Algorithm for detecting and isolating failures of the linear Kalman filter

2020 ◽  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Efficiency ◽  
Detection Accuracy ◽  
Navigation Systems ◽  
Chi Square ◽  
Satellite Navigation System ◽  
Aircraft Navigation ◽  
Algorithmic Solution ◽  
Chi Square Test

The aircraft navigation complex consisting of the inertial (ANC) and satellite (SNS) navigation systems is considered. An algorithm for the detection accuracy and stability increasing at the failures of the aircraft navigation complex in the mode of joint operation of the ANS and SNS, as well as in the absence of a signal from the SNS is proposed. In the linear Kalman filter the Chi-square test is used. The results of mathematical modeling have shown the high efficiency of the algorithmic solution. Keywords aircraft; inertial navigation system; satellite navigation system; linear Kalman filter; fault tolerance; Chi-square test

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

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5909
Author(s):  
Guangle Gao ◽  
Shesheng Gao ◽  
Genyuan Hong ◽  
Xu Peng ◽  
Tian Yu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Reliability ◽  
Statistical Characteristics ◽  
Integrated Navigation ◽  
Chi Square ◽  
Integrated Navigation System ◽  
Robust Kalman Filter ◽  
Chi Square Test ◽  
Spectral Redshift

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.

A Review of Multisensor Fusion Methodologies for Aircraft Navigation Systems

2005 ◽  
Vol 58 (3) ◽  
pp. 405-417 ◽  
Author(s):  
David J. Allerton ◽  
Huamin Jia
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Navigation System ◽  
Review Paper ◽  
Fault Tolerant ◽  
Navigation Systems ◽  
System Architectures ◽  
Advantages And Disadvantages ◽  
Aircraft Navigation ◽  
Fusion Methods

This paper reviews currently existing fault-tolerant navigation system architectures and data fusion methods used in the design and development of integrated aircraft navigation systems and also compares their advantages and disadvantages. Four fault-tolerant navigation system architectures are reviewed and the associated Kalman filter architectures and algorithms are discussed. These techniques have been used in most integrated aircraft navigation systems. The aim of this review paper is to provide a guide for navigation system designers to develop future aircraft multisensor navigation systems.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals Design of Accurate Navigation System by Integrating INS and GPS modules

2019 ◽  
Vol 11 (4) ◽  
pp. 139-154
Author(s):  
M. RAJA ◽  
Gaurav ASTHANA ◽  
Ajay SINGH ◽  
Ashna SINGHAL ◽  
Pallavi LAKRA
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
External Information ◽  
Navigation Systems ◽  
Final Solution ◽  
Long Term Stability ◽  
Noise Characteristics ◽  
Aircraft Navigation ◽  
Satellite Signal

Navigation has a huge application in aviation and aircraft automatic approach. Two widely used navigation systems are Global position System (GPS) and Inertial Navigation System (INS). Triangulation method used to determine the aircrafts location by GPS, speed whereas an INS, with the aid of gyroscope and accelerometer, estimates the location, velocity and alignment of an aircraft. Aircraft navigation is a complex task and using only one of the above navigation systems results in inaccurate and insufficient data. GPS stops working when satellite signal is not received, susceptible to interfere occasionally has high noise content, and has a low bandwidth, INS system requires external information for initialization has long-term drift errors. Certain errors like ionosphere interference, clock error, orbital error, position error, etc. might arise and disrupt the navigation process. In order to outrun the limitations of the above two systems and counter the errors, both INS and GPS can be integrated and used to attain more smooth, accurate and faster aircraft attitude estimates, as they have complementary strengths and limitations. GPS is stable for a long period and can act as an independent navigation system whereas INS is not susceptible to interference and signal losses has high radio bandwidth and works well for short intervals of time. In order to get accurate and precise attitude estimation, calculation of the parameters at different altitude using both systems is done; furthermore the comparison and contrast between the results is performed, measured quantities are transformed between various frames like longitudinal to rolling, calculation and elimination of errors is done producing the final solution. Because of integrated GPS and INS, the navigation system exhibits robustness, higher bandwidth, better noise characteristics, and long-term stability.

Studies of the integrated navigation system correction in the absence of a satellite signal

2021 ◽  
Author(s):  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Operation Mode ◽  
Satellite System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Correction Signal ◽  
Satellite Signal ◽  
Navigation Information

The schemes of navigation systems correction are considered. The operation mode of the aircraft during navigation is analyzed. An adaptive modification of the linear Kalman filter is used to correct the navigation information. An algorithm for predicting a correction signal based on a neural network in the event of a loss of a SNS correction signal is formed. Experimental results show the effectiveness of the algorithm. Keywords aircraft; inertial navigation system; satellite system; Kalman filter; neural networks; genetic algorithm

scholarly journals Robust Data Fusion of UAV Navigation Measurements with Application to the Landing System

2020 ◽  
Vol 12 (23) ◽  
pp. 3849
Author(s):  
Kirill Kolosov ◽  
Alexander Miller ◽  
Boris Miller
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Measurement Errors ◽  
High Sensitivity ◽  
False Alarm Probability ◽  
Navigation Systems ◽  
Fusion Algorithm ◽  
Chi Square ◽  
Filtering Algorithm ◽  
Anomalous Errors

To perform precise approach and landing concerning an aircraft in automatic mode, local airfield-based landing systems are used. For joint processing of measurements of the onboard inertial navigation systems (INS), altimeters and local landing systems, the Kalman filter is usually used. The application of the quadratic criterion in the Kalman filter entails the well-known problem of high sensitivity of the estimate to anomalous measurement errors. During the automatic approach phase, abnormal navigation errors can lead to disaster, so the data fusion algorithm must automatically identify and isolate abnormal measurements. This paper presents a recurrent filtering algorithm that is resistant to anomalous errors in measurements and considers its application in the data fusion problem for landing system measurements with onboard sensor measurements—INS and altimeters. The robustness of the estimate is achieved through the combined use of the least modulus method and the Kalman filter. To detect and isolate failures the chi-square criterion is used. It makes possible the customization of the algorithm in accordance with the requirements for false alarm probability and the alarm missing probability. Testing results of the robust filtering algorithm are given both for synthesized data and for real measurements.

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

scholarly journals Integration of GNSS-IMU for increasing the observation accuracy in Condensed Areas (Infrastructure and Forest Canopies)

2019 ◽  
Vol 94 ◽  
pp. 03015
Author(s):  
Mokhamad Nur Cahyadi ◽  
Irene Rwabudandi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kalman Filters ◽  
Measurement Unit ◽  
Gps Receiver ◽  
Satellite Navigation System ◽  
Loosely Coupled ◽  
Advantages And Disadvantages ◽  
Gps Satellites ◽  
Tightly Coupled

Position determination using satellite navigation system has grown significantly. It provides geospatial with global coverage called GNSS (Global Navigation System Satellite). GNSS satellites consists of GLONASS, GPS (Global positioning system) and Galileo.GPS is the most commonly used system and it is known to its capability to determine 3D position on the surface of the earth. In order to determine the position, a GPS receiver must be able to receive signals from at least four GPS satellites. However, the determination of position in condensed areas such as tunnels, area surrounded by high rise buildings, highly forested and in other closely-knit areas is not achieved because satellite signals cannot reach the receiver in the above-mentioned areas and also others where the signals are reflected before being received by a GPS receiver. In this paper, we present the algorithm to fuse GPS and the inertial measurement unit (IMU) to enable positioning in the above-mentioned Condensed Areas. The standard deviations of the two measurements show that GPS-IMU is better than GPS alone, the standard deviation when satellite outages occurred is - 4.57475 for GPS-IMU measurements and 0.218675 for GPS observations. We presented the results in graphics and it shows that GPS measurements are easily disturbed by external influence such as multipath but GPS-IMU graphic is continuous and robust. The advantages and disadvantages of GPS and INS are complementary and make them work together to enable the accurate measurements in the areas mentioned above. Integration of INS and GNSS can be classified into three types, loosely coupled Kalman filter, tightly coupled Kalman filters and ultratight coupled Kalman filter. In this research we used loosely coupled Kalman filter and tightly coupled Kalman filters to combine GPS and INS in one system.

Sign in / Sign up

Export Citation Format

Share Document