Combination Of Kalman Filter Research Articles

Kalman filter-based sensor fusion for Ackermann steering mobile robots

Abstract Autonomous navigation technologies are continuously improving in terms of performance and safety. A key subfield of autonomous navigation for mobile robots is localization or precise positioning, involving the robot understanding its position relative to key points of interest in the environment. Traditionally, localization is purely dependent on the quality of input received from onboard sensors and therefore localization quality degrades in the presence of high sensor error or other faults. Sensor fusion is useful to combat this. Sensor fusion is used to combine various data types from different sensors to gain a lower overall uncertainty or error in the resulting data, when compared to the separate data from each individual sensor. In this study a four-wheel mobile robot with front-wheel driving and steering is localized during navigation. The investigation is performed in simulation and with a physical prototype. The mobile robot has several sensors including a wheel encoder on each wheel, an inertial measurement unit as well as an indoor GPS system. A designed Kalman Filter called The Combined Kalman Filter has been designed to fuse the sensor input data in order to output the position data required to localize the robot in (X, Y) cartesian coordinates. This is completed in the presence of sensor bias error and noise with the output path compared against a pre-selected ground truth trajectory. The proposed filter is based on the Extended Kalman Filter to contend with the non-linear model arising from the four-wheel steerable robot with the steering system following the Ackermann steering condition. The robot model and sensors are simulated in MATLAB for a chosen ground truth trajectory with results highlighting any detected faults and final accurate position information. It was seen that the estimated filter output had high accuracy when compared with the ground truth. Compared with other investigations the proposed filter has reduced mean error and minimal deviations compared with the ground truth path. In addition, this filtering model can be used in general for the localization of other actuated steering vehicles and aims to widen the research in this field as the majority of research in this area leverages differential drive robots which require less complex modeling.

Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology.

Introduction: Navigation satellite systems can fail to work or work incorrectly in a number of conditions: signal shadowing, electromagnetic interference, atmospheric conditions, and technical problems. All of these factors can significantly affect the localization accuracy of autonomous driving systems. This emphasizes the need for other localization technologies, such as Lidar. Methods: The use of the Kalman filter in combination with Lidar can be very effective in various applications due to the synergy of their capabilities. The Kalman filter can improve the accuracy of lidar measurements by taking into account the noise and inaccuracies present in the measurements. Results: In this paper, we propose a parallel Kalman algorithm in three-dimensional space to speed up the computational speed of Lidar localization. At the same time, the initial localization accuracy of the latter is preserved. A distinctive feature of the proposed approach is that the Kalman localization algorithm itself is parallelized, rather than the process of building a map for navigation. The proposed algorithm allows us to obtain the result 3.8 times faster without compromising the localization accuracy, which was 3% for both cases, making it effective for real-time decision-making. Discussion: The reliability of this result is confirmed by a preliminary theoretical estimate of the acceleration rate based on Ambdahl's law. Accelerating the Kalman filter with CUDA for Lidar localization can be of significant practical value, especially in real-time and in conditions where large amounts of data from Lidar sensors need to be processed.

The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model

AbstractThis synthetic study explores the value of near‐surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS‐1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm3/cm3 for univariate soil temperature assimilation and 0.01 cm3/cm3 for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi‐arid to sub‐humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions.

Behavioral response of fish under ammonia nitrogen stress based on machine vision

The long-term accumulation of ammonia nitrogen in aquaculture seriously affects the life of fish and even causes large-scale death. Moreover, when the concentration of ammonia nitrogen starts to accumulate, it is a judgment standard to provide early warning through the changes in fish behavior to prevent excessive ammonia nitrogen in water. Therefore, this paper proposes a novel approach to monitoring water quality for aquaculture based on deep learning and three-dimensional movement trajectory. The improved YOLOv8 model was used as the object detection approach to obtain three-dimensional position information of fish by combining Kalman filter, Kuhn Munkres (KM) algorithm, and Kernelized Correlation Filters (KCF) algorithm. The proposed approach was evaluated in the recovery experiment of acute ammonia nitrogen stress of sturgeon, bass, and crucian. The experimental results show that the precision, recall, mAP@0.5, and mAP@0.5:0.95 of the improved YOLOv8 model are 0.964, 0.914, 0.979, and 0.602, respectively. In addition, the proposed three-dimensional positioning approach can qualitatively and quantitatively analyze the fish behavior in different stages and further explores the fish behavior changes through behavior trajectories, volumes of exercise, spatial distribution, and movement velocity. This research provides a new method and idea for studying the abnormal behavior of aquatic animals under ammonia nitrogen stress and has theoretical and practical significance.

Dynamic visible light positioning based on enhanced visual target tracking

In visible light positioning systems, some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy, real-time performance, and robustness. However, there are still two problems: (1) When the captured LED disappears and the uncertain LED reappears, existing tracking algorithms may recognize the landmark in error; (2) The receiver is not always able to achieve positioning under various moving statuses. In this paper, we propose an enhanced visual target tracking algorithm to solve the above problems. First, we design the lightweight recognition/demodulation mechanism, which combines Kalman filtering with simple image preprocessing to quickly track and accurately demodulate the landmark. Then, we use the Gaussian mixture model and the LED color feature to enable the system to achieve positioning, when the receiver is under various moving statuses. Experimental results show that our system can achieve high-precision dynamic positioning and improve the system's comprehensive performance.

An edge-computing framework for operational modal analysis of offshore wind-turbine tower

With massive construction of offshore wind turbines (OWT), structural health monitoring (SHM) techniques have been applied to estimate the working conditions and provide early warnings of structural damage. This study proposes an edge-computing-based SHM system for operational modal analysis (OMA) of an OWT. ThssCe system comprised an open-source data acquisition (DAQ) device, a Raspberry Pi single-board computer, and associated drivers, protocols, and algorithms. The DAQ device can measure the acceleration response of an operating OWT subjected to environmental excitation and transmit raw signals to the Raspberry Pi according to the UDP protocol. The data are further processed to obtain the first-order frequency via an OMA algorithm developed by combining Kalman filtering, the random decrement technique (RDT), and the stochastic subspace identification (SSI) method. The proposed SHM system was implemented in a scaled OWT model to verify its feasibility for field use. The experimental results indicate that (1) the edge-computing-based SHM system can accurately measure the response of the OWT; (2) the proposed algorithm can estimate the modal information of the OWT during operation; (3) the variation in the first-order frequency induced by lateral loads can be identified by the SHM system. Unlike a centralized SHM system, the proposed edge-computing-based SHM system does not require massive data transmission, making it promising for offshore environments.

Underwater Acoustic Noise Cancellation Scheme Combining Kalman Filter With Adaptive FxNLMS

Underwater environments are more challenging than that of terrestrial. The performance of a controller or the augmented system as a whole depends on the real measured data, so noise on data readings can be fatal. To effectively and adaptively control lower and higher frequency noise, the Active Noise Cancellation (ANC) was developed. Designing a system and the parameter of modified FxLMS for reducing noise, and disturbance of sensor data is the primary focus of this paper. The required equation will be analyzed and discussed briefly. Moreover, the system will be simulated in MATLAB and then the filtered result will be analyzed. Based on the simulation results, the proposed model can filter out signals with noise, particularly when there is a significant variation in the data and no knowledge of the noise frequency that might affect sensor readings.

Using the best of two worlds: A bio‐economic stock assessment (BESA) method using catch and price data

AbstractReliable stock assessments are essential for successful and sustainable fisheries management. Advanced stock assessment methods are expensive, as they require age‐ or length‐structured catch and detailed fishery‐independent data, which prevents their widespread use, especially in developing regions. Furthermore, modern fisheries management increasingly includes socio‐economic considerations. Integrated ecological‐economic advice can be provided by bio‐economic models, but this requires the estimation of economic parameters. To improve accuracy of data‐limited stock assessment while jointly estimating biological and economic parameters, we propose to use price data, in addition to catches, in a new bio‐economic stock assessment (‘BESA’) approach for de‐facto open access stocks. Price data are widely available, also in the Global South. BESA is based on a state‐space approach and uncovers biomass dynamics by use of the extended Kalman filter in combination with Bayesian estimation. We show that estimates for biological and economic parameters can be obtained jointly, with reliability gains for the stock assessment from the additional information inherent in price data, compared to alternative assessment methods for data‐poor stocks. In a real‐world application to Barents Sea shrimp (Pandalus borealis, Pandalidae), we show that BESA benchmarks well also against advanced stock assessment results. BESA can thus be both a stand‐alone approach for currently unassessed stocks as well as a complement to other available methods by providing bio‐economic information for advanced fisheries management.

Multiple targets video tracking based on extended kalman filter in combination with particle swarm optimization for intelligent applications

Multiple targets tracking is a major issue in the intelligent applications. Numerous methods have been presented for the multiple targets tracking to capture the targets trajectory in a video sequence in order to increase intelligence and reduce human error. In this paper, a method is proposed based on combining the Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO) to construct an intermediate tracker and track targets more accurately. The EKF solves targets collision problem, and PSO reduces the covariance of measured noise. Finally, the Joint Probabilistic Data Association (JPDA) filter is used to reduce the number of multiple hypotheses and create a one-to-one correspondence between targets and measurements. To detect targets, frames subtracting along with background modeling and canny edge detector are used. To reduce running time of the proposed method, number of video frames per second (fps) is reduced from 30 to 10 and the sampling rate is also reduced. Despite of this reduction, simulation reults of the proposed method show the multiple targets tracking with 98% accuracy at an acceptable running time compared to the similar methods. In addition, by using the proposed method, the number of assignment states is reduced in the targets tracking process. Overall, the proposed method not only can be used in the intelligent applications, but also in the video compression applications as well.

Statistical Diagnosis for Quality-Related Faults in BIW Assembly Process

The assembly process of a car body-in-white (BIW) in a body shop is a very important link in complete vehicle manufacturing process, and BIW dimension quality control is the most important part of BIW quality control. The quality-related fixture fault diagnosis problem is proposed in smart dimension control loop [established in BMW Brilliance Automotive company (BBA)] aiming to detect locating fixture fault, which could seriously influence BIW dimension quality. This article introduces a novel statistical quality-related fault diagnosis method by combining Kalman filter and generalized likelihood ratio test, making system innovation as a basic fault diagnosis tool and establishing a hypothesis test between fixture-fault-free model and fixture-faulty model to detect potential fixture fault, meanwhile estimates fixture fault occurred station by maximum likelihood estimate. The fixture fault diagnosis flowchart is built to decrease false and missing alarm rate in actual production process. The case study based on the real BIW component assembly process data from BBA demonstrate that this fault diagnosis method can accurately post fixture fault warning and send correct order to on-site workers to maintain locating fixture in a batch of products.

A layered roll stability control strategy for commercial vehicles based on adaptive model predictive control

Due to the high centre of mass, commercial vehicles are prone to ultimate rollover under extreme steering conditions. The rollover of commercial vehicles leads to serious traffic accidents and poses a threat to road traffic. In this paper, the mechanism of vehicle rollover is revealed by static equilibrium and phase plane analysis. Meanwhile, a robust roll angle observer is designed by combining Kalman filter and sliding mode observation. Considering the influence of wheel lifting on roll dynamics, a novel layered control strategy is proposed by employing engine torque limit, differential braking and active front-wheel steering. Moreover, the layered control of roll stability is realised by adaptive model predictive control with time-varying weights and constraints. To verify the validity and reliability of the proposed algorithm, the hardware-in-loop tests under typical driving conditions have been carried out. The results show that the proposed method can effectively restrain the divergence of roll state and prevent vehicle rollover.

Study on torsional vibration characteristics and suppression of electric vehicles with dual-motor drive system

The battery electric vehicles with dual-motor coupling drive system have serious torsional vibration problems when the driving torque changes rapidly. This paper targets at reducing the negative effect of the torsional vibration with an active control strategy. The torsional vibration model of the dual-motor coupling drive system is established through the lumped mass method. A four-inertia modeling method which can reflect the powertrain configuration characteristics is proposed, by simplifying the traditional lumped mass model, and the feasibility is verified based on the error comparing of the torsional vibration characteristics between the two models. Combined Kalman filter theory, PID control theory and fuzzy control theory, a real-time self-tuning active control strategy is proposed to compensate motor torque by considering vehicle acceleration as the control input. Finally, the four-inertia model with the control strategy is simulated under such conditions as the torque abrupt change and the C-WTVC driving cycle. The simulation results indicated that the proposed active control strategy performs well in the torsional vibration suppression and the control effect is robust to the different structural parameters.

Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space

Affected by the spatial environment, the accuracy and stability of ultra-wideband (UWB) positioning in a narrow space are significantly lower than those in the general indoor environment, which limits navigation and positioning services in a complex scene. To improve the positioning accuracy and stability of a narrow space, this study proposed a positioning algorithm by combining Kalman filter (KF) and dilution of precision (DOP). Firstly, we calculated the DOP values of the target narrow space by changing the location of the test nodes throughout the space. Secondly, the initial coordinate values of the test nodes were calculated by the weighted least squares (WLS) positioning algorithm and were used as the observation values of KF. Finally, the DOP values were adaptively introduced into KF to update the coordinates of the nodes to be tested. The proposed algorithm was tested in two narrow scenes with different length–width ratios. The experimental results showed that the DOP values of the narrow space were much higher than that of the wide space. Furthermore, even if the ranging error was low, the positioning error was high in the narrow space. The proposed fusion positioning algorithm reported a higher positioning accuracy in the narrow space, and the higher DOP values of the scene, the greater the accuracy improvement of the algorithm. This study reveals that no matter how the base stations are configured, the DOP values of the narrow space are much higher than that of the wide space, thus causing larger positioning errors. The proposed positioning algorithm can effectively suppress the positioning error caused by the narrow spatial structure, so as to improve the positioning accuracy and stability.

Mathematical Method to Construct the Linear Programming of Football Training

Abstract This article uses mathematical modeling methods to predict the trajectory of a free kick in football. The article calculates the parameters of the ball’s trajectory and quantifies the power of direct free kick shots. The article combines Kalman filter prediction and linear interpolation to supplement the position of the missed ball in the video. The experimental results show that the estimated result of this method is highly similar to the actual flight trajectory of football. This algorithm can be applied in practice.

Construction of International Education Talents Training Mechanism Based on Data Fusion Algorithm

With the increasing trend of economic globalization and the continuous evolution of economic forms, the role of science and technology in promoting the development of human civilization has become more and more obvious. With the improvement of comprehensive national strength, the knowledge economy has brought new opportunities and challenges. With the increasing internationalization of higher education, it has not only driven the economic exchanges between countries but also led to the exchange of educational resources among transnational countries. However, China’s international education talent cultivation started late, with poor teaching awareness, imperfect discipline construction, and low quality of international education talents. A series of problems have seriously hindered the establishment of the talent training mechanism. In order to establish a reasonable and perfect talent training mechanism, optimize the international education talent training mechanism, make the international education talent training mechanism keep up with the development trend of science, technology, and education in the contemporary world, promote the internationalization of international education talent training, and strengthen the exchange of educational resources among countries. This paper uses various algorithms of fusion algorithms to accurately calculate the talent data. The algorithm results show that the optimized grouped quadratic combined Kalman filter has a shorter running time than the traditional Kalman filter, which decreased about 20%. The S-LEACH algorithm outperforms the traditional algorithm in the simulation results of the number of surviving nodes, relative energy consumption, and data transmission. In response to the national strategy of rejuvenating the country through science and education, cultivating international talents and building a modern talent training mechanism are particularly important.

A Hybrid Method for Road Marking Detection

Very recently, the problems posed in the context of road safety have taken off with the growing number of accidents on the roads. These accidents result in property damage and death in several cases. Nowadays, it is no longer a question of settling for a simple conventional and ordinary road safety, but an automatic road safety is widely desired. This study fits precisely into this context and proposes, first and in terms of computer vision, a combination of two types of approaches, which are by appearance and structural. Indeed the appearance approach is presented by the statistical classifier, while the structural one is given by the set of the following methods: the detection of the segments using the algorithm of the semi local analysis, the combined Kalman filter with predictor to estimate the road line, and the layout adjustment by applying the principle of Minimum Mean Square Error (MMSE). This method is tested on several sample images and videos. It has given a good quality results with very short processing times.

Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform

Predicting financial markets is of particular importance for investors who intend to make the most profit. Analysing reasonable and precise strategies for predicting financial markets has a long history. Deep learning techniques include analyses and predictions that can assist scientists in discovering unknown patterns of data. In this project, application of noise elimination techniques such as Wavelet transform and Kalman filter in combination of deep learning methods were discussed for predicting financial time series. The results show employing noise elimination techniques such as Wavelet transform and Kalman filter, have considerable effect on performance of LSTM neural network in extracting hidden patterns in the financial time series and can precisely predict future actions in these markets.

Reevaluation of Algorithmic Basics for ZUPT-Based Pedestrian Navigation

During the last 10 to 15 years, pedestrian navigation based on zero velocity updates (ZUPT) has become very popular. One of the main reasons for this is the increasing availability of small, low-cost inertial measurement units. However, the processing of the data from these units for pedestrian navigation is almost exclusively based on algorithmic features that originate from classical inertial navigation with high-grade sensors. In addition, the historical background of the ZUPT approach presupposes also sensors with high accuracy. This leads to the problem that neither the ZUPT approach nor the algorithmic features mentioned are consistent with the accuracy level of the inertial measurement units used normally for pedestrian navigation. Therefore, the question arises of whether the usual algorithmic basics and numerical procedures employed by ZUPT-based pedestrian navigation are adequate. Supported by a literature review showing the state of the art, this study investigates the effect of basics such as the system states and the usage of Runge-Kutta method on the navigation accuracy from pedestrian inertial data aided by ZUPT. To this end, a comparative processing of a well-known, published dataset of a short walk and of own data from the authors using different data fusion algorithms was employed. The important results of this study are that the often-used omission of inertial sensor biases cannot be recommended, that a continuous-discrete Kalman filter in combination with a Runge-Kutta algorithm performs better than traditional filter configurations, and that total navigation states are an interesting alternative to classical navigation error-states.

Research on tracking method of moving object under the dynamic background

In order to solve the problem that the traditional moving target tracking algorithm is vulnerable to interference and difficult to accurately track the target for a long time, we first introduce the common target tracking algorithm under the dynamic background, and then we determine the KCF algorithm which has better tracking effect and real-time performance. Then, aiming at the problem of target box offset in the longtime tracking, a tracking algorithm combining Kalman filter algorithm and KCF algorithm is proposed. Finally, the actual effect proves to be effective.

An improved moving object tracking algorithm based on CAMSHIFT

There is a problem that Camshift algorithm relies too much on color as a criterion, in order to solve it, this paper presented an improved moving object tracking algorithm which combined Kalman filter predictor with Camshift algorithm. Firstly, the object model was built by Camshift Algorithm, and the centroid coordinates of the moving objectwere extracted, then the position of the object in the next frame was predicted by using Kalman filter, and finally, the value was used as the centroid coordinates of the object searched by Camshift algorithm in the next frame, proceeding with target match. The simulation results showed that this method can effectively solve the problem of target loss caused by the similar color between the target and the back. The new algorithm can effectively reduce the tracking error accumulation in the tracking process, greatly reduce the template drift, shorten the tracking time, and can have a better practicability and real-time performance.