet a different study; namely the Bridge and Wheelhouse Ergonomics Study, which resulted in the DOI Code of Practice for Ship's Bridge Design.

The extended Kalman filter (EKF) has been used widely in Global Position System (GPS) and Strapdown Inertial Navigation Systems (SINS) integrated navigation systems,which simply linearizes all nonlinear models.However, careful treatment of the nonlinearity of the system models is particularly critical when the integrated systems use low cost micro-electro-mechanical (MEMS) inertial sensors. Hence application of the EKF in MEMS inertial sensor-based integrated navigation systems will likely lead to inaccurate results. To overcome the shortcomings of the EKF, the unscented Kalman filter (UKF) has been proposed.The UKF does not require the linearization of the system models. Alternatively it uses a set of deterministically selected "sigma-points",which completely capture the true mean and covariance of the original random vector. Then these sigma-points are propagated through the nonlinear models.This captures the mean and covariance to second order accuracy for arbitrary nonlinear functions. In this paper the EKF and UKF are applied in GPS/SINS integrated systems respectively. Simulation result demonstrates that the performance of the UKF is better than the EKF in MEMS inertial sensor-based SINS/GPS integrated systems.

In many communication scenarios, such as in GPS communications and integrated navigation systems, signals with wide space distributions need to be processed simultaneously or promptly. Hemispherical arrays have greater capability in receiving and processing signal than planar arrays, and can provide robust performance. In previous research, the directivity of a hemispherical array is compared with several kinds of planar arrays, that shows this kind of array has essentially better scanning performance. In this paper, the effect of element pattern on the hemispherical array's scanning directivity is studied, and the main factors which affect the parameters of directivity are analysed and the effects of these factors on directivity are discussed. Simulation results are presented for illustration.

To solve the problem of information fusion in the strapdown inertial navigation system (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system described by the nonlinear/non-Gaussian error models, a new algorithm called the federated unscented particle filtering (FUPF) algorithm was introduced. In this algorithm, the unscented particle filter (UPF) served as the local filter, the federated filter was used to fuse outputs of all local filters, and the global filter result was obtained. Because the algorithm was not confined to the assumption of Gaussian noise, it was of great significance to integrated navigation systems described by the non-Gaussian noise. The proposed algorithm was tested in a vehicle’s maneuvering trajectory, which included six flight phases: climbing, level flight, left turning, level flight, right turning and level flight. Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter (FUKF). For instance, the mean of position-error decreases from (0.640×10−6 rad, 0.667×10−6 rad, 4.25 m) of FUKF to (0.403×10−6 rad, 0.251×10−6 rad, 1.36 m) of FUPF. In comparison of the FUKF, the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.

Sensors are important parts of the navigation system. The decentralized filtering for sensor fault tolerance has great significance for improving system reliability. The method combining federated filtering and covariance intersection filtering is adopted to achieve the decentralized filtering of sensor fault tolerance, and the navigation accuracy and tolerance are analyzed. The simulation results indicate that the implementation of decentralized filtering on integrated navigation system that composed of multi sensors can bring the benefits of high accuracy and improve the system capability of fault tolerance due to the information redundancy. Thus, more completed and accurate navigation information about the movable sensor carrier can be provided.

In this paper, we investigate the problem of autonomous integrated navigation of strapdown inertial navigation system (SINS)/ celestial navigation system (CNS) for a ballistic missile. To handle the nonlinear missile dynamic model, three filtering methods including extended Kalman filter (EKF), extended Kalman particle filter (EKPF) and unscented particle filter (UPF) are utilized to fusion the integrated navigation system and improve the navigation accuracy. The performance of the navigation system based on the three methods is analyzed and compared. Simulation results indicate that the UPF can provide the highest navigation accuracy with the price of highest computation burden.

This paper proposes a magnetic compass fault detection method for GPS/INS/Magnetic compass integrated navigation systems. The fault is assumed to be caused by the hard iron and soft iron effect and modeled as an abrupt change in the magnetic compass output. In order to detect the fault, a test statistic related with only azimuth error measurement is determined. When a fault is detected, the GPS/INS/Magnetic compass integrated navigation system is changed into a GPS/INS integrated navigation system mode. In order to show the validity of the proposed method, computer simulation and van testing are carried out. The simulation and van test results show that the proposed navigation system gives more accurate outputs than the GPS/INS/Magnetic compass without the proposed method.

A Kalman filter algorithm based on exact modeling for FOG GPS/SINS integration

This paper presents a new approach for integrated landing systems. Applicable avionics platforms are military aircraft, helicopters, and unmanned aerial vehicles. In this paper we focus on the introduction of NILS (new integrated landing system) in the Saab fourth generation fighter JAS39 Gripen. NILS is based on an integrated navigation system (NINS) which is covered in the paper as well. The landing system in older generations of aircraft is usually designed by combination of different equipment. In the fourth generation aircraft the same functionality is solved in software implemented in the aircraft system computers. Typical applications of Saab NINS are accurate random route navigation (RNAV) under instrument flight rules, inputs to the auto-pilot, ground collision avoidance system (GCAS), terrain following, landing system and weapon initialization. Future applications are inputs to head-up or head-down display synthetic terrain presentation, mid-air collision avoidance system (MCAS) and passive target ranging.