Data Preprocessing and Kalman Filter Performance Improvement Method in Integrated Navigation Algorithm

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.

A Kalman filter algorithm based on exact modeling for FOG GPS/SINS integration

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.

In this paper, we present a high-precision Mars entry integrated navigation algorithm under large uncertainties via a desensitised extended Kalman filter (DEKF). Firstly, a new six degree-of-freedom Mars entry dynamics model is derived based on the angular velocity outputs of a gyro, which is free of modelling errors in the aerodynamic and control torques. Secondly, both the accelerometer outputs and radio measurements between orbiters and entry vehicle are used as the observations embedded in a navigation filter to perform state estimation and suppress the measurement noise. Finally, a desensitised extended Kalman filter, exhibiting the desirable property of efficiently reducing the sensitivity of state variables with respect to model and parameter uncertainties, is adopted in order to overcome the adverse effects of initial state errors and uncertainties during Mars atmospheric entry and further improve entry navigation accuracy. The numerical simulation results show that the DEKF-based integrated navigation algorithm developed in this paper can achieve a better navigation performance with higher accuracy when compared with the standard extended Kalman filter (EKF)-based integrated navigation algorithm in the presence of larger state errors and parameter uncertainties.

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.

In this paper, four different signal processing algorithms which can be applied to reduce the noise from a MEMS-gyroscope-based computer head mouse are presented. MEMS-gyroscopes are small, light, cheap and widely used in many electrical products. MultiPos, a MEMS-gyroscope-based computer head mouse system was designed for persons with movement disorders. Noise such as physiological tremor and electrical noise is a common problem for the MultiPos system. In this study four different signal processing algorithms were applied and evaluated by simulation in MATLAB and implementation in a dsPIC, with aim to minimize the noise in MultiPos. The algorithms were low-pass filter, Least Mean Square (LMS) algorithm, Kalman filter and Weighted Fourier Linear Combiner (WFLC) algorithm. Comparisons and system tests show that these signal processing algorithms can be used to improve the MultiPos system. The WFLC algorithm was found the best method for noise reduction in the application of a MEMS-gyroscope-based head mouse.

Aiming at the problem of low positioning accuracy caused by unknown or inaccurate noise statistics when the underwater robot inspects submarine pipelines, the marine pipeline nodes' position are used to improve the navigation and positioning accuracy of the underwater INS/DVL integrated navigation system in the harsh underwater environment. The underwater integrated navigation system adopts an Adaptive Square-Root Unsented Kalman Filtering approach (ASUKF) approach based on the Square Root Unscented Kalman Filtering (SRUKF) and the improved Sage-Husa algorithm to resist errors caused by inaccurate noise such as ocean flow. Simulation experiments show that this method can improve the reliability and accuracy of the integrated navigation system compared with the traditional Square Root Unscented Kalman Filter algorithm.

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.

Aiming at the integrated navigation system with model errors, the model errors is assumed as a process noise for Gaussian white noise to process in Kalman filtering, thus causing larger state estimation error of nonlinear filtering system and even divergent. This paper presents a nonlinear model predictive particle filtering method considers the model error of real-time estimation, and then corrects the nonlinear and non-Gaussian system model with model error. Compared and simulated the new method with the predictive filtering and the particle filtering in Strap-down Inertial Navigation System/Synthetic Aperture Radar integrated navigation system. Experiment and comparison results demonstrate that the proposed method can improve the filter performance and integrated navigation calculation accuracy significantly.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

In this paper, Kalman filter is designed and implemented on a heat deformation tester (HDT) to estimate the LVDT sensor information from a noisy measured data sequence. In simulation and test, the output response of Kalman filter can separate noise from reference signal and the outcome is closely matched with reference signal. The response of low-pass IIR filter is smooth and can also separate noise but some time delay is presented. The mean squared error (MSE) of low-pass IIR filter is 0.6354 while the MSE of Kalman filter reduces to 0.1467, a significant improvement by 4 times. Also, the computational efficiency of Kalman filter is better than that of low-pass IIR filter in this application. Both filtering algorithms are implemented and programmed into a heat deformation tester (HDT). The performance is impressive.

In this study, the integrated navigation system, consisting of radio and inertial navigation system (INS) altimeters, is presented. INS and the radio altimeter have different benefits and drawbacks. The integration is achieved by using an indirect Kalman filter. Hereby, the error models of the navigators are used by the Kalman filter to estimate vertical channel parameters of the navigation system. In the open loop system, INS is the main source of information, and radio altimeter provides discrete aiding data to support the estimations. At the next step of the study, in case of abrupt faults, the performance of the integrated system is examined. The optimal Kalman filter reacts with abnormal estimates to this situation as expected. To recover such a possible malfunctioning, the robust Kalman filter algorithm is suggested.

High-precision, limited-beacon-aided AUV localization algorithm

In the underwater domain where Autonomous Underwater Vehicles (AUVs) operate, measurements may suffer from the impact of outliers and non-Gaussian noise. These factors can potentially undermine the efficacy of integrated navigation algorithms. The Maximum Correntropy Criterion (MCC) can be utilized to enhance the robustness of AUV integrated navigation algorithms through the construction and maximization of the correntropy function. Notwithstanding, the underwater environment occasionally presents unknown time-varying noise, a situation for which the MCC lacks adaptability. In response to this issue, our study introduces a novel integrated navigation algorithm that synergizes the MCC and the Variational Bayesian approach, thereby augmenting both the robustness and adaptability of the system. Initially, we implement the MCC along with a mixture kernel function in an Unscented Kalman Filter (UKF) to strengthen the robustness of the AUV integrated navigation algorithms amidst the complexities inherent to underwater environmental conditions. Additionally, we utilize the Variational Bayesian method to refine the approximation of measurement noise covariance, thereby boosting the algorithm’s adaptability to fluctuating scenarios. We evaluate the performance of our proposed algorithm using both simulation and sea trial datasets. The experimental results reveal a significant enhancement in the Root Mean Square Error (RMSE) and navigation accuracy of our proposed algorithm. Notably, in a complex noise environment, our algorithm achieves, approximately, a 50% improvement in navigation accuracy over other established algorithms.

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.