Search Results (35)

Fire trucks equipped with truck-mounted electric water cannons are key mobile firefighting assets for urban and industrial fire response. However, due to the inherent mechanical inertia of the cannon body, its low-frequency motion response cannot match high-frequency control commands, making the system prone to oscillations and control instability. To address this command–execution frequency mismatch, this paper proposes a decoupled dual closed-loop control architecture for truck-mounted electric water cannons on mobile fire trucks: the fast loop is used for fire-source tracking and rapid localization, while the slow loop is used for water-jet aiming alignment. In the fast loop, a 2-D quadrant positioning rule drives the pan–tilt unit to achieve rapid fire tracking and accurate centering. In the slow loop, Kalman-filter-based state estimation and delay-aligned prediction generate feedforward aiming commands; these commands are fused with error feedback and further processed through command limiting and trajectory optimization, ultimately producing smooth and executable angle references. The visual perception module ran at 58 FPS, satisfying the real-time requirement of the proposed system. In five repeated extinguishment tests under controlled open-site conditions, the proposed method successfully completed all trials and reduced the mean extinguishment time to 13.55 s, compared with 15.83 s for the incremental-PID baseline and 23.76 s for the coupled proportional baseline, while also showing smoother correction and less redundant oscillation. Full article

Historic buildings exhibit coupled response characteristics during long-term service, characterized by slowly varying global inclination evolution superimposed with local component-level deformation. Meanwhile, multi-source measurements are susceptible to environmental noise and structural non-integrality, which poses challenges to obtaining stable and physically interpretable inclination measurements. To address these issues, this study proposes a multi-source fusion monitoring method for global inclination and local deformation of historic buildings using an extended Kalman filter with fractional-order state modeling (FEKF). A state-space model incorporating global inclination, local component-level additional deformation, and their projection relationships is established, in which global inclination information derived from Global Navigation Satellite System (GNSS) and local observations obtained from inclinometers are formulated within a unified measurement framework. Fractional-order dynamics are introduced into the state evolution model to represent the long-memory and non-stationary characteristics of structural responses in historic buildings. By adopting a finite-memory approximation, the fractional-order model is embedded into the extended Kalman filtering framework, enabling joint estimation and physical decoupling of multi-source measurements. Numerical simulation results demonstrate that the proposed method can stably separate global inclination and local deformation components under noisy conditions, while improving the stability of global inclination estimation. Further validation using measured data from a historic building shows that the fusion results effectively suppress high-frequency disturbances in GNSS measurements and allow reliable reconstruction of local component-level inclination responses, indicating good stability and practical applicability. These results demonstrate that the proposed approach provides a physically consistent and robust solution for long-term posture and deformation monitoring of historic buildings. Full article

To mitigate the position divergence resulting from attitude error amplification in conventional fully coupled architectures, this study proposes a decoupled navigation architecture based on the Versatile Quaternion-based Filter (VQF). This architecture removes attitude estimation from the state vector, forming a two-layer structure comprising an independent attitude module and a navigation filter. The VQF is integrated as a standalone attitude module via a standardized interface. An uncertainty quantification model is developed by extracting the VQF’s internal correction states, which maps deviations among intermediate quaternion values to a measurable uncertainty metric. To compensate for the loss of cross-covariance induced by decoupling, a dual-layer compensation mechanism is introduced: a base layer adjusts the overall uncertainty using innovation statistics, while a compensation layer explicitly propagates attitude uncertainty through parameterized noise matrices. Experimental results demonstrate that the proposed method achieves notable improvements in positioning accuracy and significantly suppresses extreme errors in high-curvature scenarios. The approach is particularly effective for high-curvature, high-dynamic applications where process noise modeling is inherently difficult. Compared to traditional fully coupled architectures, the decoupled architecture offers enhanced robustness. The complementary characteristics identified between the two architectures provide valuable insights for expanding the operational envelope of underwater navigation systems. Full article

Realizing real-time dense 3D reconstruction on resource-limited mobile platforms remains a significant challenge, particularly in low-texture environments that demand robust multi-frame fusion to resolve matching ambiguities. However, the inherent tight coupling of pose estimation and mapping in traditional monolithic SLAM architectures imposes a severe restriction on integrating high-complexity fusion algorithms without compromising tracking stability. To overcome these limitations, this paper proposes MDR–SLAM, a modular and fully decoupled stereo framework. The system features a novel keyframe-driven temporal filter that synergizes efficient ELAS stereo matching with Kalman filtering to effectively accumulate geometric constraints, thereby enhancing reconstruction density in textureless areas. Furthermore, a confidence-based fusion backend is employed to incrementally maintain global map consistency and filter outliers. Quantitative evaluation on the NUFR-M3F indoor dataset demonstrates the effectiveness of the proposed method: compared to the standard single-frame baseline, MDR–SLAM reduces map RMSE by 83.3% (to 0.012 m) and global trajectory drift by 55.6%, while significantly improving map completeness. The system operates entirely on CPU resources with a stable 4.7 Hz mapping frequency, verifying its suitability for embedded mobile robotics. Full article

To address the problem of the bottom velocity being directly affected by the time-varying ocean currents when DVL operates in the water observation mode, it cannot be directly used for combined SINS/DVL navigation. Existing methods generally approximate small-scale, short-term currents as constant; however, this assumption is inconsistent with reality over longer durations. When the conventional Kalman filter (KF) algorithm incorporates currents into the state vector, their velocities become entangled with the SINS errors, limiting estimation accuracy. This paper proposes an augmented observation algorithm (AOA) that achieves error decoupling by enhancing DVL observation and deriving the observable current velocity equation without needing external observation information. This approach effectively estimates time-varying currents. The results from simulations and shipboard tests show that, compared to the reference algorithm (Augmented Observation Quantity Filtering algorithm (AOQ)), the proposed AOA significantly decreases the root mean square error (RMSE) of time-varying current velocity estimation by more than 67%. Additionally, the RMSE of the positioning accuracy of the combined SINS/DVL navigation is improved by over 68%. Full article

To achieve precise trajectory tracking and lateral-roll stability during the coordinated control of high-speed autonomous vehicles under lane-changing conditions, this paper proposes an integrated control strategy based on state estimation with a high-order sliding mode and a double-power sliding mode. Firstly, establish a three-degrees-of-freedom vehicle dynamics model and trajectory-tracking error model that includes yaw lateral-roll coupling, and use an extended Kalman filter to estimate real-time unmeasurable states such as the center of mass roll angle, roll angle, and angular velocity. Then, for the trajectory-tracking subsystem, a high-order sliding-mode controller is designed. By introducing a virtual control variable and an arbitrary-order robust differentiator, the switching signal is implicitly integrated into the derivative of the control variable, significantly reducing chattering and ensuring finite-time convergence. Furthermore, in the lateral stability loop, a double-power convergence law sliding-mode controller is constructed to dynamically allocate yaw moment and roll moment with estimated state as feedback, achieving the decoupling optimization of stability and tracking performance. The joint simulation results show that the proposed strategy significantly outperforms traditional sliding-mode schemes in terms of lateral deviation, heading deviation, and key state oscillations under typical high-speed lane-changing conditions. This can provide theoretical basis and engineering reference for integrated control of autonomous vehicles under high dynamic limit conditions. Full article

In high-precision inertial navigation systems, suppressing the random errors of a fiber-optic gyroscope is of great importance. However, the traditional rule-based autoregressive moving average modeling method, when applied in Kalman filtering considering colored noise, presents inherent disadvantages in principle, including inaccurate state equations and difficulties in state dimension expansion. To this end, the noise characteristics in the fiber-optic gyroscope signal are first deeply analyzed, a random error model form is clarified, and a new model-order determination criterion is proposed to achieve the high-precision modeling of random errors. Then, based on the effective suppression of the angle random walk error of the fiber-optic gyroscope, and combined with the linear system equation of its colored noise, an adaptive Kalman filter based on noise-spectrum information decoupling is designed. This breaks through the principled limitations of traditional methods in suppressing colored noise and provides a scheme for modeling and suppressing fiber-optic gyroscope random errors under static conditions. Experimental results show that, compared with existing methods, the initial alignment accuracy of the proposed method based on 5 min data of fiber-strapdown inertial navigation is improved by an average of 48%. Full article

We propose an adaptive multi-timescale particle filter (AMTS-PF) for nonlinear state estimation in wastewater treatment plants (WWTPs) to address multi-scale temporal dynamics. The AMTS-PF decouples the problem into minute-level state updates and hour-level parameter refinements, integrating adaptive noise tuning, multi-scale entropy-driven feature extraction, and dual-timescale particle weighting. It dynamically adjusts noise covariances via Bayesian fusion and uses wavelet-based entropy analysis for adaptive resampling. The method interfaces seamlessly with existing WWTP control systems, providing real-time state estimates and refined parameters. Implemented on a heterogeneous computing architecture, it combines edge-level parallelism and cloud-based inference. Experimental validation shows superior performance over extended Kalman filters and single-timescale particle filters in handling nonlinearities and time-varying dynamics. The proposed AMTS-PF significantly enhances the accuracy of state estimation in WWTPs compared to traditional methods. Specifically, during the 14-day evaluation period using the Benchmark Simulation Model No. 1 (BSM1), the AMTS-PF achieved a root mean square error (RMSE) of 54.3 mg/L for heterotroph biomass (XH) estimation, which is a 37% reduction compared to the standard particle filter (PF) with an RMSE of 68.9 mg/L. For readily biodegradable substrate (Ss) and particulate products (Xp), the AMTS-PF also demonstrated superior performance with RMSE values of 7.2 mg/L and 9.8 mg/L, respectively, representing improvements of 24% and 21% over the PF. In terms of slow parameters, the AMTS-PF showed a 37% reduction in RMSE for the maximum heterotrophic growth rate (μH) estimation compared to the PF. These results highlight the effectiveness of the AMTS-PF in handling the multi-scale temporal dynamics and nonlinearities inherent in WWTPs. This work advances the state-of-the-art in WWTP monitoring by unifying multi-scale temporal modeling with adaptive Bayesian estimation, offering a practical solution for improving operational efficiency and process reliability. Full article

With the increasing capacity of wind turbines, key components including the rotor diameter, tower height, and tower radius expand correspondingly. This heightened inertia extends the response time of pitch actuators during rapid wind speed variations occurring above the rated wind speed. Consequently, wind turbines encounter significant output power oscillations and complex structural loading challenges. To address these issues, this paper proposes a novel pitch control strategy combining an effective wind speed estimation with the inverse system method. The developed control system aims to stabilize the power output and rotational speed despite wind speed fluctuations. Central to this approach is the estimation of the aerodynamic rotor torque using an extended Kalman filter (EKF) applied to the drive train model. The estimated torque is then utilized to compute the effective wind speed at the rotor plane via a differential method. Leveraging this wind speed estimate, the inverse system technique transforms the nonlinear wind turbine dynamics into a linearized, decoupled pseudo-linear system. This linearization facilitates the design of a more agile pitch controller. Simulation outcomes demonstrate that the proposed strategy markedly enhances the pitch response speed, diminishes output power oscillations, and alleviates structural loads, notably at the tower base. These improvements bolster operational safety and stability under the above-rated wind speed conditions. Full article

The performance limitations of single-sensor systems in target tracking have led to the widespread adoption of multi-sensor fusion, which improves accuracy through information complementarity and redundancy. However, on mobile platforms, dynamic changes in sensor attitude and position introduce coupled measurement and attitude errors, making accurate sensor registration particularly challenging. Most existing methods either treat these errors independently or rely on simplified assumptions, which limit their effectiveness in dynamic environments. To address this, we propose a novel joint error estimation and registration method based on a pseudo-Kalman filter (PKF). The PKF constructs pseudo-measurements by subtracting outputs from multiple sensors, projecting them into a bias space that is independent of the target’s state. A decoupling mechanism is introduced to distinguish between measurement and attitude error components, enabling accurate joint estimation in real time. In the shipborne environment, simulation experiments on pitch, yaw, and roll motions were conducted using two sensors. This method was compared with least squares (LS), maximum likelihood (ML), and the standard method based on PKF. The results show that the method based on PKF has a lower root mean square error (RMSE), a faster convergence speed, and better estimation accuracy and robustness. The proposed approach provides a practical and scalable solution for sensor registration in dynamic environments, particularly in maritime or aerial applications where coupled errors are prevalent. Full article

Low-cost MEMS sensors are widely utilized in UAV platforms to address attitude estimation problems due to their compact size, low power consumption, and cost-effectiveness. Diverse UAV payloads pose new challenges for attitude estimation, such as magnetic interference environments and high dynamic environments. In this paper, we propose a hierarchical decoupled attitude estimation algorithm, termed HDAEA. Initially, a novel hierarchical decoupling approach is introduced for the attitude and angle representation of the direction cosine matrix, enabling the representation of angles in a new manner. This method reduces the data dimensionality and nonlinearity of observation equations. Furthermore, a magnetic interference identification algorithm is proposed to compute the magnetic interference intensity accurately and quantitatively. Combining the quantified errors of estimated state variables, an error model for magnetic interference and attitude angles in high-dynamic environments is constructed. Subsequently, the proposed error model is employed to calibrate the hierarchical decoupled angles using accelerometer and magnetometer measurements, effectively mitigating the impact of magnetic interference on the calculation of pitch angles and roll angles. Moreover, the integration of the proposed hierarchical decoupled attitude estimation algorithm with the error-state extended Kalman filter reduces system nonlinearity and minimizes linearization errors. Experimental results demonstrate that HDAEA exhibits significantly improved attitude estimation accuracy of UAV payloads. Full article

Deep space exploration utilizing high-orbit vehicles is a vital approach for extending beyond near-Earth space, with orbit information serving as the foundation for all functional capabilities. The performance of orbit determination is primarily influenced by observation types, errors, geometrical structures, and physical perturbations. Currently, research on orbit determination for high-orbit spacecraft predominantly focuses on single observation methods, error characteristics, multi-source fusion techniques, and algorithms. However, these approaches often suffer from low observation accuracy and increased costs. This paper advocates for the comprehensive utilization of existing multi-source observation methods, such as GNSS (Global Navigation Satellite System), SLR (Satellite Laser Ranging), and VLBI (Very Long Baseline Interferometry), in research. The decoupled Kalman filter reveals a positive correlation between measurement positioning accuracy and orbit determination accuracy, and it derives a simple orbit performance evaluation model that considers the influence of observation value types and geometric configurations, without the need to introduce complex dynamic models. Simulations are then employed to verify and analyze antenna gain, observation values, and performance evaluation. The results indicate the following: (1) Under simulated conditions, the optimal strategy involves employing the SLR/VLBI dual system during periods when VLBI orbit determination is feasible, yielding an average Weighted Position Dilution of Precision (WPDOP) of 26.79. (2) For periods when VLBI orbit determination is not feasible, the optimal approach is to utilize the GNSS/SLR/VLBI triple system, resulting in an average WPDOP of 16.32. (3) The orbit determination performance of the triple system is not significantly impacted by the use of global SLR stations compared to using only Chinese SLR stations. However, the global network enables continuous, round-the-clock orbit determination capabilities. Full article

This paper introduces a cooperative positioning algorithm for agricultural vehicles, which uses the relative distance of the workshop to improve the performance of the Global Navigation Satellite Systems (GNSS), to improve the positioning accuracy and stability. Firstly, the extended Kalman filter (EKF) fuses the vehicle motion state data with GNSS observation data to improve the independent GNSS positioning accuracy. Subsequently, vehicle state and observation models are formulated using Bayesian theory, incorporating GNSS/UWB data with UWB tag network ranging and with GNSS positioning data among agricultural vehicles and Inter-Vehicular Ranges (IVRs). This integration addresses the significant drift issue in GNSS elevation positioning by employing a high-dimensional decoupling algorithm, standardizing the discrete elevation data, and improving the data’s continuity and predictability. A particle filter is used to refine the vehicle’s position estimation further. Finally, experiments are carried out to verify the robustness of the proposed algorithm under different working conditions. Full article

First-order algorithms have long dominated the training of deep neural networks, excelling in tasks like image classification and natural language processing. Now there is a compelling opportunity to explore alternatives that could outperform current state-of-the-art results. From the estimation theory, the Extended Kalman Filter (EKF) arose as a viable alternative and has shown advantages over backpropagation methods. Current computational advances offer the opportunity to review algorithms derived from the EKF, almost excluded from the training of convolutional neural networks. This article revisits an approach of the EKF with decoupling and it brings the Fully Decoupled Extended Kalman Filter (FDEKF) for training convolutional neural networks in image classification tasks. The FDEKF is a second-order algorithm with some advantages over the first-order algorithms, so it can lead to faster convergence and higher accuracy, due to a higher probability of finding the global optimum. In this research, experiments are conducted on well-known datasets that include Fashion, Sports, and Handwritten Digits images. The FDEKF shows faster convergence compared to other algorithms such as the popular Adam optimizer, the sKAdam algorithm, and the reduced extended Kalman filter. Finally, motivated by the finding of the highest accuracy of FDEKF with images of natural scenes, we show its effectiveness in another experiment focused on outdoor terrain recognition. Full article

Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively. Full article