A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

INS-GNSS Integrated Navigation Algorithm Based on TransGAN

With the rapid development of autopilot technology,a variety of engi-neering applications require higher and higher requirements for navigation and positioning accuracy,as well as the error range should reach centimeter level.Single navigation systems such as the inertial navigation system(INS)and the global navigation satellite system(GNSS)cannot meet the navigation require-ments in many cases of high mobility and complex environments.For the purpose of improving the accuracy of INS-GNSS integrated navigation system,an INS-GNSS integrated navigation algorithm based on TransGAN is proposed.First of all,the GNSS data in the actual test process is applied to establish the data set.Secondly,the generator and discriminator are constructed.Borrowing the model structure of generator transformer,the generator is constructed by multi-layer transformer encoder,which can obtain a wider data perception ability.The generator and discriminator are trained and optimized by the production countermeasure network,so as to realize the speed and position error compensa-tion of INS.Consequently,when GNSS works normally,TransGAN is trained into a high-precision prediction model using INS-GNSS data.The trained Trans-GAN model is emoloyed to compensate the speed and position errors for INS.Through the test analysis offlight test data,the test results are compared with the performance of traditional multi-layer perceptron(MLP)and fuzzy wavelet neural network(WNN),demonstrating that TransGAN can effectively correct the speed and position information when GNSS is interrupted,with the high accuracy.

SINS/CNS/GPS integrated navigation algorithm based on UKF

A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

Robust adaptive UKF based on SVR for inertial based integrated navigation

Aiming at the problem that the traditional Unscented Kalman Filtering(UKF) algorithm can't solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers,this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression(SVR).The algorithm combines the advantages of support vector regression with small samples,nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation.Firstly,the SVR model is trained by using the innovation in the sliding window,and the new innovation is monitored.If the deviation between the estimated innovation and the measured innovation exceeds a given threshold,then measured innovation will be replaced by the predicted innovation,and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm.Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF,robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.

Intelligent fault-tolerant algorithm with two-stage and feedback for integrated navigation federated filtering

In order to take full advantage of federated filter in fault-tolerant design of integrated navigation system, the limitation of fault detection algorithm for gradual changing fault detection and the poor fault tolerance of global optimal fusion algorithm are the key problems to deal with. Based on theoretical analysis of the influencing factors of federated filtering fault tolerance, global fault-tolerant fusion algorithm and information sharing algorithm are proposed based on fuzzy assessment. It achieves intelligent fault-tolerant structure with two-stage and feedback, including real-time fault detection in sub-filters, and fault-tolerant fusion and information sharing in main filter. The simulation results demonstrate that the algorithm can effectively improve fault-tolerant ability and ensure relatively high positioning precision of integrated navigation system when a subsystem having gradual changing fault.

Practical integrated navigation fault detection algorithm based on sequential hypothesis testing

In detecting system fault algorithms,the false alarm rate and undectect rate generated by residual Chi-square test can affect the stability of filters.The paper proposes a fault detection algorithm based on sequential residual Chi-square test and applies to fault detection of an integrated navigation system.The simulation result shows that the algorithm can accurately detect the fault information of global positioning system（GPS）,eliminate the influence of false alarm and missed detection on filter,and enhance fault tolerance of integrated navigation systems.

Kalman filter applied in underwater integrated navigation system

For the underwater integrated navigation system, information fusion is an important technology. This paper introduces the Kalman filter as the most useful information fusion technology, and then gives a summary of the Kalman filter applied in underwater integrated navigation system at present, and points out the further research directions in this field.

Multi-Scale Fusion Algorithm for AUVs Integrated Navigation Systems

To deal with the low location accuracy issue of existing underwater navigation technologies in autonomous underwater vehicles(AUVs),a distributed fusion algorithm which combines the model's analysis method with a multi-scale transformation method is proposed for integrated navigation system based on AUV.First,integrated navigation system theory and system error sources are introduced in details.Secondly,a navigation system's observation equation on the original scale is decomposed into different scales by the discrete wavelet transform method,and noise reduction is performed by setting the wavelet de-noising threshold.At last,the dynamic equation and observation equations are fused on different scales by the wavelet transformation and Kalman filter.The results show that the proposed algorithm has smaller navigation error and higher navigation accuracy.

Unequal-interval data fusion algorithm for inertial/gravity matching integrated navigation system

Inertial/gravity matching integrated navigation system can effectively improve the longendurance navigation ability of underwater vehicles.Through the analysis of the matching process,the problem of unequal-interval in matching trajectory is addressed by an unequal-interval data fusion algorithm which is based on the unequal-interval characteristics analysis of the matching trajectory.Compared with previously available methods,the proposed algorithm improves the location precision.In conclusion,simulations of the integrated navigation system demonstrated the effectiveness and superiority of the proposed algorithm.

Progress and Achievements of Multi-sensor Fusion Navigation in China during 2019—2023

Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.

Indoor Navigation Network Model Construction Method Based on Building Information Model

In view of the poor information integrity of the 3D model used to make the indoor road network and the lack of versatility of the constructed indoor road network, a method for building an indoor navigation network model that can be seamlessly connected with outdoor paths is proposed in this paper. First, the IFC model is converted to the CityGML model using the BIM model as the indoor data source. Then, using GIS technology and limited Delaunay triangulation refinement algorithm, the necessary elements of indoor navigate on network model such as semantic information, geometric information and topological relationship contained in CityGML model are extracted. Finally, it is visualized and verified based on experimental model data. The results show that the indoor navigation network model constructed based on the CityGML model can accurately perform indoor navigation, make the constructed road network more general, and provide reference and technical support for the integrated construction of indoor and outdoor road network models.

High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

An efficiency algorithm on Gaussian mixture UKF for BDS/INS navigation system

To further improve the performance of UKF(Unscented Kalman Filter) algorithm used in BDS/SINS(BeiDou Navigation Satellite System/Strap down Inertial Navigation System), an improved GM-UKF(Gaussian Mixture Unscented Kalman Filter) considering non-Gaussian distribution is discussed in this paper. This new algorithm using SVD(Singular Value Decomposition) is proposed to alternative covariance square root calculation in UKF sigma point production. And to end the rapidly increasing number of Gaussian distributions, PDF(Probability Density Function) re-approximation is conducted. In principle this efficiency algorithm proposed here can achieve higher computational speed compared with traditional GM-UKF. And simulation experiment result show that, compared with UKF and GM-UKF algorithm, new algorithm implemented in BDS/SINS tightly integrated navigation system is suitable for handling nonlinear/non-Gaussian integrated navigation position calculation, for its lower computational complexity with high accuracy.

Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS

High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system(SINS/GNSS)integrated navigation system,and its estimation plays an important role in the performance evaluation of the navigation system.Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution,without considering the influence of the pollution introduced by the GNSS signal,which is susceptible to external interference.To address this problem,a high-precision filter estimation method using Gaussian process regression(GPR)is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator(USQUE)to improve the navigation accuracy.Based on the advantage of the GPR machine learning function,the estimation performance of the sliding window for model training is measured.This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter.The combination of GPR and the USQUE algorithm establishes a robust mechanism framework,which enhances the robustness and stability of traditional methods.The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy,which demonstrates the effectiveness of the proposed method.

Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.

A Novel SINS/IUSBL Integration Navigation Strategy for Underwater Vehicles

This paper presents a novel SINS/IUSBL integration navigation strategy for underwater vehicles.Based on the principle of inverted USBL(IUSBL),a SINS/IUSBL integration navigation system is established,where the USBL device and the SINS are both rigidly mounted onboard the underwater vehicle,and fully developed in-house,the integration navigation system will be able to provide the absolute position of the underwater vehicle with a transponder deployed at a known position beforehand.Furthermore,the state error equation and the measurement equation of SINS/IUSBL integration navigation system are derived,the difference between the position calculated by SINS and the absolute position obtained by IUSBL positioning technology is used as the measurement information.The observability of the integration system is analyzed based on the singular value decomposition(SVD)method.Finally,a mathematical simulation is performed to demonstrate the effectiveness of the proposed SINS/IUSBL integration approach,and the observable degrees of the state variables are also analyzed.

INTEGRATED GPS/INS OF PSEUDO-RANGE DELTA-RANGE SEM I-PHYSICAL SIMULATION SRESEARCH

In orderto furtherstudy theperform ance oftightly integrated navigation system ofGPS/ INS, a sem i-physicalsim ulation oftightly coupled system has been done based on the data gathered from the experim entof integrated system ofGPSand INS. The closed-loop Kalm an Filter and U-D discom pose algorithm have been used. The sim ulation results associated to four integrated m odels of pseudo-range, delta-range, pseudo-range and delta-range alternation, and pseudo-range and delta- range synthesis have been provided, and the actualeffects of variously integrated m odels have been analyzed. The results show that the pseudo-range and delta-range synthesis coupled m odelis the m osteffective to im provethe coupled system perform anceand the individualdelta-rangecoupled m od- elhad betterbe avoided in application.

Tightly coupled INS/CNS/spectral redshift integrated navigation system with the aid of redshift error measurement

The integration of an inertial navigation system(INS) and a celestial navigation system(CNS) has the superiority of high autonomy. However, its reliability and accuracy are permanently impaired under poor observation conditions. To address this issue, the present paper proposes a tightly coupled INS/CNS/spectral redshift(SRS) integration framework based on the spectral redshift error measurement. In the proposed method, a spectral redshift error measurement equation is investigated and embedded in the traditional tightly coupled INS/CNS integrated navigation system to achieve better anti-interference under complicated circumstances. Subsequently, the inaccurate redshift estimation from the low signal-to-noise ratio spectrum is considered in the integrated system, and an improved chi-square test-based covariance estimation method is incorporated in the federated Kalman filter, allowing to deal with measurement outliers caused by the inaccurate redshift estimation but not influencing the effect of other correct redshift measurements in suppressing the error of the navigation parameter on the filtering solution. Simulations and comprehensive analyses demonstrate that the proposed tightly coupled INS/CNS/SRS integrated navigation system can effectively handle outliers and outages under hostile observation conditions, resulting in improved performance.

A novel Bayesian-based INS/GNSS integrated positioning method with both adaptability and robustness in urban environments

Achieving higher accuracy positioning results in urban environments at a lower cost has been an important pursuit in areas such as autonomous driving and intelligent transportation.Lowcost Inertial Navigation System and Global Navigation Satellite System(INS/GNSS)integrated navigation systems have been an important means of fulfilling the above quest due to the complementary error characteristics between INS and GNSS.The complex urban driving environment requires the system sufficiently adaptive to keep up with the time-varying measurement noise and sufficiently robust to cope with measurement outliers and prior uncertainties.However,many efforts lack a balance between adaptability and robustness.In this paper,a novel positioning method with both adaptability and robustness is proposed by coupling the Mahalanobis distance method,the Variational Bayesian method and the student’s t-distribution in one process(M-VBt method).This method is robust against non-Gaussian noise and priori uncertainties,plus adaptive against measurement noise uncertainty and time-varying noise.The field test results show that the M-VBt method(especially the Mahalanobis distance part)has significantly improved the system performance in the complex urban driving environment.