A Method for INS / DVL Integrated Navigation System Based on Marine Pipeline Node Position

The tightly coupled GPS/SINS integrated navigation system is studied in this paper. In order to meet the requirement of precision and stability, a nonlinear error model of SINS based on additive quaternion is established and a kind of SRUKF(Square-Root Unscented Kalman Filter) algorithm is designed. Compared whith EKF, The SRUKF not only improves the location precision greatly but also avoids the calculating burden of Jacobin matrices. Simulation results show the feasibility and effectiveness of the proposed algorithm in tightly coupled GPS/SINS integrated navigation system.

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

This paper investigates the application, design, and implementation of the Square Root Unscented Kalman Filter (SRUKF) for induction motor (IM) sensorless drives. The UKF uses nonlinear unscented transforms (UT) in the prediction step in order to preserve the stochastic characteristics of a nonlinear system. The advantage of using the UT is its ability to capture the nonlinear behavior of the system, unlike the extended Kalman filter (EKF) that uses linearized models. The SRUKF implements the UKF using square root filtering and has the potential of reducing the errors in numerical computation. We discuss the theoretical aspects and implementation details of the SRUKF for IM drives. Experimental results for a direct torque controlled drive are presented for a wide speed range of operation, with focus on low speed performance. A comparison with the conventional EKF is included. It is concluded that the SRUKF is a viable and powerful tool for IM state estimation.

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.

For the long-range voyage and light Autonomous Underwater Vehicle (AUV), the high-precision navigation and positioning is the key point in the completion of the task. Therefore, this paper designs an underwater integrated navigation system, including the strapdown inertial navigation system (SINS), Doppler velocity loci (DVL), magnetometers and depth sensor. Based on the principle of each navigation sensor and the corresponding error model, the mathematic model of each sub-filter of the integrated navigation system is firstly established. Secondly, combining the combination filter and federated filtering theory, the integrated navigation system based on federated filtering is designed. Finally, to eliminate the influence of unknown or time-varying statistical characteristics on the Kalman filter (KF), a new federated filtering algorithm based on Sage-Husa adaptive KF is proposed to estimate each local filter. And the corresponding simulation results will show the performance of the research.

Aiming at the situation that the underwater integrated navigation system is easy to be disturbed and the output is unavailable during the task of underwater vehicle, a RVBAKF/ LSTM hybrid method is proposed in this paper. Considering the actual underwater working environment of autonomous underwater vehicles (AUV), the measured value of Doppler velocity log (DVL) is prone to outliers or even interruption, so it is very important to improve the fault tolerance of integrated navigation system. For the processing of outliers, a robust adaptive filter based on VB theory is proposed. Its advantage is to realize the adaptation of time-varying noise and reduce the interference of outliers to the integrated navigation system. Firstly, selecting the SINS output related to AUV velocity, such as velocity, attitude, angular velocity and specific force as the training input when the DVL output is normal. When DVL output is interrupted, the long short-term memory (LSTM) model is used to provide pseudo measurement values for integrated navigation system. The effectiveness of the method is verified by the on-board experimental data. When the DVL output is interrupted, the method can effectively improve the accuracy of the integrated navigation system. The method in this paper is compared with four different hybrid methods, and the results are obviously better than the four methods.

The Internet of Things (IoT), which is usually established over architectures of wireless sensor networks, provides an actual platform for various applications of personal and ubiquitous computing. Recently, moving target localization and tracking in an IoT environment have been paid more and more attention. This paper proposes a square-root unscented Kalman filtering (SR-UKF)-based algorithm to discover real-time location of a moving target in an IoT environment where there exist quantities of sensors. The data generated from wireless sensor nodes of the IoT make contributions to localization and tracking of the moving target. First, a least-square (LS) criterion-based mathematical model is proposed for localization initialization in an IoT scenario. Next, we employ an SR-UKF idea for the further localization and tracking. By using the data coming from sensor nodes near the target, real-time location of the moving target can be estimated by implementation of SR-UKF in an iterative fashion so as to achieve target status tracking. Simulation results show that the proposed algorithm achieves good performance in estimation of both position and velocity of the target with either uniform linear motion or variable-speed curve motion. Compared with some existing conventional extended Kalman filtering (EKF) or UKF-based methods, the proposed algorithm shows lower location/velocity estimation error under the same computational complexity, which demonstrates its potential significance in ubiquitous computing applications for an IoT environment.

Aiming at solving the problem that the accuracy and stability of Square-root Unscented Kalman Filter(SRUKF) will be affected if there are outliers in the observation values, this paper proposes an improved SRUKF restraining outliers based on orthogonality of innovation in the filtering processing. This modified filter at first detects whether there are outliers in the observation values by judging if the orthogonality of innovation is lost or not, and then assigns an activation function as the weight to each observation value, which can keep the orthogonal properties of the innovation sequence and the outliers can be detected and corrected. The Matlab simulation results about the GPS/DR integrated navigation system show that this modified filter is effectively resistant to outliers in the observation values and improves the filtering accuracy and stability.

Abstract：On the basis of the basic principles of weighted fusion, Kalman filtering and BP neural networks, the basic principles of information fusion methods used in integrated navigation systems are expounded. Through the analysis of the basic principles, the association of information fusion methods commonly used in integrated navigation systems and information failure modes is obtained: the information fault mode of weighted fusion method The model is closely related to the specific weight allocation method, which depends on the fault mode of the sensor or sub-system in which the weight is dominant; the information fault mode of the Kalman filtering information fusion method is a continuous mutation fault corresponding to the nonlinear time interval of the system; the information fault mode of the BP neural network method is gradual with time. The information failure mode of the BP neural network method is a slowly varying fault that gradually accumulates over time. Starting from the complexity associated with the information fusion method and the information failure mode, it is pointed out that in order to systematically express the relationship between the information fusion method and the information failure mode, further research can be carried out.

The conventional Kalman filter assumes that the system model and the observation model is linearity. Also the noise in system and observation model is white noise. However the practical environment is very complex. When we use Kalman filter to estimate, we can not got the transcendent information. The paper considers the characteristic of aircraft, research the structure and scheme of INS/CNS/GPS integrated navigation system. The error model of integrated navigation system has been set up. The status of navigation system has been estimated with Kalman filter, and the simulation calculation has been progressed. The paper presents a method, which use neural network ensembles to deal with the Kalman filter. This method improves the generalization and stabilization ability. The simulation shows that the arithmetic has high precision.

When designing the Kalman filter of integrated navigation system, the point of pinnacle importance focuses on the systemic state equation and measurement equation. Aiming at the relativity and poor observability among measurement information in loosely coupled integrated navigation system, the tightly coupled integrated navigation system is constructed with the information fusion technology based on Kalman filter. In this mode, pseudo-range and pseudo-range rate are used as measurement information for their better observability and independence, to improve the accuracy of Kalman filter. The experiments are carried out based on semi-physical, using the actual datum which are received by Global Position System (GPS) software receiver. The results show clearly that when the accuracy of inertial components declines, the tightly coupled integration can provide higher position accuracy than the loosely coupled integration.

Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (eg, learning the weights of a neural network). The EKF applies the standard linear Kalman filter methodology to a linearization of the true nonlinear system. This approach is sub-optimal, and can easily lead to divergence. Julier et al. (1997), proposed the unscented Kalman filter (UKF) as a derivative-free alternative to the extended Kalman filter in the framework of state estimation. This was extended to parameter estimation by Wan and Van der Merwe et al., (2000). The UKF consistently outperforms the EKF in terms of prediction and estimation error, at an equal computational complexity of (OL/sup 3/)/sup l/ for general state-space problems. When the EKF is applied to parameter estimation, the special form of the state-space equations allows for an O(L/sup 2/) implementation. This paper introduces the square-root unscented Kalman filter (SR-UKF) which is also O(L/sup 3/) for general state estimation and O(L/sup 2/) for parameter estimation (note the original formulation of the UKF for parameter-estimation was O(L/sup 3/)). In addition, the square-root forms have the added benefit of numerical stability and guaranteed positive semi-definiteness of the state covariances.

As environmental regulations become stricter, the advantages of pure electric vehicles over fuel vehicles are becoming more and more significant. Due to the uncertainty of the actual operating conditions of the vehicle, accurate estimation of the state-of-charge (SOC) of the power battery under multi-temperature scenarios plays an important role in guaranteeing the safety, economy, and reliability of electric vehicles. In this paper, a SOC estimation method based on the fusion of convolutional neural network-transformer (CNN-Transformer) and square root unscented Kalman filter (SRUKF) for lithium-ion batteries in low-temperature scenarios is proposed. First, the CNN-Transformer base model is established. Then, the SRUKF algorithm is used to update the state of the Coulomb counting method results based on the base model results. Finally, ensemble learning theory is applied to estimate SOC in multi-temperature scenarios. Data is obtained from laboratory conditions at −20 °C, −7 °C, and 0 °C. The experimental results show that the SOC estimation method proposed in this study is stable in terms of the root mean square error (RMSE) being between 2.69% and 4.22%. The proposed base model is also compared with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network to demonstrate its relative advantages.

In Micro/Nano-satellite based active debris removal (ADR), the most promoted relative navigation method is angles-only navigation (AON), the relative state estimations are computed from a low-volume/mass, power saving, and low-cost optical/infrared camera's observations, but the uncooperative rendezvous faces drawback of range-observability problem. Based on the drawbacks of the extended Kalman filter (EKF), a more accurate on-board square root unscented Kalman filter (SRUKF) AON scheme is developed, along with a closed-loop linear covariance analysis model. By balancing fuel consumption, control accuracy, and trajectory robustness, we developed a smarter time and covariance threshold triggered optimal maneuver planning method and showed how these results correlate to past methods and missions, and how they could potentially influence future ones. Numerical simulation proved that it is feasible to control the two-line element (TLE) level of an uncertain initial relative state to a 100-m v-bar relative station keeping point, under a 30% data gap per orbital period on account of the eclipse.

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.