Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach.

The integration of micro-electro-mechanical system-inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system's performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss-Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading's angular increment, and to fit the integrated navigation system's dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

Neural Inference Search for Multiloss Segmentation Models.

Semantic segmentation is vital for many emerging surveillance applications, but current models cannot be relied upon to meet the required tolerance, particularly in complex tasks that involve multiple classes and varied environments. To improve performance, we propose a novel algorithm, neural inference search (NIS), for hyperparameter optimization pertaining to established deep learning segmentation models in conjunction with a new multiloss function. It incorporates three novel search behaviors, i.e., Maximized Standard Deviation Velocity Prediction, Local Best Velocity Prediction, and n -dimensional Whirlpool Search. The first two behaviors are exploratory, leveraging long short-term memory (LSTM)-convolutional neural network (CNN)-based velocity predictions, while the third employs n -dimensional matrix rotation for local exploitation. A scheduling mechanism is also introduced in NIS to manage the contributions of these three novel search behaviors in stages. NIS optimizes learning and multiloss parameters simultaneously. Compared with state-of-the-art segmentation methods and those optimized with other well-known search algorithms, NIS-optimized models show significant improvements across multiple performance metrics on five segmentation datasets. NIS also reliably yields better solutions as compared with a variety of search methods for solving numerical benchmark functions.

Integrated navigation of GPS/INS based on fusion of recursive maximum likelihood IMM and Square-root Cubature Kalman filter.

Information fusion of the GPS/INS integrated system is always related to characteristics of the inertial system and the sensor feature, yet prior knowledge is still difficult to obtain in real applications. To deal with the uncertainty of error covariance and state noise in vehicle navigation, this paper presents a novel approach, wherein the integration of Square-root Cubature Kalman Filters (SCKF) and Interacting Multiple Model (IMM) are also introduced. In the framework of IMM, the SCKFs with different covariance are designed to reflect various vehicle dynamics. Besides, since the IMM-SCKF can switch flexibly among the filters, the transition probability matrix is computed with maximum likelihood method to adapt to different noise characteristics. The performance of the proposed algorithm is guaranteed by theoretical analyses, and a series of vehicular experiments with different maneuvers are carried out in an urban environment. The results indicate that, in comparison with the CKF and the IMM-CKF, the accuracy of velocity and attitude are increased by the proposed strategy.

Attitude determination method integrating square-root cubature Kalman filter with expectation-maximization for inertial navigation system applied to underwater glider.

The uncertainty, complexity, and variability of the marine environment inevitably lead to a change in the measurement error resulting in erroneous estimation of navigation information. To solve this problem, this paper proposes a novel method integrating the square-root cubature Kalman filter (SCKF) with the expectation-maximization (EM) algorithm. The proposed new SCKF (NSCKF) algorithm makes better use of the advantages of SCKF and the EM online algorithm. The performance of NSCKF is verified theoretically and evaluated by experiments. The results indicate that the proposed NSCKF algorithm can better estimate predicted error covariance and measurement noise than two other comparison methods owing to the online EM method so that the more accurate attitude estimation can be obtained by the NSCKF algorithm although the measurement error has a great variation. Moreover, the accuracy and efficiency can be guaranteed by employing the SCKF. Experimental results demonstrate that the NSCKF can provide a more stable attitude estimation in different cases of measurement errors. Therefore, the NSCKF is more suitable to be used in underwater navigation than other comparison methods because of higher accuracy, more efficiency, and better robustness.

High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold.

High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) and wavelet threshold is proposed. Firstly, EMD decomposition is performed on the output of the main accelerometer to obtain the intrinsic mode function (IMF). Then, the continuous mean square error rule is used to find energy cut-off point, and then the corresponding high frequency IMF component is denoised by wavelet threshold. Finally, the processed high-frequency IMF component is superposed with the low-frequency IMF component, and the reconstructed signal is denoised signal. Experimental results show that this method integrates the advantages of EMD and wavelet threshold and can retain useful signals to the maximum extent. The impact peak and vibration characteristics are 0.003% and 0.135% of the original signal, respectively, and it reduces the noise of the original signal by 96%.

Combination of iterated cubature Kalman filter and neural networks for GPS/INS during GPS outages.

To improve the performance of the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation system, current research studies merely combine neural networks with nonlinear filter methods. Few studies focus on how to optimize the parameters of the neural network and how to further improve the small error accumulated into the next filter step due to the imprecise design of the filter when setting the initial parameters in the GPS/INS integrated system. In this article, a dual optimization method consisting of an iterated cubature Kalman filter-Feedforward Neural Network (ICKF-FNN) and a radial basis function-cubature Kalman filter (RBF-CKF) is proposed to compensate the position and velocity errors of the integrated system during GPS outages. The prominent advantages of the proposed method include the following. (i) The ICKF is designed to optimize the parameters of the introduced FNN adaptively and obtain an appropriate internal structure when GPS is available, which improves the accuracy of the training model. (ii) The RBF establishes the relationship between filter parameters and the optimal estimation errors, reducing the errors caused by inaccurate predicted observation during GPS outages. (iii) The proposed dual optimization method takes advantages over other combination algorithms under different moving conditions or even during long period of GPS outages, which shows its great stability. Experimental results show that the root mean squared error of the east position is reduced by 85.79% to 3.2187 m using the proposed strategy during turning movement and the east velocity error accumulation rate decreases by 92.69% during the long straight movement of 250 s. These results are from offline processing.

Robust cubature Kalman filter for GNSS/INS with missing observations and colored measurement noise.

In order to improve the accuracy of GNSS/INS working in GNSS-denied environment, a robust cubature Kalman filter (RCKF) is developed by considering colored measurement noise and missing observations. First, an improved cubature Kalman filter (CKF) is derived by considering colored measurement noise, where the time-differencing approach is applied to yield new observations. Then, after analyzing the disadvantages of existing methods, the measurement augment in processing colored noise is translated into processing the uncertainties of CKF, and new sigma point update framework is utilized to account for the bounded model uncertainties. By reusing the diffused sigma points and approximation residual in the prediction stage of CKF, the RCKF is developed and its error performance is analyzed theoretically. Results of numerical experiment and field test reveal that RCKF is more robust than CKF and extended Kalman filter (EKF), and compared with EKF, the heading error of land vehicle is reduced by about 72.4%.

High accuracy navigation information estimation for inertial system using the multi-model EKF fusing adams explicit formula applied to underwater gliders.

The underwater navigation system, mainly consisting of MEMS inertial sensors, is a key technology for the wide application of underwater gliders and plays an important role in achieving high accuracy navigation and positioning for a long time of period. However, the navigation errors will accumulate over time because of the inherent errors of inertial sensors, especially for MEMS grade IMU (Inertial Measurement Unit) generally used in gliders. The dead reckoning module is added to compensate the errors. In the complicated underwater environment, the performance of MEMS sensors is degraded sharply and the errors will become much larger. It is difficult to establish the accurate and fixed error model for the inertial sensor. Therefore, it is very hard to improve the accuracy of navigation information calculated by sensors. In order to solve the problem mentioned, the more suitable filter which integrates the multi-model method with an EKF approach can be designed according to different error models to give the optimal estimation for the state. The key parameters of error models can be used to determine the corresponding filter. The Adams explicit formula which has an advantage of high precision prediction is simultaneously fused into the above filter to achieve the much more improvement in attitudes estimation accuracy. The proposed algorithm has been proved through theory analyses and has been tested by both vehicle experiments and lake trials. Results show that the proposed method has better accuracy and effectiveness in terms of attitudes estimation compared with other methods mentioned in the paper for inertial navigation applied to underwater gliders.

Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS.

In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton-Raphson algorithm and online noise estimator. Then the effect of state-dependent noise coming from iterated update is analyzed theoretically, and an augmented form of CKF algorithm is applied to improve the estimation accuracy. The performance of IICKF is verified by field test and numerical simulation, and results reveal that, compared with non-iterated filter, iterated filter is less sensitive to the system uncertainty, and IICKF improves the accuracy of yaw, roll and pitch by 48.9%, 73.1% and 83.3%, respectively, compared with traditional iterated KF.

Study of the algorithm of backtracking decoupling and adaptive extended Kalman filter based on the quaternion expanded to the state variable for underwater glider navigation.

High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.