Correction of navigation systems by using satellite navigation system signals and genetic algorithm

Navigation parameters such as vehicle heading, linear speed and latitude and longitude are determined today, as a rule,using satellite navigation systems. However, there is a need for autonomous determination of navigation parameterswhen satellite navigation system signals are unavailable for one reason or another. For example, satellite navigationsystem signals can be jammed by electromagnetic countermeasures.Autonomous systems for determining the vehicle navigation parameters are inertial navigation systems, the basis ofwhich is the double integration of signals from accelerometers, as well as the integration of signals from gyroscopes toform one or another coordinate system, if we are talking about strapdown inertial navigation systems (SINS).Integration of the output signals of accelerometers and gyroscopes, which contain errors, leads to the accumulation ofSINS errors. To increase accuracy, SINS are integrated, for example, with satellite navigation systems. However, suchsystems cease to be completely autonomous.The article uses the method of autonomous determination of vehicle latitude and longitude, which is a new method ininertial navigation. The method is based on the strapdown inertial technology, which, unlike the standard methods usedin SINS, does not use the integration of the output signals of accelerometers and gyroscopes. To determine the angles oflatitude and longitude, the signals of the gyroscopes included in the inertial measurement unite (IMU) are used.As a result of a semi-natural experiment, the efficiency of the method of autonomous determination of latitude andlongitude was verified. Flight data of a small-sized aircraft were used for experimental verification. The data containedinformation about the angles of the heading, pitch, roll of the aircraft, the output signals of three micromechanicalgyroscopes, three micromechanical accelerometers, information about latitude, longitude and altitude measured by thereceiver of the satellite navigation system, as well as information about the horizontal and vertical components of speedand current time. Plots of calculated values of latitude and longitude and values measured using the receiver of thesatellite navigation system are given. From the given plots and tables, it can be seen that from a certain time, thecalculated values coincide with the "reference" ones, which confirms the efficiency of the method of autonomousdetermination of latitude and longitude.

The sidewalk is often located in complex environments such as buildings or covered by dense trees. Satellite navigation systems are susceptible to the surrounding environment, resulting in multipath effects, resulting in reduced positioning accuracy. Therefore, the global satellite navigation system (GNSS) and inertial navigation system (INS) based on kalman filter (KF) algorithm are studied, and the navigation algorithm and system model suitable for pedestrians are given. Considering that the pedestrian speed is low, the satellite navigation system obtains the carrier orientation information (heading angle), which also produces a large error. Therefore, the pedestrian dead reckoning (PDR) correction method is adopted to make the positional accuracy of the integrated navigation continuously stable. This paper proposes a pedestrian navigation system solution based on a single navigation device.

Satellite navigation systems, inertial navigation systems and integrated systems (satellite navigation systems and inertial navigation systems) are used to determine navigation parameters. But satellite navigation systems are not autonomous. Although inertial navigation systems are autonomous, they accumulate errors over time that require external correction. A new method of autonomous determination of latitude and longitude of moving objects is proposed. Unlike standard methods, this method does not use double integration of accelerometer signals. To determine latitude and longitude, gyroscope signals are used, which are part of the inertial measurement unit (IMU). To implement the method of autonomous determination of latitude, it is enough to have the signals of the IMU gyroscopes, the elements of the direction cosines matrix, the angles of rotation of the object, and their derivatives. For the autonomous determination of longitude, it is necessary, in addition, to know the initial value of the longitude. The use of the Luenberger observer to determine of latitude and longitude is considered.

A new dynamic calibration method for IMU deterministic errors of the INS on the Hypersonic Cruise Vehicles

INS/UWB fusion localization algorithm in indoor environment based on variational Bayesian and error compensation

The Extended Kalman Filter (EKF) is currently a dominant method of sensor fusion used for navigation of mobile devices, robotics and autonomous vehicles. One navigation system involves the EKF fusion of an Inertial Navigation System (INS) with a Global Navigation Satellite System (GNSS) to perform 3D pose estimation, which is essential to practical applications like autonomous vehicles and UAVs. This system involves using the INS to predict pose changes and uses GNSS measurements when available to correct for estimation drifts and sensor errors. The performance of the state estimation is heavily dependent on the accurate selection of EKF parameters, leading to the optimal selection of parameters being a critical factor in the design and use of the EKF. In this paper, a methodical and efficient method of EKF parameter tuning is presented. The tuning framework uses nominal parameters generated by Gauss Markov (GM) and Allan Variance (AV) methods that are tuned by Genetic Algorithms (GA) accelerated by a Design of Experiment (DoE) technique to efficiently optimize EKF parameters. This framework has been implemented in MATLAB and tested using simulations and real data. The results demonstrate that GA tuned parameters increases accuracy substantially, and that the DoE technique consistently improves the convergence behavior of the GA.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

The article considers the estimation algorithms used in the correction schemes of aircraft inertial navigation systems. The Kalman filter and its adaptive modifications capable of functioning in the absence of a priori information on the statistical characteristics of the input as well as measurement noise are investigated. The accuracy of the algorithms was estimated using the mathematical modeling and modeling from a seminatural experiment with a real inertial navigation system. The real navigation system is installed on a fixed base, which made it possible to isolate its errors in determining the speed and then evaluate the entire vector of the INS error state using the adaptive Kalman filter. The accuracy of the error compensation was evaluated using a special technique. The results of the mathematical and semi-natural modeling confirmed the high efficiency of the developed inertial navigation system correction algorithm.

The necessity of integrated navigation complexes (INC) construction is substantiated. It is proposed to include in the complex the following inertial systems: inertial, satellite and visual. It helps to increase the accuracy of determining the coordinates of unmanned aerial vehicles. It is shown that in unfavorable cases, namely the suppression of external noise of the satellite navigation system, an increase in the errors of the inertial navigation system (INS), including through the use of accelerometers and gyroscopes manufactured using MEMS technology, the presence of bad weather conditions, which complicates the work of the visual navigation system. In order to ensure the operation of the navigation complex, it is necessary to ensure the suppression of interference (noise). To improve the accuracy of the INS, which is part of the INC, it is proposed to use the procedure for extracting noise from the raw signal of the INS, its prediction using neural networks and its suppression. To solve this problem, two approaches are proposed, the first of which is based on the use of a multi-row GMDH algorithm and single-layer networks with sigm_piecewise neurons, and the second is on the use of hybrid recurrent neural networks, when neural networks were used, which included long-term and short-term memory (LSTM) and Gated Recurrent Units (GRU). Various types of noise, that are inherent in video images in visual navigation systems are considered: Gaussian noise, salt and pepper noise, Poisson noise, fractional noise, blind noise. Particular attention is paid to blind noise. To improve the accuracy of the visual navigation system, it is proposed to use hybrid convolutional neural networks.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

Navigation systems for ships mainly include inertial navigation system (INS), “BeiDou” satellite navigation system, Global Positioning System (GPS), and Doppler navigation system and so on. The characteristics of each system are different. In order to improve the accuracy and fault-tolerant ability of naval-ship navigation system, an integrated navigation algorithm based on Measurement-based adaptive Kalman filtering algorithm (MAKF), improved Suge-Husa algorithm and non-reset federated Kalman filter structure is proposed in this paper. The algorithm uses the short-term high precision of the inertial navigation system and the prior velocity information of the ship to adaptively estimate the measurement noise characteristics of the satellite navigation system. When the satellite navigation system is unable to locate, the INS and the log are combined with the help of the prior velocity information. The simulation results show that the algorithm can effectively exploit the characteristics of each navigation system, and can output high-precision navigation results even when the GPS/BeiDou satellite navigation system fails.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

Inertial navigation system (INS) is utilized in several applications such as missile guidance, space navigation, and marine navigation. An efficient calibration method for improving the inertial navigation system accuracy is presented. As the vital error sources in the inertial navigation system are associated with the deterministic errors of the inertial measurement unit (IMU), the proposed technique precisely determines the calibration parameters to reduce these errors, especially the gyro’s scale factor, and non-orthogonality error. In recently proposed calibration methods, the scale factor is determined by the output/input relationship linear fitting. Although the determined scale factor meets the requirement of various navigation systems to some extent it doesn’t fit the high accurate ones, such as guided missiles and marines. That’s because the gyro damping effect is changed with different input angular rates which causes the gyro scale factor varying. The presented calibration method tackles this phenomenon by assigning different weights to each input rate through a weighted linear regression fit. Moreover, the gyro nonorthogonal error which comes from the imperfection gyro mounting is merely determined by the lateral coupling signal. But the fact is that the lateral coupling signal is not induced from the gyro non-orthogonal error only but also comprises the gyro signal which is directly proportional to the centripetal acceleration caused by the applied angular rates. Even though this signal is tiny but it will be accumulated for the long-time navigation system and degrades its accuracy. The presented calibration method utilizes a lateral accelerometer to realize that signal and tear out it to accurately obtain the nonorthogonal error. Finally, a laboratory test for the proposed method was carried out to ensure its effectiveness. Where the actual applied rates are determined twice, once with the built error model by the presented calibration method and the other by the traditional one.

A Fuzzy Kalman Filter Optimized Using a Genetic Algorithm for Accurate Navigation of an Autonomous Underwater Vehicle

Hybrid noise removal to improve the accuracy of inertial sensors using lifting wavelet transform optimized by genetic algorithm