182 results about "Unscented kalman filtering" patented technology

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention discloses a traveling wave fault positioning system and method based on Beidou synchronization and intelligent filtering, and belongs to the technical field of power system fault detection. The system comprises a Beidou synchronization module used for acquiring a high-precision time service signal and realizing multi-terminal clock consistency; the traveling wave sampling module is used for synchronously collecting multi-point electric power signals and filtering the multi-point electric power signals; the wave head extraction module realizes wave head identification through a sliding window and dynamic threshold judgment; the time coding module constructs a time difference vector and maps a position relation in combination with a power grid topology; the fault positioning module estimates an initial position based on a nonlinear propagation model, and the intelligent filtering module carries out dynamic correction through state prediction and unscented Kalman filtering. The method realizes rapid and accurate positioning of fault points, has the advantages of high synchronization precision, strong anti-interference capability, small positioning error and the like, and is suitable for traveling wave fault positioning requirements in various electric power scenes.

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

The invention discloses a ship-shore cooperative tracking and positioning method based on multi-modal sensor fusion, and belongs to the technical field of target positioning. The method comprises the steps that multi-sensor layout and sensor fusion calibration are carried out on a target ship set and a shore end respectively, multi-view image sequence data and three-dimensional point cloud data are obtained, and the target ship set comprises a plurality of target ships; performing data fusion based on the multi-view image sequence data and the three-dimensional point cloud data to obtain fusion data of the target ship set, establishing an adaptive motion state model and an adaptive observation model based on the fusion data, and performing state prediction, state updating, data association and tracking management on the target ship set by adopting an unscented Kalman filtering algorithm; and establishing a space-time diagram model based on the Kalman filtering fusion observation factor and the Kalman filtering state prediction factor, and performing pose optimization on the target ship set in combination with the GPS factor. According to the method, the accuracy of ship and ship-shore cooperative positioning is improved.

The invention discloses a CAN bus intrusion detection method based on adaptive unscented Kalman filtering, and the method comprises the steps: obtaining real-time message data, and carrying out the preprocessing of the real-time message data, and obtaining a time sequence feature vector; constructing a nonlinear state space model based on the time sequence feature vector; performing unscented Kalman filtering state prediction based on the nonlinear state space model; inputting the time sequence feature vector as an actual observation value, calculating a Kalman gain to correct an unscented Kalman filtering state prediction result, and outputting a state estimation residual error; dynamically updating a process noise covariance matrix through exponentially weighted moving average based on the state estimation residual, and adjusting a measurement noise covariance matrix according to the measurement innovation sequence; calculating the mahalanobis distance of the state estimation residual error, comparing the mahalanobis distance with a self-adaptive anomaly detection threshold value, and judging whether an intrusion behavior occurs or not; and if the abnormal score exceeds a threshold value, triggering a multi-level alarm mechanism, recording a suspicious message and executing a safety protection operation.

The embodiment of the invention provides an aircraft motion state determination method based on Lie group unscented Kalman filtering, and the method comprises the steps: building a state model of a target aircraft, and a measurement model of the target aircraft; obtaining a final Lie group state estimation value of the target aircraft through iterative calculation according to the state model and the measurement model; and determining the motion state of the target aircraft according to the final Lie group state estimation value. According to the technical scheme, a Lie group unscented Kalman filtering navigation framework based on external measurement information is provided, specific forms of sigma point error propagation on Lie algebra and state updating on the Lie group are given, and a self-adaptive updating method of a state noise covariance matrix and a measurement noise covariance matrix is designed. Therefore, the self-adaptive Lie group unscented Kalman filtering method is provided, and the multi-sensor multi-beacon target state estimation precision is greatly improved by means of the self-adaptive Lie group unscented Kalman filtering method.

The invention provides a multi-source data fusion self-positioning method and system. The method comprises the following steps: acquiring GPS positioning data, visual image data and laser radar point cloud data of an unmanned aerial vehicle; carrying out noise reduction preprocessing on the GPS positioning data by adopting an unscented Kalman filtering algorithm; performing sensor joint calibration based on a visual image and a laser radar point cloud, and establishing a geometric mapping relation between a camera coordinate system and a world coordinate system by retrieving a preset high-precision tower ledger library and solving a PnP problem; inputting the image target detection data, the GPS state estimation value and the geometric mapping data into a pre-trained auto-encoder regression network; anti-interference potential features are extracted through an encoder of the network, and a target position estimation value of the target space position of the unmanned aerial vehicle is output through a regression head. According to the invention, the problem of positioning drift caused by strong electromagnetic interference and complex landform in electric power inspection is effectively solved.

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The invention discloses a pumped storage unit fault diagnosis method based on multi-modal data fusion, and the method comprises the steps: collecting a voiceprint signal, an infrared thermal imaging image and historical operation data of a pumped storage unit, and carrying out the noise reduction of a voiceprint set through employing an improved unscented Kalman filtering algorithm, a COMRes + model is used to train the voiceprint signal after noise reduction and historical operation data, a future voiceprint signal is predicted, an improved Deeplabv3 + model is used to train an image set, and features of an infrared thermal imaging image are extracted; the voiceprint feature, the historical operation data text feature and the image feature are input into an improved CentraNet model for multi-modal data fusion, and a fault category is output through a classification recognition module, so that diagnosis of the pumped storage unit fault is completed; according to the method, through fusion of the multi-modal data and optimization of the deep learning model, the accuracy and efficiency of fault diagnosis can be effectively improved, and a powerful guarantee is provided for safe operation of the pumped storage unit.

The invention provides an energy storage system electric quantity state estimation platform and method based on improved unscented Kalman filtering, and relates to the technical field of electric quantity state estimation, and the method comprises the steps: carrying out the reference calibration through recognizing a standing condition, and matching a standing terminal voltage with an OCV-SOC curve to generate a three-dimensional calibration vector; performing space-time correlation on the real-time current, the voltage and the battery temperature with the reference points, predicting an SOC value by using unscented Kalman filtering, calculating a time attenuation weight and a space distance weight of each candidate reference point, and constructing a composite weight coefficient to form an effective reference set; and dynamically adjusting the covariance matrix of the process noise and the observation noise of the UKF based on the composite weight coefficient of the effective reference set, and updating the SOC estimated value. And establishing a dynamic safety evaluation domain on the SOC-temperature plane, and triggering early warning when an estimated value continuously exceeds a confidence interval for three times. According to the invention, the accuracy of electric quantity state estimation and the system security are improved.

The invention belongs to the technical field of power system operation control, and relates to a real-time estimation method and system for comprehensive inertia containing a network construction VSG, a storage medium and electronic equipment, and the method comprises the following steps: S1, constructing a system model; s2, inertia constant state conversion is carried out; s3, mutation detection and correction are carried out; aiming at the problem of real-time evaluation when inertia sudden change occurs in a power system, on the basis of an improved unscented Kalman filtering algorithm, conversion from parameter estimation to real-time state estimation and verification and correction of inertia sudden change conditions are realized, the real-time performance and accuracy of an inertia evaluation result are ensured, and the real-time performance of the power system is improved. And the evaluation method of inertia real-time estimation is enriched.

The invention relates to a self-adaptive longitudinal vehicle speed estimation method, which comprises the following steps of: establishing detailed vehicle dynamic models, including a vehicle longitudinal dynamic model and a wheel speed dynamic model; a Sage-Husa self-adaptive unscented Kalman filtering algorithm is combined with a vehicle dynamics model, a state equation and an observation equation are designed, the longitudinal vehicle speed of a vehicle serves as a state variable, and observation vectors are constructed through data collected by a wheel speed sensor, an acceleration sensor and the like; divergence calculation is introduced for detecting and correcting divergence phenomena in the filtering process; and on the basis of a self-adaptive mechanism of a Sage-Husa algorithm, the filtering gain is adjusted in real time, and the vehicle speed estimation process is optimized. The improved Sage-Husa adaptive filter can dynamically adjust the filter parameters according to the change of the system state and the difference of noise, detects transient disturbance in combination with divergence calculation, reduces the influence of the transient disturbance on longitudinal vehicle speed estimation, and effectively improves the estimation precision.

The invention belongs to the technical field of battery management systems, and particularly relates to a battery state-of-charge estimation method for resisting a voltage sensor fault, which comprises the following steps: S1, data acquisition and system initialization; s2, state prediction; s3, measurement updating and online compensation are carried out; and S4, recursive circulation is carried out. According to the method, the direct-current bias quantity generated by a voltage sensor due to zero drift is widened into a state variable of a battery model, and the state variable, ohm internal resistance, polarization internal resistance, polarization voltage and charge state of a battery jointly form an expansion state vector; and performing real-time recursive estimation on the extended state vector by adopting an unscented Kalman filtering algorithm, and simultaneously obtaining an estimated value of the offset of the sensor and an estimated value of the SOC of the battery subjected to deviation compensation. Through combination of state extension and unscented Kalman filtering, fault bias of the voltage sensor can be identified and compensated online, non-Gaussian noise interference is effectively suppressed, and accuracy and robustness of battery SOC estimation in a complex industrial environment are remarkably improved.

The invention provides a road adhesion coefficient estimation method based on an adaptive slip speed tire model. The method comprises the following steps: establishing a vehicle dynamics model; estimating a tire force based on vehicle dynamics; estimating the vehicle speed by using an unscented Kalman filtering algorithm based on singular value decomposition; a tire model based on the slip speed is constructed, and normalized tire force is calculated according to the tire model; estimating a road adhesion coefficient by using an unscented Kalman filtering algorithm based on singular value decomposition; according to the self-adaptive slip speed tire model provided by the invention, the problem of denominator singularity of a current slip rate-based tire model at a low speed and the limitation of a fixed stiffness parameter are overcome, so that the estimation precision and the response speed of the road adhesion coefficient are improved.

The invention discloses an underground pipeline internal integrated positioning method based on multi-sensor cooperation, and belongs to the technical field of navigation positioning. The method comprises the following steps: correcting a yaw angle based on a magnetometer, performing attitude compensation on a Z axis through the magnetometer, and effectively eliminating an integral error caused by performing attitude calculation only depending on a gyroscope; on the basis of displacement estimation of multi-redundancy odometers, four wheels at the bottom of the robot are each provided with a motor encoder, the four odometers can complement one another, and position estimation of the IMU is effectively corrected through more reliable data; based on data fusion of bidirectional unscented Kalman filtering, non-linear errors are avoided through unscented Kalman filtering, and a smooth trajectory with higher precision can be obtained in cooperation with bidirectional filtering. According to the invention, the underground pipeline track positioning precision in a satellite signal rejection environment can be effectively improved.

The invention discloses a high-voltage circuit breaker state monitoring and risk prediction method, and the method comprises the steps: carrying out the dynamic calibration of a health degree model based on an electromechanical-thermoelectric-material coupling differential equation set and unscented Kalman filtering through synchronous collection of mechanical, electrical, environmental and insulation characteristic quantities; and adaptive dynamic threshold optimization is realized by combining kernel density clustering and a deep Q network, identity verification is completed by using operation fingerprint cosine distance comparison, and finally a precise risk early warning instruction is generated. According to the method, the problems of poor multi-parameter synchronism, threshold staticization, identity verification deficiency and the like in traditional monitoring are solved, the fault recognition accuracy and the early warning coverage rate are remarkably improved, and the operation and maintenance cost and the power grid power failure risk are reduced.

The invention belongs to the technical field of lithium ion batteries, and discloses a novel lithium ion battery estimation method and system considering the core temperature, and the method comprises the steps: building a thermoelectric coupling model of a lithium ion battery, the thermoelectric coupling model comprises an electric model and a thermal model, respectively determining parameters of the electric model and the thermal model by adopting a maximum likelihood method MLM and an adaptive genetic algorithm AGA; on the basis of the thermoelectric coupling model with the determined parameters, an unscented Kalman filter UKF is adopted to estimate the SOC, the terminal voltage and the battery nuclear temperature of the lithium ion battery, a basis is provided for energy management and service life prolonging of the lithium battery, and the safety of the lithium battery at the high temperature can be improved.

The invention relates to the technical field of dynamic object pose recognition and mechanical arm grabbing control algorithms, and discloses a dynamic object pose recognition and mechanical arm grabbing control algorithm. The framework comprises a multi-source heterogeneous sensing fusion module, a motion state joint estimation engine, a grabbing feasibility online evaluator and a rolling time domain grabbing controller, and low-delay collaboration is achieved through unified state space modeling and event-driven scheduling. Wherein the sensing module fuses RGB-D and inertial data to output a six-degree-of-freedom pose with covariance, the estimation engine adopts self-adaptive unscented Kalman filtering to recursion a complete motion state, the evaluator combines dynamic constraints to screen feasible grabbing postures, and the controller solves an optimal joint trajectory with obstacle avoidance constraints on line based on a self-adaptive target trajectory. And feedforward compensation is introduced to suppress dynamic disturbance. According to the technical scheme, the real-time performance is guaranteed, and meanwhile the grabbing success rate and robustness of high-speed, non-uniform-speed and sudden turning dynamic objects are remarkably improved.

The invention discloses a three-dimensional underwater target tracking method based on improved particle filtering, which belongs to the technical field of underwater wireless sensor networks, and comprises the following steps: collecting three-dimensional target tracking data of an underwater target; constructing a target tracking model and a measurement model according to the three-dimensional target tracking data; the method comprises the following steps: improving a particle filtering algorithm based on unscented Kalman filtering, constructing an importance density function based on the improved particle filtering algorithm, a target tracking model and a measurement model, and re-sampling particles; a dynamic adaptive hierarchical weight factor is introduced to correct different particle weights, and normalization processing is performed on the corrected particle weights; performing anomaly detection on the three-dimensional target tracking data based on an optimized Grubbs criterion; and fusing the data of the trusted nodes by adopting an information entropy weighting strategy to obtain a fused target state estimation value, and realizing three-dimensional tracking of the underwater target. According to the invention, high-precision and high-reliability underwater target three-dimensional tracking can be realized.

The invention discloses a spherical simplex robust unscented Kalman filter and a PWM (Pulse Width Modulation) rectifier control method and device, which are characterized in that (2n + 1) Sigma points in standard unscented Kalman filtering are replaced by (n + 2) simplex vertexes which are symmetrically distributed on a unit hyper-sphere, so that the calculation complexity is remarkably reduced. A covariance matrix and a state quantity are dynamically adjusted in combination with a corrected observation noise covariance matrix and an adaptive factor adjustment mechanism, so that the estimation precision of the filter on the lumped disturbance quantity is remarkably improved, and the robustness and the tracking performance of the system on non-Gaussian noise and abnormal observation values are enhanced. And estimating the lumped disturbance quantity of the system in real time through spherical simplex robust unscented Kalman filtering, introducing the lumped disturbance quantity into a cost function for optimization solution, and selecting an optimal voltage vector to control the on-off state of the rectifier. According to the invention, the total harmonic distortion rate of the output current and the voltage fluctuation of the direct-current bus are obviously reduced under the steady-state condition, and higher response speed and stronger anti-interference capability can be shown under the dynamic working condition.

The invention provides a condenser state estimation method based on adaptive noise square root Kalman filtering. Firstly, mathematical models of a condenser evaporation area, a hot well water area and a cooling pipe area are established according to the operation mechanism of a thermal power generating unit condenser; an unscented Kalman filtering algorithm is selected as a basic algorithm for state estimation of a condenser system, and then an optimal adaptive noise square root unscented Kalman filtering algorithm is constructed. The method comprises the steps of filtering initialization, Sigma point set generation, time updating, Sigma point set reconstruction, measurement updating, filtering updating, adaptive factor construction, cross covariance matrix / square root correction, fading forgetting factor calculation and noise covariance matrix correction. And finally, estimating the pressure state and the liquid level state of the condenser by using an optimal adaptive noise square root unscented Kalman filtering algorithm.

The invention relates to the field of unmanned aerial vehicle navigation and control, and particularly discloses an unmanned aerial vehicle three-dimensional track prediction method based on a pre-distribution state estimation model and particle swarm optimization, which comprises the following steps: initializing a particle set, and calculating an initial state mean value and a covariance matrix by adopting unscented Kalman filter (UKF); predicting the state of the particles by using unscented Kalman filter (UKF); sampling the particles based on the state distribution predicted by the UKF; particle weights are calculated and normalized; carrying out resampling on the particles; and calculating a three-dimensional track estimation value of the unmanned aerial vehicle and predicting a future track. According to the method for predicting the three-dimensional flight path of the unmanned aerial vehicle, high-precision state estimation of the UKF is introduced on the basis of particle filtering, so that the algorithm can effectively solve the problems of particle degradation and sample depletion encountered by a traditional PF algorithm in the three-dimensional flight path prediction of the unmanned aerial vehicle, and the prediction precision and stability are improved; and the future trajectory of the unmanned aerial vehicle can be accurately predicted in a complex three-dimensional environment.

The invention discloses an improved optimization link state routing protocol for an unmanned aerial vehicle cluster under a hybrid communication architecture, the unmanned aerial vehicle cluster comprises a plurality of unmanned aerial vehicle nodes, and each node is simultaneously equipped with an omnidirectional RF transceiver antenna and a directional FSO transceiver; in the routing process, the RF link is used for controlling stable distribution of messages, and the FSO link is used for high-speed transmission of service data; a multi-index multi-point relay node comprehensive selection algorithm is designed, and message propagation is optimized and controlled by combining node coverage, average two-hop link stability and relay load indexes; establishing a real-time link quality prediction mechanism based on unscented Kalman filtering, and generating a routing path of stability perception; and a routing compression method adaptive to heterogeneous link propagation characteristics is developed, and redundant hops are eliminated. Through the improved OLSR protocol, the problems of topological response lagging, insufficient link stability and low heterogeneous network efficiency of a traditional routing protocol under a hybrid communication architecture are effectively solved.

The invention provides an FSO multi-sensor fusion method based on unscented Kalman filtering and an RBF neural network, and the method comprises the steps: carrying out the unscented Kalman filtering time updating, and carrying out the measurement of a noise covariance adjustment mechanism and an event triggering mechanism based on light intensity self-adaption; the unscented Kalman filter measures and adjusts confidence weights of the infrared camera and the four-quadrant detector according to the channel state provided by the avalanche photodiode, and processes data output by the infrared camera and the four-quadrant detector according to the confidence weights; then, the posterior state estimation of the unscented Kalman filter at the current moment is output, and a position component is extracted and transmitted. Normalized light intensity reflecting channel quality is used as a key state to carry out synchronous estimation, an RBF neural network is used to carry out dynamic modeling and compensation on nonlinear errors of a four-quadrant detector, and a self-adaptive unscented Kalman filtering framework with a line learning capability is constructed. And finally, a virtual sensor output with high update rate, high precision and high sensitivity is generated.

The invention discloses a multi-sensor fusion road adhesion coefficient prediction method and equipment, and the method comprises the steps: obtaining vehicle state data and road environment data, carrying out the multi-modal feature extraction based on a convolutional neural network, carrying out the deep fusion of the multi-modal features through a feature fusion algorithm, and carrying out the prediction of a road adhesion coefficient. Obtaining a first road adhesion coefficient prediction result based on the convolutional neural network; based on an unscented Kalman filtering algorithm, obtaining a second road adhesion coefficient prediction result based on vehicle dynamics; and carrying out space-time synchronization on prediction results obtained by the two methods, establishing a fusion mechanism, and effectively fusing the road adhesion coefficient prediction results obtained by different methods through a fuzzy algorithm. According to the method, the fusion mechanism of confidence analysis is established, the Cage-Based method and the Eject-Based method are effectively fused, the advantages of the two methods are complemented, and a new thought is provided for solving the problem that the prediction precision of the road adhesion coefficient is low under the complex working condition.

The invention provides an adaptive optimization compensation method for dynamic errors of an inertial navigation system. The method comprises the following steps: obtaining original measurement data of a gyroscope and an accelerometer through measurement and navigation calculation of the inertial navigation system; inputting the original measurement data into a self-adaptive unscented Kalman filter, and carrying out self-adaptive unscented Kalman filtering; time is updated, state prediction is carried out, and observation prediction is carried out; calculating an innovation sequence, and setting a sliding window to calculate an innovation covariance estimated value; updating an observation noise covariance matrix according to the theoretical innovation covariance in combination with an innovation covariance estimated value, and updating a process noise covariance matrix according to residual analysis; updating measurement; the gyroscope zero offset estimation value and the accelerometer zero offset estimation value which are output through filtering are used for compensating gyroscope and acceleration original measurement data. According to the method disclosed by the invention, the high-precision online estimation and compensation of the time-varying error of the inertial navigation system are realized by constructing a self-adaptive filtering framework integrating a dynamic model and data driving, and the navigation precision and stability of the system in a high-dynamic environment are improved.

The invention discloses a multi-source navigation data fusion sensing method and system for ship piloting in a low-visibility environment, and relates to the technical field of ship piloting. Multi-source data acquisition systems such as a ship-borne radar, an AIS, an optical fog-penetrating camera, a hydro meteorological detector and an airborne SAR radar are constructed; and constructing a low-visibility image enhancement model in combination with a generative adversarial network (GAN) to realize all-day holographic perception enhancement. A cross-domain multi-modal data fusion method is researched, space-time synchronization and trajectory feature fusion of radar, AIS and video data are realized by using fuzzy mathematics, unscented Kalman filtering and a deep auto-encoding network, and target identity matching and motion state estimation precision is improved. In order to solve the problem of small target recognition, a head-up-down double-view-angle fusion strategy is adopted, adaptive affine transformation, anchor frame clustering and multi-level feature weighted fusion technologies are integrated, and precise recognition of the small target in the port water area is achieved. Research results can effectively reduce piloting operation risks and improve navigation efficiency.

The invention provides a method for estimating the state of charge of a marine lithium iron phosphate battery based on model compensation and improved Kalman filtering. The method comprises the following steps: acquiring voltage and current data of the marine lithium iron phosphate battery; constructing a novel compensation model taking an improved Play hysteresis operator as a core, and quantifying the hysteresis voltage of the battery; on the basis of a second-order RC equivalent circuit model, hysteresis voltage is used as voltage input of an ideal voltage source in the battery and is injected into model internal resistance and capacitance parameter values obtained through identification at different temperatures, and an equivalent circuit model considering hysteresis at different temperatures is formed; performing parameter identification on the equivalent circuit model with hysteresis compensation by adopting an adaptive forgetting factor recursive least square method, and adaptively optimizing a forgetting factor and an initial parameter value, so that an error between a terminal voltage value simulated by the equivalent circuit model and an actual voltage value is minimum; and carrying out joint estimation on the battery end voltage and the SOC by adopting an adaptive square root unscented Kalman filtering algorithm, so that the error of the end voltage is minimum, and an estimated value of the battery SOC is obtained.

The invention discloses a GEO binocular orbit determination method and system based on inter-satellite communication, and belongs to the technical field of aerospace, and the method comprises the steps: predicting the priori estimation of the state variable of a binocular orbit determination system through unscented transformation according to an orbit dynamics model of the binocular orbit determination system; simulating and generating space-based observation data based on an observation model of a binocular orbit determination system; according to the principle of unscented Kalman filtering, the priori estimation of the state variable and the space-based observation data are fused, and the posteriori estimation of the state variable of the binocular orbit determination system is obtained; repeatedly iterating until the posteriori estimation orbit determination precision of the state variable of the binocular orbit determination system meets the task requirement; according to the method, target observation data is obtained through binocular observation, observation data between observation satellites is obtained by considering inter-satellite communication, prediction data and the observation data are fused through an unscented Kalman filtering algorithm, and a high-precision orbit determination method is provided for a GEO spacecraft.

The invention discloses a self-adaptive vehicle navigation filtering method and device, and belongs to the technical field of intelligent driving. The method comprises the following steps: constructing a nonlinear uncertainty tracking system model according to the mutability of an actual vehicle tracking state, the nonlinearity of a sensor measurement equation and the non-Gaussian property of a measurement error; aiming at a nonlinear uncertainty tracking system model, combining strong tracking filtering and a maximum entropy criterion, and constructing a cost function for estimating the optimal state of the unmanned vehicle; determining a fading factor in the cost function based on the orthogonality of the measurement residual sequence; constructing a novel adaptive navigation filtering algorithm by combining an unscented Kalman filtering framework according to the cost function and the fading factor; and carrying out data processing on the unmanned vehicle tracking system according to the constructed adaptive navigation filtering algorithm. According to the method, the problems of mutability, nonlinearity, non-Gaussian property and the like in a nonlinear tracking system can be inhibited at the same time, and the tracking precision and reliability of the unmanned vehicle in a complex environment are improved.