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Accurate spacecraft position and velocity is crucial for autonomous navigation technique for deep space mission. For the deep space spacecraft approaching a target planet, the rapid increase in the gravitational pull of the celestial body results in a swift rise in the error of the spacecraft's state integral, thereby making it challenging to accurately estimate the process noise covariance in real-time, which degrades the autonomous navigation system performance. Thus, state estimation for deep space spacecraft during the approach phase under unknown process noise is an important research topic. The traditional adaptive unscented Kalman filter (AUKF) algorithm, relying on measurement innovation, faces challenges in directly capturing abrupt variations in process noise, potentially leading to navigation system divergence. To address the issue of divergence in spacecraft navigation systems and enhance navigation accuracy, this paper proposes an adaptive unscented Kalman filter based on sequential state difference (AUKF-SSD). Additionally, a state-based chi-square test is employed for real-time detection of abrupt variations in process noise. Based on the dynamics model, the AUKF-SSD method is applied to state estimation of Mars spacecraft. Compared with the traditional unscented Kalman filter, the AUKF based on measurement innovation, and the AUKF based on variational Bayesian, the proposed method can effectively restrain estimation errors and solve the divergence problem, in the case of sudden changes in the system process noise. • A novel adaptive Kalman filter is proposed for deep space navigation. • Employs state-based chi-square test for real-time process noise detection. • The proposed method suppresses system divergence. • The effectiveness of the proposed method are verified by simulation experiments. • Offers a promising solution for accurate deep space navigation. [ABSTRACT FROM AUTHOR]

Abstract This study considers an asynchronous multirate data integration problem in the linear state space model with unknown and time-varying statistical parameters of the measurement noises. To improve performance of the multirate adaptive Kalman filter algorithm, a multi-sensor adaptive Kalman filtering algorithm based on variational Bayesian approximations has been developed in an asynchronous multirate multi-sensor integrated navigation system. The proposed filtering algorithm estimates measurement noise variances of the sensors adaptively and also it is robust to anomalous measurements of sensors and however, multirate adaptive Kalman filter is required to use an appropriate algorithm for outlier rejection to achieve a reliable and optimal estimation of position, velocity, and orientation. A navigation system composed of a strapdown inertial navigation system along with Doppler velocity log, inclinometer and depth meter with different sampling rates is designed to evaluate performance of multirate error state Kalman filter (MESKF) and multirate adaptive error state Kalman filter (MAESKF) algorithms and the proposed algorithm. Results of two experimental tests show that the average relative root mean square error (RMSE) of the position estimated by the proposed filtering algorithm can be decreased approximately 57% and 36% when compared to that of MESKF and MAESKF algorithms, respectively. Highlights • A multisensor adaptive Kalman filtering based on variational Bayesian is developed. • Asynchronous multirate multisensor integrated navigation improves performance of MAKF. • Proposed algorithm estimates measurement noise variances of the sensors adaptively. • Experimental results showed that the proposed algorithm is robust to outliers. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of low-cost sensors for autonomous underwater vehicle (AUV). We propose an intelligent assistance positioning methodology by combining the modified incremental smoothing and mapping (iSAM) and constrained optimally pruned extreme learning machine (OP-ELM) for low-cost sensors, which makes full use of GPS data and produce a variety of correction models. Compared to relinearizing and variable reordering by period batch step in the original iSAM, modified iSAM is implemented variable reordering alone and conducted adaptive relinearization when the value of local Chi-square exceeded a certain threshold. Meanwhile, a novel constrained OP-ELM is presented by mapping the output to the constraint space, which provides full guarantee for generating reliable correction model. When GPS is valid, the constrained OP-ELM is applied to the low-cost sensors to generate correction model. Simultaneous, the correction model of measurement for modified iSAM is also given by this way. Once GPS becomes invalid, the correction models are used to amend the low-cost sensors data and measurement model for getting more accurate location information. Experimental results and analysis show that the proposed method outperforms the traditional algorithm, which RMSE can improve by at most 83.8% than Extended Kalman Filter's (EKF). [ABSTRACT FROM AUTHOR]

Constrained adaptive filtering algorithms have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under the mean square error (MSE) criterion, which is an ideal optimality criterion under Gaussian noises. This assumption however fails to model the behavior of non-Gaussian noises found in practice. Motivated by the robustness and simplicity of maximum correntropy criterion (MCC) for non-Gaussian impulsive noises, this paper proposes a new adaptive filtering algorithm called constrained maximum correntropy criterion (CMCC). Specifically, CMCC incorporates a linear constraint into a MCC filter to solve a constrained optimization problem explicitly. The proposed adaptive filtering algorithm is easy to implement, has low computational complexity, and can significantly outperform those MSE based constrained adaptive algorithms in heavy-tailed impulsive noises. Additionally, the mean square convergence behaviors are studied under energy conservation relation, and a sufficient condition to ensure the mean square convergence and the steady-state mean square deviation (MSD) of the CMCC algorithm are obtained. Simulation results confirm the theoretical predictions under both Gaussian and non-Gaussian noises, and demonstrate the excellent performance of the novel algorithm by comparing it with other conventional methods. [ABSTRACT FROM AUTHOR]

Two robust kernel adaptive filter (KAF) algorithms, called the kernel least mean p-power (KLMP) and kernel recursive least mean p-power (KRLP), are developed by combining mean p-power error (MPE) criterion and kernel trick for noisy chaotic time series prediction (CTSP). The proposed algorithms employ the MPE to overcome the performance degradation of the CTSP when training data are corrupted by impulsive noises (especially the α-stable noises). First, the KLMP algorithm is proposed by the gradient decent method to improve the robustness of the traditional kernel least mean square (KLMS). Second, the recursion idea and the kernel method are utilized to develop a recursive KAF, namely KRLP, to improve the robustness of the traditional kernel recursive least squares (KRLS). Simulation results show that the proposed algorithms display notable robustness in CTSP when the training data contain different levels of noises, and can perform better in terms of testing MSE than other algorithms. [ABSTRACT FROM AUTHOR]

The design of unknown-input decoupled observers and filters requires the assumption of an existence condition in the literature. This paper addresses an unknown input filtering problem where the existence condition is not satisfied. Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem. It is proved that the state and the unknown inputs can be estimated and decoupled using the extended Double-Model Adaptive Estimation approach without satisfying the existence condition. Numerical examples are presented in which the performance of the proposed approach is compared to methods from literature. [ABSTRACT FROM AUTHOR]

To tackle the challenge of more and more stringent emission regulations, a selective catalytic reduction (SCR) system is widely used all over the world in Diesel-engine applications. In SCR system, input states may be indispensable for onboard diagnostic strategy. Conventionally, the NO x and ammonia input informations are measured by several sensors, however, physical sensors are too costly for application. Besides, sensors would also increase the burden of diagnosis. Inspired by this problem, in this paper, an adaptive unscented Kalman filter (AUKF) is designed to estimate the input concentrations, due to the excellent capacity to deal with nonlinear system and calculate the noise covariance matrices online. Go a step further, the physical sensors can be replaced by the AUKF-based observer. Simulation results through the vehicle simulator cX-Emission show that the performance of observer based on AUKF is outstanding, and the estimation error is very small. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust semi-autonomous algorithm for 3D vessel segmentation and tracking based on an active contour model and a Kalman filter. For each computed tomography angiography (CTA) slice, we use the active contour model to segment the vessel boundary and the Kalman filter to track position and shape variations of the vessel boundary between slices. For successful segmentation via active contour, we select an adequate number of initial points from the contour of the first slice. The points are set manually by user input for the first slice. For the remaining slices, the initial contour position is estimated autonomously based on segmentation results of the previous slice. To obtain refined segmentation results, an adaptive control spacing algorithm is introduced into the active contour model. Moreover, a block search-based initial contour estimation procedure is proposed to ensure that the initial contour of each slice can be near the vessel boundary. Experiments were performed on synthetic and real chest CTA images. Compared with the well-known Chan-Vese (CV) model, the proposed algorithm exhibited better performance in segmentation and tracking. In particular, receiver operating characteristic analysis on the synthetic and real CTA images demonstrated the time efficiency and tracking robustness of the proposed model. In terms of computational time redundancy, processing time can be effectively reduced by approximately 20%. [ABSTRACT FROM AUTHOR]

This paper proposes three steps of improvements for identification of the nonlinear dynamic system, which exploits the concept of a state-space based time domain Volterra model. The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). The applicability of the proposed methods is tested in three simulated data and one experimental data. The results show that Volterra model with PSO–KS is preferable for fast identification process, while ABC–KS method is preferable for accurate identification process. However, in some cases, as the iteration number increases the result of PSO–KS method is comparable with ABC–KS method. [ABSTRACT FROM AUTHOR]

Summary Reservoir inflow forecasting is an integral element of hydropower systems operation and is of paramount importance to hydropower producers. Effective forecasts directly impact power production scheduling, which in turn effects the revenues earned from power production. In this study, we implement a filter updating procedure (GU-COMP) that updates the gains on the error-forecasting component of a complementary forecasting framework (COMP) that also comprises a conceptual model with time invariant parameters. The GU-COMP procedure is applied for forecasting hourly flows of the Krinsvatn catchment (207 km 2 ; located in Norway) over a forecast lead-time of 24 h. The gain coefficients are considered as the only state variables and are updated daily using the error observed between measured and forecasted flows at the catchment outlet. The performance is rated based on evaluation of filter performance (i.e. convergence and consistency), and relative assessment of forecasting skills using the root mean square error (RMSE) and the percentage volume error (PVE) metrics. Bracketing close to 95% of the innovation sequences within two standard deviations from the mean, the filter is found to be well behaved. The RMSE and PVE metrics agree that GU-COMP outperforms COMP in reducing the forecast errors, and significantly altering distributional characteristics of the PVEs in the spring and summer seasons. It is also noted that the relative forecast accuracy enhancement diminishes for forecast lead-times beyond 20 time steps (hours). [ABSTRACT FROM AUTHOR]

Highlights: [•] Fuzzy Logic Adaptive Kalman Filter for system identification is proposed. [•] FLAC-EKF has been verified to exhibit fast convergence rate with limited error. [•] Intensive experiments have been undertaken to examine the efficacy of FLAC-EKF. [Copyright &y& Elsevier]

Abstract: In Interferometric Fiber Optic Gyroscope (IFOG), the diminution of random noise and drift error is a critical task. These errors degrade the performance of IFOG. In this paper, a modified adaptive Kalman gain correction (AKFG) algorithm is proposed to denoise IFOG signal. The covariance matrix of innovation sequence is estimated using weighted average window method in which the weights are randomly generated in the range [0, 1]. Innovation based random weighted estimation (IRWE)-AKFG is applied to denoise the IFOG drift signal. The Kalman gain is adaptively updated using the covariance matrix of innovation sequence. The proposed algorithm is applied for denoising IFOG signal under static and dynamic environment. Allan variance method is used to analyze and quantify the stochastic errors in IFOG sensor. The performance of the proposed algorithm is compared with Conventional Kalman filter (CKF) and the simulation results reveal that the proposed algorithm is an efficient algorithm for denoising the IFOG signal. [Copyright &y& Elsevier]

Abstract: The presence of artifacts and noise effects in temporal series can seriously hinder the analysis of Heart Rate Variability (HRV). The tachograms should be carefully edited to avoid erroneous interpretations. The physician should carefully analyze the tachogram in order to detect points that might be associated with unlikely biophysical behavior and manually eliminate them from the data series. However, this is a time-consuming procedure. To facilitate the pre-analysis of the tachogram, this study uses a method of data filtering based on an adaptive filter which is quickly able to analyze a large amount of data. The method was applied to 229 time series from a database of patients with different clinical conditions: premature newborns, full-term newborns, healthy young adults, adults submitted to a very-low-calorie diet, and adults under preoperative evaluation for coronary artery bypass grafting. This proposed method is compared to the demanding conventional method, wherein the corrections of occasional ectopic beats and artifacts are usually manually executed by a specialist. To confirm the reliability of the results obtained, correlation coefficients were calculated, using both automatic and manual methods of ltering for each HRV index selected. A high correlation between the results was found, with highly significant p values, for all cases, except for some parameters analyzed in the premature newborns group, an issue that is thoroughly discussed. The authors concluded that the proposed adaptive filtering method helps to efficiently handle the task of editing temporal series for HRV analysis. [Copyright &y& Elsevier]