Speed Requirements Research Articles

Inline Measurement of Light Beam Induced Current (LBIC) Under High-Injection and Reverse Bias Conditions

Quality control in solar cell production relies on characterization methods that are fast enough to yield information on the properties of each solar cell in less than about one second. Imaging techniques such as electroluminescence (EL) are well established methods revealing spatially resolved quality mappings. Scanning techniques such as light beam induced current (LBIC) are common laboratory methods yielding complementary information but do not meet the speed requirements. In our work, we analyse an inline implementation of the LBIC method which is extended with respect to its measurement parameters, i.e., both the laser power and a solar cell bias-voltage are varied. As these extended conditions differ from conventional LBIC-applications at zero bias and low injection, we compare our inline-LBIC images with EL images and conventional LBIC images by means of an image contrast analysis. We find that our method resembles more the EL imaging as variations in the local series resistance are prominently detected. Thus, the proposed LBIC approach with extended measurement parameter range can yield both local short circuit information and local series resistance information. With our setup, measurement speeds around 700 ms are achieved.

The Impact of Disease X on Potential Travelers’ Travel Decision

This study used ANCOVA models to investigate how pandemic characteristics—spreading speed, severity, and vaccination requirements—affect travel intentions. The results reveal that these factors explain 31.7% of the variance in travel decisions, with disease-spreading speed and severity being the most significant determinants. While vaccination requirements are relevant, they play a secondary role compared to the immediate threat of disease characteristics. The interaction effects between these factors further demonstrate their combined impact on travel reluctance. Demographic variables, such as gender and the presence of children, also influence decisions in specific contexts. These findings contribute to the understanding of risk perception during health crises, reinforcing the role of perceived severity in shaping cautious travel behavior. Practical implications for the tourism industry include the need for transparent communication, tailored health protocols, and demographic-specific marketing strategies. Future research should explore broader factors and adopt longitudinal approaches to capture evolving travel intentions during pandemics.

ACCELERATING IMAGE PROCESSING USING PARALLEL COMPUTATIONS IN OPENMP: DEVELOPMENT AND PERFORMANCE ANALYSIS OF A GRAPHICS EDITOR

In today’s digital world, image processing plays a crucial role in various aspects of human life. From creating visual content for social media to analyzing medical images, powerful tools for image manipulation are in constant demand. The growing requirements for image quality and processing speed make the search for efficient methods to accelerate these processes highly relevant. This paper investigates the development and implementation of a graphics editor that utilizes OpenMP parallel computing to accelerate data processing. This technology enables the efficient distribution of computational tasks across multiple processor cores, significantly improving system performance. The developed software offers a set of popular graphic effects, including negative, grayscale, sepia, blur, sharpening, and posterization. Each effect is implemented in both sequential and parallel modes using OpenMP, allowing for the comparison of different computational approaches. To evaluate the effectiveness of the proposed solution, a series of experiments were conducted on images of various sizes. These experiments involved applying each effect to images ranging from small icons to high-quality photographs. A comparative analysis of the efficiency of sequential and parallel computation methods demonstrated the significant advantages of the latter. The results of the study show a substantial acceleration of image processing when using OpenMP technology. This acceleration is particularly noticeable for computationally intensive effects such as blurring or sharpening, and when working with large images. In some cases, it was possible to achieve a significant increase in processing speed, opening up new possibilities for working with large volumes of graphic data. This research has significant practical value for software developers working on optimizing the performance of graphics editors and other image processing applications. It demonstrates how the application of modern parallel computing technologies can significantly improve the efficiency of working with graphic data, paving the way for the creation of more powerful and faster image processing tools.

A Human Pose Estimation Method Based on the BlazePose Model

Objective: This study aims to evaluate the performance of the BlazePose model in human pose estimation for sports actions to improve the accuracy and computational efficiency of the model. Methods: The research employed a variety of datasets for training and evaluating the model, investigating the generalization capability of the model under different experimental conditions. Detection and tracking were carried out through the two-stage mechanism of the BlazePose model, identifying the human figure contours and initially predicting the key points, and then refining the positions of the key points through the tracking module. Different parameters such as model complexity and key point smoothing were set, and the steps including image preprocessing, feature extraction, key point detection, naming, and skeleton construction were conducted. Results: The research outcomes yielded the two-dimensional keypoints of single-frame human body images and the construction of 3D coordinate models, and obtained the visualization line charts of PCK experimental data. Through the comparison of PCK scores of different models, it was concluded that on the basketball Dataset, the performance of BlazePose was superior to that of OpenPose, particularly in terms of FPS performance. Although the PCK score of the lightweight version of BlazePose was slightly lower, its FPS performance was higher, making it suitable for scenarios with high requirements for speed. Furthermore, BlazePose effectively reduced the model complexity by offering models of different complexities and using occlusion simulation in training, without sacrificing too much accuracy. Conclusion: This research presents the efficacy of lightweight neural networks in real-time human pose estimation and, through further experimental analyses, investigates the possibilities of enhancing their performance advantages and application effects.

Unbalance detection of threshing cylinder in non-stationary rotation based on variational mode extraction

The rotating speed of the threshing cylinder was hardly constant in practice due to transmission error and cylinder speed would change with feed quantity to ensure the grain threshing effect. This paper proposed a novel unbalance detection method for non-stationary rotation. Variational mode extraction (VME) was introduced to extract unbalanced vibration signal and the unbalance feature index (UFI) was constructed as a criterion of weighting factor selection. Moreover, the unbalance phase calculation method and phase reference in non-stationary were proposed, which could eliminate the influence of speed change. The advantages of VME and UFI were verified by simulation, and the effectiveness of proposed method was proved by experiment on multi-cylinder test rig and combine harvest. This method could realize the reconstruction of unbalanced vibration signal in time-varying speed with low computational cost, break the strict requirement of constant speed of the existing balancing method.

A trajectory tracking control method for the discharge arm of the self-propelled forage harvester

The cooperative operation between the self-propelled forage harvester and the trailer aims to achieve precise and automatic forage unloading. The discharge arm serves as the core structure for conveying forage, and the precision and speed of its motion control are crucial factors influencing the efficiency of forage harvesting. In this context, we proposed a trajectory tracking control method for the discharge arm based on an improved particle swarm optimization (IPSO)-PID controller. Firstly, we utilized the Denavit-Hartenberg (D-H) model of the discharge arm for a positive kinematics solution and the geometric resolution and linear fitting method for the inverse kinematics solution. Secondly, we employed the polynomial interpolation method for trajectory planning on the joint space of the discharge arm, and the PSO algorithm for time-optimal trajectory planning. Then, we designed an IPSO-PID trajectory tracking control algorithm and built an Amesim-Simulink co-simulation model for multiple simulation experiments. Finally, we conducted several performance tests of the discharge arm automatic control system in the workstation and the field, respectively. The experiment results indicate that the performance of the IPSO-PID controller exceeds that of all other controllers, which can meet the discharge arm’s motion control accuracy and speed requirements. Our research results are of great significance for improving the productivity and automation process of the self-propelled forage harvester and provide valuable references for research on automatic and precise control of material loading in other agricultural cooperative harvesting.

The Identification, Separation, and Clamp Function of an Intelligent Flexible Blueberry Picking Robot

Identifying fruit maturity accurately and achieving damage-free harvesting are challenges in designing blueberry-picking robots. This paper presents an intelligent flexible picking system. First, we trained a deep learning-based YOLOv8n network to locate the position of the fruit and determine fruit ripeness. We used a neural network to establish the relationship between fruit hardness and shape parameters, achieving an adaptive gripping force for different fruits. To address the issue of dense clusters in some blueberry varieties, we designed a fruit separation subsystem using a combination of flow field analysis and pressure-sensitive experiments. The results show that the mean average precision can reach 84.62%, the precision is 94.49%, the recall is 83.85%, the F1 score is 88.85%, and the test time is 0.12 s, which can meet the requirements for blueberry fruit recognition accuracy and speed. The spacing between closely packed fruits can increase by 4 mm, and the damage-free picking rate exceeds 92%, achieving stable, damage-free harvesting.

Detection and assessment of post-earthquake functional building ceiling damage based on improved YOLOv8

Ceiling is one of the crucial non-structural components in public buildings, but it is susceptible to damage after earthquakes. For important functional buildings, rapid assessment of their safety, economic losses, and recoverability is crucial post-earthquake. However, the current detection and assessment of ceiling damage in earthquake-stricken areas heavily relies on manual efforts. Due to limitations in detection methods and technological levels, these manual efforts struggle to meet the requirements of speed, accuracy, and safety. In response to these challenges, this study proposes a post-earthquake functional building ceiling damage detection and assessment method based on GTS-YOLOv8. Firstly, a dataset containing 1059 real ceiling damage images is established, categorized into two damage types: “Panels Fallen" (PF) and “Complete Destruction" (CD). Next, Ghost convolution is introduced, combined with Swin Transformer blocks, and a novel Slim Neck structure is designed to enhance the YOLOv8 algorithm. The GTS-YOLOv8 object detection model is presented. On the established dataset, GTS-YOLOv8 achieves 92 % mAP50 and 72.2 % mAP50-95, surpassing the baseline YOLOv8 by 2.3 % and 4.1 %, respectively. The study investigates ceiling damage detection under different lighting conditions and shooting distances. The results indicate that the highest detection accuracy is achieved under well-lit conditions and when the shooting perspective covers the entire ceiling area. Finally, based on the research outcomes, a detection and assessment architecture is constructed, and a corresponding system is developed, capable of automatically determining the damage State based on ceiling damage detection results. This architecture provides a foundation for the rapid assessment of building safety, economic losses, and recoverability post-earthquake.

Research on ultra-clean micro gas flow calibration technology of passive piston type with sealing

Aiming at the portable measurement and calibration needs of ultra-clean micro gas flow meters in the semiconductor industry, this paper designs an ultra-clean micro gas flow standard device based on passive piston type with sealing. The device adopts a horizontal structure, using the O-ring on the piston for radial sealing, unlike the traditional mercury and gap sealing, this design avoids the stringent requirements for piston speed and clearance. To increase automation of the calibration process, an automatic calibration system is built. Mathematical modeling and 6DOF dynamic mesh analysis are used to ensure that the piston operates at a stable stage. Through the uncertainty analysis, the extended uncertainty of the device reaches 0.21 % (k = 2). The uncertainty was verified by normalizing the deviation En and the results show that it is feasible to trace the clean gas flow rate with the piston standard device.

Application of Traffic Cone Target Detection Algorithm Based on Improved YOLOv5.

To improve the automation level of highway maintenance operations, the lightweight YOLOv5-Lite-s neural network was deployed in embedded devices to assist an automatic traffic cone retractor in completing recognition and positioning operations. The system used the lightweight shuffle Net network as a backbone for feature extraction, replaced convolutional layers with focus modules to reduce computational complexity, and reduced the use of the C3 layer to increase network speed, thereby meeting the speed and accuracy requirements of traffic cone placement and retraction operations while maintaining acceptable model inference accuracy. The experimental results show that the network could maintain recognition accuracy and speed values of around 89% and 9 fps under different working conditions such as varying distances, lighting conditions, and occlusions, meeting the technical requirements for deploying and retrieving cones at a speed of 30 cones per minute when the operating vehicle's speed was 20 km/h. The automatic traffic cone placement and retraction system operated accurately and stably, achieving the application of machine vision in traffic cone retraction operations.

Enhancing Efficiency Through Hydrogen-Powered Hyperloop Capsules: AI-Assisted Route Planning in Accordance with Circular Economy Principles

Abstract This study investigates the feasibility of integrating Hyperloop technology with hydrogen fuel systems. The primary objective was to evaluate how Hyperloop infrastructure can incorporate hydrogen refueling stations to optimize transport efficiency and sustainability. The research utilized simulation models developed with ArchiCAD and FlexSim software to analyze the integration of Hyperloop cargo and passenger transport with hydrogen refueling processes. The study included detailed simulations of Hyperloop operations and refueling logistics, focusing on safety, efficiency, and the potential for reducing carbon emissions. Key parameters such as capsule speed, refueling times, and infrastructure requirements were analyzed. Results indicated that Hyperloop technology can significantly enhance transport efficiency, particularly for high-value and time-sensitive goods, while hydrogen refueling stations offer a sustainable energy solution with considerable potential for reducing environmental impact. The findings demonstrate that integrating hydrogen refueling stations into the Hyperloop system can improve operational efficiency and sustainability. However, challenges remain in optimizing refueling processes and infrastructure development. Future research should address these challenges and explore additional AI-based optimization techniques to enhance the overall system performance. This research is especially useful for urban planners, transportation engineers, and policymakers aiming to develop efficient, sustainable transportation systems that incorporate cutting-edge technology like Hyperloop and hydrogen refueling infrastructure.

Research on Target Hybrid Recognition and Localization Methods Based on an Industrial Camera and a Depth Camera in Complex Scenes

In order to improve the target visual recognition and localization accuracy of robotic arms in complex scenes with similar targets, hybrid recognition and localization methods based on an industrial camera and depth camera are proposed. First, according to the speed and accuracy requirements of target recognition and localization, YOLOv5s is introduced as the basic algorithm model for target hybrid recognition and localization. Then, in order to improve the accuracy of target recognition and coarse localization based on an industrial camera (eye-to-hand), the AFPN feature fusion module, simple and parameter-free attention module (SimAM), and soft non-maximum suppression (Soft NMS) are introduced. In order to improve the accuracy of target recognition and fine localization based on a depth camera (eye-in-hand), the SENetV2 backbone network structure, dynamic head module, deformable attention mechanism, and chain-of-thought prompted adaptive enhancer network are introduced. After that, on the basis of constructing a dual camera platform for target hybrid recognition and localization, the hand–eye calibration, collection and production of image datasets required for model training are completed. Finally, for the docking of the oil filling port, the hybrid recognition and localization experimental tests are completed in sequence. The test results show that in target recognition and coarse localization based on the industrial camera, the recognition accuracy of the designed model reaches 99%, and the average localization errors in the horizontal and vertical directions are 2.22 mm and 3.66 mm, respectively. In target recognition and fine localization based on the depth camera, the recognition accuracy of the designed model reaches 98%, and the average errors in depth, horizontal, and vertical directions are 0.12 mm, 0.28 mm, and 0.16 mm, respectively. These not only verify the effectiveness of the target hybrid recognition and localization methods based on dual cameras, but also demonstrate that they meet the high-precision recognition and localization requirements in complex scenes.

Emerging Trends in Integrated Digital Microfluidic Platforms for Next-Generation Immunoassays.

Technologies based on digital microfluidics (DMF) have made significant advancements in the automated manipulation of microscale liquids and complex multistep processes. Due to their numerous benefits, such as automation, speed, cost-effectiveness, and minimal sample volume requirements, these systems are particularly well suited for immunoassays. In this review, an overview is provided of diverse DMF manipulation platforms and their applications in immunological analysis. Initially, droplet-driven DMF platforms based on electrowetting on dielectric (EWOD), magnetic manipulation, surface acoustic wave (SAW), and other related technologies are briefly introduced. The preparation of DMF is then described, including material selection, fabrication techniques and droplet generation. Subsequently, a comprehensive account of advancements in the integration of DMF with various immunoassay techniques is offered, encompassing colorimetric, direct chemiluminescence, enzymatic chemiluminescence, electrosensory, and other immunoassays. Ultimately, the potential challenges and future perspectives in this burgeoning field are delved into.

Visualization of particulate matters in air using lensless microscopy

Abstract. Air quality is crucial for health, yet monitoring airborne particulate matter (PM), particularly PM2.5, remains challenging due to pollution and widespread distribution. PM2.5, with diameters less than 2.5 micrometers, are known to have significant health implications. Traditional methods of PMs detection and analysis are often time-consuming or require complex optical systems. This study explores the use of lensless microscopy as an innovative and cost-effective alternative for the visualization and measurement of PMs. Utilizing computational imaging techniques, lensless microscopy captures high-resolution images without the need for traditional lenses, facilitating rapid and accurate analysis of air quality. We describe the implementation of lensless imaging to detect and quantify PM2.5 particles in the air, highlighting the use of the single light wavelength and the computational reconstruction method. Our experiments demonstrated that this approach could effectively capture fine details of PMs, achieving resolutions comparable to traditional microscopy. This validates the potential of lensless microscopy as a practical tool for real-time air quality monitoring. By adopting lensless microscopy, we offer a more efficient and economical method for the continuous monitoring of PMs, which can significantly contribute to environmental health studies and pollution control efforts. According to specific needs, simply replacing the flow rate of the air pump can adapt to the PM measurement speed requirements in different environments. This research introduces a novel approach to air quality assessment, providing a promising solution for the rapid and detailed analysis of airborne particulate matter while at an affordable cost.

Unveiling robust security: Chaotic maps for frequency hopping implementation in FPGA

Frequency hopping is a main technique for wireless communication, avoiding interference and interception. This paper provides novel hardware design for frequency-hopping pseudorandom bit generator (PRBG).PRBG design by chaotic maps on FPGA.Two proposed methods in this work first combine chaotic maps in a cascade manner called fixed point cascade chaotic maps (FPCCM-FHSS), and second, the conjunction of chaotic maps done in an XORed manner called fixed point an XOR chaotic method (FPXORCM-FHSS).The results were the first eight NIST randomness tests.Frequency indicates that all p-values larger than 0.01 are needed to achieve better randomness,Second and third were die-hard tests, many distribution tests with significant p-values (p <0.01) that meet high standards of statistical randomness, making them suitable for channel security.Last were the FPGA results between the proposed methods for speed and hardware resources).The works implemented on XILINX ZC702 achieved 2 Gbps to meet the speed requirements of the change of the carrier frequency.

Optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant

This paper presents optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant. The power plant is equipped with four steam boilers using a damper-louvers airflow control system. Analysis has revealed significant energy wastage due to inefficiency and over-air supply, with the system requiring 59,904,436 kWh annually at a financial cost of N1, 093,255,884. The existing system's inefficiency results in an annual electrical energy waste of 8,502,302 kWh. This inefficiency is attributed to the Force Draft Fan (FDF) motor operating at maximum speed continuously, regardless of the steam profile or airflow requirements. This study aimed at optimizing the steam boiler airflow control system in a typical oil refinery power plant for improved power efficiency, energy conservation, and cost savings citing Port Harcourt Refinery Company (PHRC) at Alesa-Eleme, Rivers State, Nigeria. To address this, the damper-louvers system was replaced with a Variable Frequency Drive (VFD), which would adjust the motor speed to match the required airflow, thereby reducing energy wastage. The process was simulated and VFD/FDF motor analyzed in MATLAB Simulink, determining the optimal speed, airflow, and power requirements. Results obtained show that the VFD control system improves the FDF motor's power efficiency from 47.7% to 85%, with an annual energy saving of 8,502,302 kWh (8.5 GWh). This results in a total cost benefit of N478.18 million annually, representing a 43.8% cost saving. The implementation cost of the VFD infrastructure was estimated at N111, 440,000, with a payback period of approximately eleven months. The findings suggest deployment of higher FDF motor speed ratings to enhance speed control and energy efficiency in Oil refinery power plant.

Performance evaluation of 3D printed acoustic metamaterial using soft computing approach

This paper introduces a novel methodology for predicting the acoustic absorption coefficient of DENORMS cell based acoustic metamaterial. The samples were printed from resin using Digital Light Processing based 3D printing technique. The manufactured samples were tested in an Impedance tube using the two Microphone method. A virtual simulation test rig was used to generate data sets for geometrically distinct DENORMS cell based metamaterial. Four distinct soft computing techniques specifically the “Neural Networks (NN), Random Forests (RF), Decision Trees (Rpart) and Generalized Linear Model (GLM)”, were employed and compared to develop an accurate prediction model for forecasting the absorption coefficient of the developed metamaterial. The machine learning techniques were used due to their higher speed and lower computational power requirement compared to numerical simulations to determine the absorption coefficient. The input variables consist of the Spherical Diameter, Cylindrical Diameter, Cylinder Length of the DENORMS cell and Frequency of Incident noise. The performance of the four prediction models was evaluated based on criteria such as Root mean square error, Coefficient of determination, Correlation and Accuracy. Ten-Fold cross validation is performed to test the robustness of the model.

The concept of effective coverage radius use of the unlicensed high-frequency range in the operation of the 5G network

Economic expediency encourages mobile operators to deploy 5G networks in places with a high concentration of speed-demanding subscribers. In such conditions, sharp fluctuations in the volume of traffic with regulated requirements for the quality of service are inevitable. Note that 5G operates in the millimeter range. Accordingly, the quality of traffic service is affected both by the number of subscribers simultaneously initiating requests from one sector of coverage, and by the appearance of obstacles opaque to radio radiation in the space between the subscriber device and the base station. Effective smoothing of 5G traffic fluctuations, taking into account these disturbing factors, is an urgent task. The goal of this research is to evaluate the service quality parameters in a target area characterized by a specific user density. It takes into account that if the declared QoS requirements for connection speed for users in a network segment deployed within the licensed frequency range for 5G are not met, they can utilize a network segment deployed in the unlicensed high-frequency range for 5G under conditions of free competition. The metric being studied is the probability of session loss in the licensed network segment and the achievable transmission speed in the unlicensed network segment. Based on this, a method for assessing the density of base station deployment in the unlicensed network segment necessary to support the specified user density in the licensed network segment with defined QoS guarantees in terms of bandwidth is formalized. The experiment results showed that the probability of losing sessions with regulated requirements for the quality of service in both network segments, in addition to the base station placement density and subscriber devices, is significantly affected by the minimum data transfer rate, the intensity of obstacles, and the value of the Contention Window.

Analysis of mixed traffic flow characteristics based on cellular automata model under lane management measures

In the future, mixed traffic flow comprising Human-Driven Vehicles (HDVs), Connected and Autonomous Vehicles (CAVs), and CAV platoons will coexist for an extended period. Exploring the operational characteristics of mixed traffic flow under different lane management measures is essential for effective management and control. Initially, we analyze the motion characteristics of HDVs, CAVs, and CAV platoons and identify the car-following types within mixed traffic. Based on vehicle motion characteristics and the variance in maximum desired speeds among HDV drivers, we establish longitudinal motion rules for HDVs using a safety distance model. For CAVs and their platoons, we develop longitudinal motion rules by considering platoon merging and splitting behaviors, as well as speed and spacing error requirements for platoon driving. Subsequently, we formulate lateral lane-change rules based on the Symmetric Two-lane Cellular Automata (STCA) model, considering differences in reaction times and following distances between HDVs and CAVs. Finally, we conducted simulation experiments on a unidirectional three-lane highway using the multi-lane mixed traffic flow cellular automata model, analyzing the characteristics of mixed traffic under various lane management measures, such as mixed lane, CAV-dedicated lane, and CAV-priority lane. The results show that as the proportion of CAV (denoted as p) increases, the traffic flow capacity, optimal density, and jam density also increase. When p≤0.6, the k−q diagram exhibits a triangular shape, whereas for p>0.6, it assumes a trapezoidal shape. Implementing CAV-dedicated lane can reduce Cooperative Adaptive Cruise Control (CACC) degradation rates but only enhances traffic flow capacity when the CAV proportion reaches a certain threshold. Compared to the mixed lane scheme, when p≤0.3 and k≤0.3, the CAV-priority lane schemes not only meet traffic demands but also reduce CACC degradation rates. The vehicle speed in CAV-priority lane surpasses that in HDV lanes, facilitating improved traffic efficiency for CAVs. The distribution of maximum speeds among HDV drivers affects the fundamental diagram of mixed traffic flow and the performance of CAV-priority lane, with greater impacts observed as the standard deviation of the HDVs' maximum speed increases.

Adaptive interacting multiple model particle filter based on a dual-pattern bat algorithm for maneuvering target tracking

The interacting multiple model particle filter (IMM-PF) is a filtering method commonly used for nonlinear non-Gaussian radar systems. Its transfer probabilities are usually fixed and dependent on prior information. When tracking maneuvering targets, the IMM-PF may cause excessive tracking errors due to the model switching lag. In addition, the particle impoverishment caused by the resampling of the particle filter seriously affects the filtering accuracy. Therefore, the IMM-PF has difficulty meeting the accuracy and speed requirements of modern high-performance radar target tracking systems. To address these problems, an adaptive interacting multiple model particle filter based on the dual-pattern bat algorithm (ADIMM-DPBA-PF) is proposed for tracking maneuvering targets under nonlinear and non-Gaussian conditions. First, the adaptive model switching mechanism is established to adjust the transition probabilities using the model probability posterior information at consecutive times, improving the model switching efficiency. Second, a particle filter based on the dual-pattern bat algorithm (DPBA-PF) is proposed. The filter exploits global search and local search strategies to optimize the particles and intelligently move the particles to the high likelihood region. Finally, a particle filter based on the dual-pattern bat algorithm is used to form the adaptive interacting multiple model method. The experimental results show that the proposed algorithm has better comprehensive performance than the IMM-PF.