Navigation Satellite Research Articles

Adaptive algorithms for drone flight control under communication constraints and information incompleteness

Abstract With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

Mapping the ionosphere with millions of phones.

The ionosphere is a layer of weakly ionized plasma bathed in Earth's geomagnetic field extending about 50-1,500 kilometres above Earth1. The ionospheric total electron content varies in response to Earth's space environment, interfering with Global Satellite Navigation System (GNSS) signals, resulting in one of the largest sources of error for position, navigation and timing services2. Networks of high-quality ground-based GNSS stations provide maps of ionospheric total electron content to correct these errors, but large spatiotemporal gaps in data from these stations mean that these maps may contain errors3. Here we demonstrate that a distributed network of noisy sensors-in the form of millions of Android phones-can fill in many of these gaps and double the measurement coverage, providing an accurate picture of the ionosphere in areas of the world underserved by conventional infrastructure. Using smartphone measurements, we resolve features such as plasma bubbles over India and South America, solar-storm-enhanced density over North America and a mid-latitude ionospheric trough over Europe. We also show that the resulting ionosphere maps can improve location accuracy, which is our primary aim. This work demonstrates the potential of using a large distributed network of smartphones as a powerful scientific instrument for monitoring Earth.

Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation

The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite–ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system’s reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40×10−15 and 2.30×10−15 for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively.

Enhanced Forecasting of Global Ionospheric Vertical Total Electron Content Maps Using Deep Learning Methods

The ionospheric state holds significant implications for satellite navigation, radio communication, and space weather; however, precise forecasting of the ionosphere remains a formidable challenge. To improve the accuracy of traditional forecasting models, we developed an enhancement model based on the CODE and IRI forecasting methods, termed the Global Ionospheric Maps Forecast Enhancement Model (GIMs-FEM). The results indicated that by extracting the GIM features from existing forecasts and incorporating additional proxies for geomagnetic and solar activity, the GIMs-FEM provided stable and reliable forecasting outcomes. Compared to the original forecasting models, the overall model error was reduced by approximately 15–17% on the test dataset. Furthermore, we analyzed the model’s performance under different solar activity conditions and seasons. Additionally, the RMSE for the C1pg model ranged from 0.98 TECu in the solar minimum year (2019) to 6.91 TECu in the solar maximum year (2014), while the enhanced GIMs (C1pg) model ranged from 0.91 to 5.75 TECu, respectively. Under varying solar activity conditions, the RMSE of GIMs-FEM for C1pg (C2pg) ranged from 0.98 to 6.91 TECu (0.96 to 7.26 TECu). Seasonally, the GIMs-FEM model performed best in the summer, with the lowest RMSE of 1.9 TECu, and showed the highest error in the autumn, with an RMSE of 2.52 TECu.

Serial Multicode Direct Sequence Spread Spectrum With Applications to Satellite Navigation Pilot Channels

Serial Multicode Direct Sequence Spread Spectrum With Applications to Satellite Navigation Pilot Channels

Frequency dependence of reflections on radar landmarks

Purpose. Reducing the dispersion of radar reflections from local objects, with multi-frequency sensing, to solve the problem of orientation by radar reflections from objects. Methodology. Reflections from local objects in the entire frequency range of the radar station (RS) at the same radio engineering position were measured by three independent radars of the same type on different days and at different antenna elevations. The deviation of the radar stations in the position did not exceed 500 meters. Coordinates (azimuth, range) of reflections of several separate local objects were allocated for each radar. The average values of reflections from local objects and their dispersion in the frequency range were calculated. Using various algorithms, individual frequencies were sampled and the reflected signals were averaged at these frequencies. The decrease in the dispersion of the reflected signal from the number of frequencies at which reflections were measured and from the algorithm for selecting these frequencies was investigated. Findings. Averaging the values of reflections from local objects for several frequencies leads to a decrease in dispersion and, as a result, to a more accurate correspondence of the reflected signal level to the geometric size of the local object. The variance decreases most rapidly for a small number of frequencies selected for averaging when selecting frequencies located in an interval of at least 1% relative to each other. Originality. To solve the problem of orientation based on radar reflections from local objects, it is necessary to identify the landmarks selected on a digital terrain model. Due to the fact that local objects (hills) are a collection of many reflectors falling into the allowed volume of the radar, with different levels of reflections and random phases, there may not be radar reflection from a local object at a certain frequency, or it may be very small. In order to unambiguously identify all landmarks, measurements must be carried out at several frequencies. The work has established how many frequencies measurements should be performed at and on what principle these frequencies should be selected. Practical value. The advent of digital terrain models made it possible to solve the problem of terrain orientation by comparing radar reflections from local objects with reflection models based on digital terrain maps. Radar reflection models use mathematical expectations of reflection values, unlike real reflections, which have random deviations in signal levels depending on the operating frequency. Reducing the variance of these deviations increases the accuracy of identifying characteristic local objects (landmarks) used to orient the radar in the absence of data from satellite navigation systems.

Analytical Second-Order Extended Kalman Filter for Satellite Relative Orbit Estimation

This study considers a relative orbit estimation problem wherein an observing spacecraft navigates with respect to a target space object at a large separation distance (several kilometers) using only the bearing angles obtained by a single onboard camera. Generally, the extended Kalman filter (EKF), which is based on linear relative motion equations such as the Clohessy–Wiltshire equation, is used for the relative navigation of satellites. The EKF linearizes the estimation error around the current estimate and applies the Kalman filter equations to this linearized system. However, it has been shown that nonlinearities of the orbit determination problem can make the linearization assumption insufficient to represent the actual uncertainty. Therefore, an analytical second-order extended Kalman filter (ASEKF) for relative orbit estimation is proposed in this study. The ASEKF, to sequentially estimate the relative states of satellites and their associated uncertainties, is formulated based on a second-order analytic relative-motion equation under J2-perturbtation, which can overcome the deficiencies of existing approaches that mainly focus on applications in two-body, near-circular, and linearized orbit dynamics. Numerical results show that the proposed method provides superior robustness and mean-square error performance compared to linear estimators under the conditions considered.

Estimating the Orbital Parameters of a Nanosatellite When Measuring the Total Electron Content in the Ionosphere Based on the Retransmission of GPS Navigation Signals

A method for estimating the orbital parameters of a nanosatellite-relay, implemented by measuring the total electron content in the ionosphere based on relaying GPS navigation signals at frequencies of 150/400 MHz, is substantiated. The method involves estimating the Cartesian coordinates of the nanosatellite-relay based on the results of measurements of the total ranges “navigation satellite – nanosatellite relay – ground receiving point”, obtaining estimates of the orbital plane inclination angle and the longitude of the ascending node using the least squares method, and estimating the remaining orbital parameters using the maximum likelihood method. Simulation results and accuracy characteristics of the proposed method are presented. It is shown that for a typical signal-to-noise ratio in the equipment of the receiving point and an observation time of the nanosatellite-relay of 600–800 s, the mean square errors in estimating the orbital parameters of the nanosatellite-relay are: for angular quantities – fractions of arc seconds, the semi-major axis of the ellipse – 4–5 m, eccentricity – 2–3 ppm.

Optimal design of the long-term LEO almanac by considering the atmosphere drag effects

ABSTRACT Almanac is an elegant design to help Global Navigation Satellite Systems (GNSS) signal acquisition, which has been adopted by all global satellite navigation systems. For the future low Earth orbit (LEO) navigation constellation, the almanac will be more critical due to larger constellation and signal Doppler variation, while the LEO orbit prediction is more challenging due to complex perturbation forces, especially atmospheric drag. This study introduces a novel 9-parameter almanac model for LEO constellation, leveraging the Empirical Mode Decomposition (EMD) method. Our innovative approach uniquely balances almanac accuracy and simplicity by considering the atmosphere drag effects, addressing a critical gap in existing models. The proposed almanac model is validated with four in-orbit LEO satellites at various altitudes. For the LEO with a 600 km orbit or lower, our model achieves a 50% improvement in 23-day orbit prediction accuracy compared to the classical GPS almanac model. This significant enhancement extends to visibility prediction and signal acquisition performance. Our design limits the maximum elevation angle error to less than 1.5 degrees, efficiently excluding invisible satellites during LEO navigation signal acquisition. Furthermore, for visible satellite, the proposed LEO almanac reduces the signal frequency searching range by about 20 times during acquisition, with Doppler prediction errors better than 0.5 kHz/0.3 kHz for 95% of cases. These advancements represent a substantial leap in LEO navigation technology, offering improved efficiency and accuracy for future satellite systems.

Spatiotemporal Information, Near-Field Perception, and Service for Tourists by Distributed Camera and BeiDou Positioning System in Mountainous Scenic Areas

The collaborative use of camera near-field sensors for monitoring the number and status of tourists is a crucial aspect of smart scenic spot management. This paper proposes a near-field perception technical system that achieves dynamic and accurate detection of tourist targets in mountainous scenic areas, addressing the challenges of real-time passive perception and safety management of tourists. The technical framework involves the following steps: Firstly, real-time video stream signals are collected from multiple cameras to create a distributed perception network. Then, the YOLOX network model is enhanced with the CBAM module and ASFF method to improve the dynamic recognition of preliminary tourist targets in complex scenes. Additionally, the BYTE target dynamic tracking algorithm is employed to address the issue of target occlusion in mountainous scenic areas, thereby enhancing the accuracy of model detection. Finally, the video target monocular spatial positioning algorithm is utilized to determine the actual geographic location of tourists based on the image coordinates. The algorithm was deployed in the Tianmeng Scenic Area of Yimeng Mountain in Shandong Province, and the results demonstrate that this technical system effectively assists in accurately perceiving and spatially positioning tourists in mountainous scenic spots. The system demonstrates an overall accuracy in tourist perception of over 90%, with spatial positioning errors less than 1.0 m and a root mean square error (RMSE) of less than 1.14. This provides auxiliary technical support and effective data support for passive real-time dynamic precise perception and safety management of regional tourist targets in mountainous scenic areas with no/weak satellite navigation signals.

Intelligent algorithmic framework for detection and mitigation of BeiDou spoofing attacks in vehicular ad hoc networks (VANETs)

This research tackles the critical challenge of BeiDou signal spoofing in vehicular ad-hoc networks and addresses significant risks to vehicular safety and traffic management stemming from increased reliance on accurate satellite navigation. The study proposes a novel hybrid machine learning framework that integrates Autoencoders and long short-term memory (LSTM) networks with an advanced cryptographic method, attribute-based encryption, to enhance the detection and mitigation of spoofing attacks. Our methodology leverages both real-time and synthetic navigational data in a comprehensive experimental setup that simulates various spoofing scenarios to test the resilience of the proposed system. The findings demonstrate a significant improvement in the accuracy of spoofing detection and the robustness of mitigation strategies by ensuring the integrity and reliability of navigational data. This investigation enhances the existing body of knowledge by demonstrating the effectiveness of integrating machine learning with cryptographic techniques to secure VANETs. Ultimately, it effectively paves the way for future research into adaptive security mechanisms that can dynamically respond to evolving cyber threats.

Ionospheric TEC and its irregularities over Egypt: a comprehensive study of spatial and temporal variations using GOCE satellite data

Abstract This study delves into ionospheric characteristics during solar cycle 24 using data from the Global Positioning System (GPS) and Low Earth Orbit (LEO) satellites, GOCE. The present study fills the gap of not assessing and studying GOCE satellite data before in Egypt. Focusing on spatial and temporal variations of ionospheric Total Electron Content (TEC) over Egypt and the Middle East across a 4-year dataset, monthly average Vertical Total Electron Content (VTEC) variations are scrutinized, emphasizing extremes during heightened and reduced solar and geomagnetic activities. Results TEC typically decreases with latitude’s increase, with peak ionization during equinoxes and troughs during solstices. Notably, VTEC values consistently reach maximum values on days of heightened solar and geomagnetic activities. GOCE data is evaluated against International Reference Ionosphere (IRI) model and NeQuick2 model by selecting 10 days from all data by using a statistical comparison via t-tests. There is not significant difference between them except for two days between GOCE-IRI. The values of Root Mean Square Error (RMSE) are 3.7403 and 4.4655 for GOCE – NeQuick2 model and GOCE – IRI model, respectively. Ionospheric scintillation, signifying rapid electron density fluctuations, is assessed through amplitude scintillation index (S4), S4 proxy, and Rate Of TEC Index (ROTI). A robust correlation between S4 and S4 proxy is noted, thus scintillation can be studied by using S4 proxy instead of S4. Temporal variations indicate heightened scintillation during geomagnetic storms and peak solar activity, contrasting with reduced activity during solar minimum. Throughout the study period the maximum scintillation index values for ROTI, S4, and S4 proxy are 0.3, 0.15, and 0.05, indicating minimal scintillation. This comprehensive analysis, rooted in GOCE data, illuminates spatial and temporal dynamics of ionospheric TEC and elucidates ionospheric scintillation characteristics in the Egypt region. These findings are crucial for refining ionospheric models, improving scintillation prediction, and enhancing satellite communication and navigation systems, especially in the study region.

Design and experiment of Beidou satellite navigation railway tunnel coverage enhancement system

Abstract In areas of satellite navigation signals being obstructed, coveraging the interior of the tunnel with Satellite navigation signals based on navigation signals real-time regeneration technology is an important way to achieve the applications of satellite navigation in railway mobile equipment positioning and auxiliary protection for operators. We analyzed the application requirements on navigation positioning and time synchronization, and technical requirements on universality and electromagnetic compatibility, etc. The article designed the composition and logical architecture of the Beidou satellite navigation railway tunnel coverage enhancement system, proposed the design of system functions and performance parameters, and verified the system’s functionality and performance through static mode testing and dynamic mode testing. The design and implementation of the Beidou satellite navigation railway tunnel coverage enhancement system have important practical value for the application in mobile equipment positioning and auxiliary protection for operators in railway tunnel.

Preface

With the rapid development of science and technology, computer, remote sensing technology and aerospace science have increasingly become the focus of attention of academia and industry. Since the first International Conference on Computer, Remote Sensing and Aerospace (CRSA) was held, the conference has become an important platform for scholars in related fields to exchange ideas and share results. The 2024 4th International Conference on Computer, Remote Sensing and Aerospace (CRSA 2024) continues the high-level academic tradition of the previous three conferences, and was held virtually in Osaka, Japan from July 5th to 7th, 2024. The conference aimed to promote the cross-fertilization between computer science, remote sensing technology and aerospace science, to promote technological innovation and industrial upgrading, and to contribute wisdom and strength to the global scientific and technological development. It called for papers on such topics as High-Performance Computing, Computer Aided Design, Robotics and Automation, Geographic Information Science, Satellite Navigation and Positioning, Optical Remote Sensing, Aeroelasticity and Structural Dynamics, Manned / Unmanned Aviation, etc. The conference attracted the active participation of experts, scholars, engineers and industrial elites from all over the world, and the latest research results and technological advances in relevant field were demonstrated through thematic reports, oral and poster presentations. The thematic speeches covered the problems, challenges, solutions, and future research directions of computer science, remote sensing technology and aerospace science, and provided participants with comprehensive and in-depth understanding and insight, stimulating thinking and innovation. Scholars who participated in oral and poster presentations had in-depth exchanges with other participants, sharing experiences and insights, and jointly exploring cutting-edge issues in relevant disciplines and domains. In addition, we have carefully selected and prepared a collection of Proceedings based on this conference, which represents a bunch of excellent papers, covering the latest research achievements and innovative ideas in related fields. We hope that this Proceedings will be an important reference for the academic community, providing researchers with opportunities for in-depth research, and will also inject new vitality and impetus into the development and innovation in the fields of and related to computer science, remote sensing technology and aerospace science. At the same time, we also hope that scholar and researchers will continue to uphold the spirit of science and the courage to explore unknown areas, contributing to the prosperity and progress of mankind. Finally, we would like to express our sincere thanks to all for their support and participation in the conference. We believe that through the exchange and cooperation of this conference, we will jointly create a new future in the fields of computer science, remote sensing technology and aerospace science. We also believe that the publication of this Proceedings will make a positive contribution to the development of the academic community and the progress of aerospace industry! The Committee of CRSA 2024 List of Committee Member is available in this Pdf.

Research on Design and Staged Deployment of LEO Navigation Constellation for MEO Navigation Satellite Failure

Low Earth orbit (LEO) satellites have unique advantages in navigation because of their high signal intensity and rapid geometric changes in a short period. In order to solve the problem of constellation performance degradation after a potential failure pertaining one or more medium Earth orbit (MEO) navigation satellites, this paper designs the LEO navigation constellation and considers the task requirements of different stages of constellation deployment. Firstly, the LEO navigation constellation is designed by a non-dominated sorting genetic algorithm II (NSGA-II). The average position dilution of precision (PDOP) is 1.676, which is an improvement compared to the average PDOP offered by the four traditional GNSS. Secondly, the staged deployment of constellation takes into account the degradation of constellation performance caused by the failure of MEO navigation satellites, and the Monte Carlo method is used to analyze the case of three simultaneous satellite failures. The results show that a single satellite failure within each orbital plane and adjacent satellites with close phase separation has a great impact on the performance of the MEO navigation constellation. On this basis, a staged deployment strategy was adopted in order to balance cost, risk, and performance. The three phases deploy 66, 156, and 288 satellites, respectively; as a make-up constellation under contingencies, a navigation enhancement constellation, and an independent navigation constellation, the deployment of the staged sub-constellations meets the mission requirements. The constellation design and staged deployment method proposed in this paper can provide reference for the future study of LEO navigation constellations.

Research on the meteorological data transmission system of civil aircraft based on BeiDou satellite

Abstract With the official opening of the Beidou-3 global satellite navigation system, Beidou has entered a new era of global services, extending its service area to the whole world. With the advantages of sufficient data volume and high data security, the BeiDou-3 short message satellite communication system can provide global communication services for airlines, realize two-way data transmission between air and land, and effectively reduce the operating costs of airline companies. Aiming at the demand for low-cost civil aircraft weather data transmission proposed by domestic and foreign airline companies, the current development of civil aviation weather information transmission and the current application status of BeiDou in civil aviation were studied, and the BeiDou satellite-based civil aircraft weather data transmission system was designed. The system architecture, detailed functions and data transmission workflow were analyzed, and the message coding structure was designed using bit-based coding technology. Finally, the feasibility of the scheme is verified through the data demand analysis of weather news and the onboard flight test on a domestic aircraft. The results show that t-he BeiDou short message function has the ability to realize the weather data transmission of a civilian aircraft.

Gross error detection method for high-precision data processing of beidou satellite navigation system

Gross error detection method for high-precision data processing of beidou satellite navigation system

Precise baseline determination for InSAR formation-flying satellites based on spaceborne BDS-3 and GPS observations

The precise baseline determination (PBD) is a crucial technique for spaceborne Interferometric Synthetic Aperture Radar (InSAR) formation-flying satellites to successfully carry out scientific missions. The Chinese InSAR formation T-A and T-B satellites are equipped with the multi-GNSS receiver that can track Global Positioning System (GPS) and BeiDou satellite navigation System (BDS-3) signals for PBD. First, we evaluate the data quality of spaceborne GPS and BDS-3 observations and carry out the absolute precise orbit determination (POD) with single-satellite ambiguity fixed, respectively. The result shows that a three-dimensional (3D) POD consistency of 1.48 cm (RMS) between them is reached. Then, the baseline products are obtained based on GPS and BDS-3 observations, respectively. The PBD of GPS-based is assessed via internal consistency checks and external baseline validations, Internally, the overlap comparison is sub-millimeter. Externally, comparisons with external agency is 1.36 mm in 3D. Taking the baseline results based on GPS as a reference, the difference shows that a consistency of PBD result based on BDS-3 is 0.91 mm in 3D. Finally, we perform the GPS + BDS-3 multi-GNSS data fusion for PBD experiment. The result shows that combining multi-GNSS data can not only increase the position precision and obtain a higher overlapping accuracy, but also improve the internal consistency of baseline products.

Employing a two stage process with resampling, decision rules, and fine acquisition to optimize the acquisition stage of GNSS receiver performance

The optimal design of Global Navigation Satellite System (GNSS) software receivers should enable the accurate estimation of the receiver's position, velocity, and time under various environmental conditions. The software receivers consist of three sections: acquisition, tracking, and navigation. This paper specifically centers on the acquisition sections of the Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou, and Galileo satellite navigation systems. The acquisition stage necessitates the prompt and precise transmission of the satellite list within the receiver's view, along with Doppler frequency estimation and phase code offset, to facilitate a seamless transition to the tracking stage with the utmost speed and accuracy. Conventional acquisition demonstrates commendable speed, but lacks accuracy, especially in environments with a low Carrier-to-Noise Ratio (CNR). Contrastingly, precise acquisition methods will impede the speed of the acquisition stage. In this paper, a novel acquisition method is proposed. By integrating concepts like resampling, two-stage acquisition, and fine-acquisition, this method enables the attainment of higher accuracy without compromising speed. The results demonstrate that the proposed method enhances accuracy by 7.275% compared to conventional methods and boosts speed by 76.97% compared to methods focused on accuracy improvement.

Polar Motion Ultra-Short-Term Prediction of Least-Squares+Multivariate Autoregressive Hybrid Method by Using the Kalman Filter.

The polar motion (PM, including two parameters PMx and PMy) ultra-short-term prediction (1-10 days) is demanded in the real-time navigation of satellites and spacecrafts. Improving the PMx and PMy ultra-short-term predictions accuracies are a key to optimize the performance of these related applications. Currently, the least squares (LS)+autoregressive (AR) hybrid method is regarded as one of the most capable approaches for ultra-short-term predictions of PMx and PMy. The Kalman filter has proven to be effective in improving the ultra-short-term prediction performance of the LS+AR hybrid method, but the PMx and PMy ultra-short-term predictions accuracies are still not able to satisfy some related applications. In order to improve the performance of PM ultra-short-term prediction, it is worth exploring the combinations of existing methods. Throughout the existing predicted methods, the LS+multivariate autoregressive (MAR) hybrid method by using the Kalman filter has the potential to improve the accuracy of PM ultra-short-term prediction. In addition, a PM prediction performance analysis of the LS+MAR hybrid method by using the Kalman filter, namely the LS+MAR+Kalman hybrid method, is still missing. In this contribution, we proposed the LS+MAR+Kalman hybrid method for PM ultra-short-term prediction. The data sets for PM predictions, which range from 1 to 10 days, have been tested based on the International Earth Rotation and Reference Systems Service Earth Orientation Parameter (IERS EOP) 14 C04 series to assess the performance of the LS+MAR+Kalman hybrid model. The experimental results illustrated that the LS+MAR+Kalman hybrid method can effectively execute PMy ultra-short-term predictions. The improvement of PMy prediction accuracy can rise up to 12.69% for 10-day predictions, and the improvement of ultra-short-term predictions is 7.64% on average.