294 results about "Extended Kalman filter" patented technology

The invention relates to the field of low-altitude airspace monitoring, and relates to a low-altitude monitoring system based on multi-source heterogeneous perception fusion and rule engine driving, which comprises a perception access layer used for completing data standardization and protocol adaptation and realizing data stream release through message middleware; the processing calculation layer is used for generating a structured alarm event; the application support layer is used for three-dimensional visual interactive display of target situation data and alarm events, unified access of multiple types of sensors such as radar, photoelectricity, radio frequency, ADS-B and meteorological sensors is realized through a plug-in protocol adapter, and target state estimation and track smoothing are realized by combining space-time registration and extended Kalman filtering. The limitation that an existing system can only process a single data source or simple superposition is overcome, and the target recognition precision and the track continuity are remarkably improved.

The invention relates to the technical field of data processing, and discloses a VICTS antenna attitude estimation and correction method and system based on satellite pointing feedback, and the method comprises the steps: constructing a tight coupling depth fusion system with extended Kalman filtering as a frame, introducing satellite communication antenna pointing information as a new observation source on the basis of traditional IMU / GNSS fusion, and carrying out the satellite communication antenna attitude estimation and correction. By deducing the Jacobian matrix of the carrier attitude, effective fusion of the observed quantity is realized, so that the attitude estimation precision and robustness of the carrier are reversely corrected and remarkably improved, the three-axis attitude error is estimated and corrected more comprehensively and steadily, and the problem of lack of roll angle observation is fundamentally solved. Observation information of different dimensions is unified into the same state estimation framework through accurate mathematical modeling, and the precision, autonomy and robustness in a complex environment of the system are greatly improved.

The invention relates to the technical field of forklift driving stability control, in particular to a forklift driving stability control system based on ultrasonic and laser composite ranging, which comprises a composite ranging module, a multi-modal data fusion module, a stability prediction and control module, an execution mechanism control module and an online learning system. The composite distance measuring module collects surrounding environment information and corrects motion errors through a dynamic compensation algorithm; the multi-modal data fusion module dynamically adjusts the weight of the sensor based on an environmental interference factor, and realizes space-time calibration in combination with extended Kalman filtering; the stability prediction and control module is fused with the vehicle dynamic characteristics to predict the rollover risk and generate a hierarchical control instruction; the actuating mechanism control module drives actuating components such as an active suspension and an electronic differential mechanism through a layered framework. The method can adapt to complex working conditions, the prediction precision and fault-tolerant capability are improved, safety and efficiency are balanced, the maintenance cost is reduced, and the method is suitable for various forklifts.

The invention discloses a multi-parameter coupling intelligent regulation and control method and system in a vanadium ore smelting process, relates to the technical field of non-ferrous metal smelting automatic control, and solves the problems of feeding deviation and resource chain scission caused by slow response of an existing heat balance model to ore impurity fluctuation, online sensing lag and process island. The impurity concentration and the dielectric constant are obtained in real time through an LIBS / XRF combined probe and a microwave dielectric spectrum, rheological disturbance is dynamically corrected through extended Kalman filtering, a dynamic coupling response model is input, a thermodynamic interference factor is calculated, and a leaching and precipitation strategy is generated; performing mother liquor tail gas material flow collaborative optimization in parallel, constructing a multi-dimensional control vector U, performing iterative updating through near-end strategy optimization in a digital twin and issuing the multi-dimensional control vector U to an edge PLC (Programmable Logic Controller) in a distributed manner, and finally correcting a model weight through online XRD (X-Ray Diffraction) feedback; according to the method, the adaptive capacity to complex vanadium ore component disturbance, the response precision of online control and the inter-process resource circulation efficiency are remarkably improved.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention discloses a real-time power generation load estimation method and system for photovoltaic power generation, relates to the technical field of photovoltaic power generation load estimation, and aims to solve the problems of state judgment delay and model mismatching and misestimation caused by short-time cloud shadow rapid movement. The method comprises the following steps: collecting power, current, voltage, irradiance and temperature signals through a multi-array sensor, separating a high-frequency characteristic signal from a low-frequency characteristic signal through high-pass / low-pass filtering, and generating a static baseline multi-dimensional characteristic vector; based on the power spectrum entropy, adaptively adjusting a time window and extracting dynamic characteristics; fusing the static and dynamic characteristics to generate discrimination input; performing state judgment by adopting a logistic regression model, and starting extended Kalman filtering online correction when an energy residual exceeds a threshold; and model drift is detected based on CUSUM and Shewhart algorithms, and an incremental training set is automatically constructed to update model parameters. And high-precision and low-delay real-time power generation load estimation and operation and maintenance support under a short-time cloud shadow condition are realized.

The invention provides a three-dimensional tracking method applied to a single-photon laser radar to detect a far-field dynamic unmanned aerial vehicle point target, and belongs to the technical field of laser radar detection and target tracking. The problems that in the prior art, under the long-distance condition, the target imaging size is smaller than one pixel, and a detection and tracking algorithm based on shape, texture or edge features fails are solved. The method comprises the following steps: constructing a six-dimensional state vector containing a three-dimensional position and a three-dimensional speed of a target; predicting a state vector according to a state transfer function containing air resistance and turning acceleration; and an extended Kalman filtering framework is utilized to update the predicted state and obtain state estimation of the target, and the extended Kalman filtering framework comprises a self-adaptive process noise adjustment mechanism based on innovation feedback and is used for adjusting a process noise covariance matrix on line. The method is mainly used in the field of detection and multi-dimensional tracking of airspace moving targets.

The invention relates to an agricultural robot, and discloses an animal husbandry pushing robot autonomous navigation method based on a laser radar, which comprises a microsecond-level multi-sensor time synchronization architecture based on an FPGA (Field Programmable Gate Array); in a navigation layer, seamless switching between the GNSS and the laser SLAM is realized by constructing a virtual fusion observation value by utilizing extended Kalman filtering and a self-adaptive dynamic weight algorithm; in the sensing layer, an RGB-I multi-mode model fused with laser reflectivity and a biological micro-motion detection technology are adopted, and feed and biological obstacles are accurately distinguished; and in the execution layer, flexible self-adaptive material pushing is achieved in combination with material groove edge fitting and force-position hybrid control, and the navigation precision, the operation quality and the safety of the material pushing robot in the all-weather and unstructured pasture environment are effectively improved.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

The invention discloses a micro-action three-dimensional sensing method and system based on WiFi channel state information, and relates to the technical field of wireless signal sensing. The method comprises the following steps: collecting CSI data, preprocessing, and outputting a CSI amplitude matrix and a phase matrix; the phase difference of adjacent antenna pairs is used for counteracting common noise, a linear mapping model of displacement increment and phase change is constructed, and displacement calculation is achieved; designing a space-time TransFormer model, fusing CSI amplitude, phase difference, displacement increment and Doppler frequency shift characteristics through a time sequence and space joint attention mechanism, and outputting an initial three-dimensional trajectory; optimizing the initial three-dimensional trajectory by using a self-supervised learning mechanism based on physical consistency constraint; and finally, constructing a dynamic path filter, carrying out real-time filtering correction on the optimized trajectory in combination with extended Kalman filtering and Bayesian multipath fusion, and outputting a final three-dimensional trajectory, thereby realizing high-precision and low-delay trajectory reconstruction without labeling in a complex scene.

The invention discloses an AGV-six-degree-of-freedom mechanical arm operating system and method based on double-vision fusion, the system comprises a control system and a six-degree-of-freedom mechanical arm, and the control system comprises a master control unit, a slave control unit and a dynamic error compensation module; the method comprises the steps of AGV autonomous navigation and material transfer, cross-station accurate docking and dynamic error compensation, high-precision biological medicine operation under double-vision fusion guidance, and material recovery and circulation. According to the invention, through dual-vision dynamic fusion of eyes outside the hand and eyes on the hand, adaptive switching from global coarse positioning to local fine positioning is realized; due to the integrated design of the AGV and the six-degree-of-freedom mechanical arm, the operation flexibility is greatly improved; an extended Kalman filtering dynamic error compensation algorithm is innovatively adopted, so that the comprehensive error of the AGV, the mechanical arm and the visual system can be corrected in real time; and by combining with a double-layer distributed control architecture, the system realizes multi-task intelligent scheduling and modular collaboration.

The invention provides a Q / R dual-adaptive hybrid quantum filtering method for an OTFS (Over the The Over the File System) communication and inductance integrated system, and the method comprises the following steps: constructing an OTFS communication and inductance integrated signal model, and obtaining a target measurement vector; establishing a state vector for describing a target motion state, and constructing a state transition equation and a measurement equation; the method comprises the following steps: constructing a Q / R mixed quantum adaptive adjustment module based on dual-channel features to construct a dual-channel feature input vector, generating a dual-path adjustment factor through a mixed quantum neural network, and constructing an adaptive Q matrix and an adaptive R matrix; and performing adaptive state prediction and updating at each time step by using the adaptive Q and R matrixes constructed in the step 3 to realize tracking of the motion state of the target. According to the method, the hybrid quantum neural network is introduced for driving, collaborative optimization of filter parameters is achieved, and therefore the precision, robustness and response speed of target tracking are remarkably improved in a complex dynamic noise environment.

The invention discloses a transmission conductor three-dimensional attitude estimation method, system and equipment based on RTK and inertial measurement coupling and a medium, and belongs to the technical field of three-dimensional state monitoring, and the method comprises the following steps: fusing RTK absolute positioning and IMU inertial measurement data, and realizing accurate estimation of the transmission conductor three-dimensional attitude by using extended Kalman filtering; and abnormal identification and risk early warning are carried out based on an attitude calculation result, and finally real-time monitoring and intelligent warning of the state of the power transmission line are realized through a three-dimensional visual platform. According to the method, RTK and IMU data are fused, accurate sensing and continuous tracking of the three-dimensional attitude of the power transmission line are achieved through extended Kalman filtering, the defect that a single technology is insufficient in reliability in a complex environment is overcome, and finally a complete monitoring closed loop of the safety state of the power transmission line is achieved through intelligent early warning and three-dimensional visualization.

The invention discloses an environment monitoring method based on a low-altitude aircraft, belongs to the technical field of low-altitude aircrafts, and realizes high-precision monitoring through cooperation of anti-interference hardware, edge calculation, navigation communication and cloud modeling. According to the system, a hexagonal prism cabin body is integrated with multiple types of sensors, and data are dynamically calibrated through a BP neural network. And aligning the multi-source spatio-temporal data by using extended Kalman filtering, and generating a three-dimensional grid model containing obstacles and a flow field. Navigation adopts Beidou / IMU combination and visual SLAM redundancy switching, and communication is guaranteed through a 5G private network and UWB double links. And the cloud constructs a three-dimensional AQI thermodynamic diagram based on inverse distance weighting and a Kriging algorithm, predicts pollutant diffusion in combination with a random forest, and drives the aircraft to execute a spiral / grid composite route. The problems that traditional monitoring is low in density, complex in model and insufficient in precision are solved, and a full-link anti-interference perception-decision closed loop is achieved.

The invention provides an integrated navigation method combining starlight refraction positioning with inertial navigation in a near space, and belongs to the technical field of starlight refraction navigation positioning. The method comprises the following steps: firstly, determining a state model of starlight refraction positioning in a near space by taking an error equation of an inertial navigation system of a near space aircraft as a state equation; designing a near space starlight refraction positioning algorithm, identifying a refraction star by using a star sensor on the aircraft, acquiring a refraction angle as a measurement, and establishing a measurement model of starlight refraction positioning in a near space; and obtaining the optimal estimation value of the system state variable by using an extended Kalman filter (EKF) method. The method has the advantages of being suitable for near space, high in reliability, high in autonomy and the like, and can be finally used for pure astronomical positioning and astronomical / inertial integrated navigation.

The invention relates to a lunar satellite formation collaborative navigation method, which comprises the following steps of: inputting an orbit state and an observation residual error of a lunar satellite formation at a previous moment into an LSTM (Long Short Term Memory) prediction network of a navigation algorithm to predict a process noise covariance matrix and an observation noise covariance matrix, and multiplying the process noise covariance matrix and the observation noise covariance matrix by a scaling factor for scaling, obtaining a scaled prediction process noise covariance matrix and a scaled observation noise covariance matrix; and inputting the orbit state of the lunar satellite formation at the previous moment, the multi-source observation data at the current moment and the scaled process noise covariance matrix and observation noise covariance matrix into a self-adaptive extended Kalman filtering module in a navigation algorithm to predict the orbit state of the lunar satellite formation at the current moment.

The invention discloses a micro-grid safety monitoring method and system based on attack detection data, and relates to the field of power grid safety monitoring, and the method comprises the steps: outputting a state vector of each node; obtaining a predicted attacked result of each node; outputting an estimated value set and a measured value set of the operation data of each prediction node; and obtaining an attack detection result of each prediction node and executing corresponding operation. According to the method, the data channel safety degree and the multi-dimensional evaluation parameters are fused, a state estimation and attack probability recognition mechanism combining extended Kalman filtering and a time sequence model is introduced, a hierarchical response mechanism is set according to attack levels, the elastic defense and active recovery capability of the system under different attack intensities is improved, and stable operation of the micro-grid is guaranteed.

The invention provides three-dimensional vehicle-mounted navigation equipment and a navigation method based on the equipment. The method comprises the following steps: carrying out equipment initialization and reference calibration, arranging communication equipment on a vehicle roof, arranging acquisition equipment on a vehicle head grid, and arranging a calibration and calculation module in a vehicle cabin; synchronously acquiring multi-source scene and body state data by using hardware; identifying an operation scene through a CNN-LSTM hybrid model, and dynamically allocating a sensor fusion weight based on a reinforcement learning algorithm; a BP neural network is utilized to construct nonlinear mapping of the vibration frequency and the positioning error, and a dynamic error value is calculated; the dynamic error is substituted into extended Kalman filtering for real-time correction, and vibration influence is eliminated; the method comprises the following steps: pre-judging a GNSS signal trend by using an LSTM model, and initializing a backup module in advance to realize multi-mode seamless switching; dynamically adjusting map updating frequency according to environment change and compressing data by using an octree; and outputting a three-dimensional navigation instruction on the display screen. The problems of high-precision positioning and smooth switching of the engineering vehicle under strong vibration and complex scenes are effectively solved.

The invention provides a method for improving source network load storage integrated adjustment capability, and belongs to the technical field of power system automation and energy management. Multi-side data acquisition terminals are deployed to acquire power grid operation parameters in real time and perform adaptive extended Kalman filtering noise elimination; a source-load-storage dynamic coupling matrix is constructed based on historical data, adjustment weight coefficients of all links are calculated, and an adaptive prediction fusion model is used for energy storage state estimation and new energy output prediction. An energy storage power compensation instruction, an adjustable load starting instruction and a source load storage scheduling instruction are generated in a grading manner according to the magnitude of power grid frequency deviation by adopting a hierarchical cooperative regulation and control strategy, and an adjustment weight coefficient and a control instruction are iteratively optimized according to an adjustment effect, so that the technical problem of insufficient source network load storage multi-link cooperative adjustment capability is solved.

The invention provides a Beidou RTK transformer substation operation positioning method based on AI auxiliary dynamic fusion, and belongs to the technical field of power detection. Multi-source data are collected and time synchronization is carried out; based on the collected RTK data, RTK resolving state features, satellite geometric distribution features, signal-to-noise ratio features and position mutation features are extracted, feature vectors are constructed, a feature vector sequence is used as input, and credibility weights of the RTK data are obtained through a lightweight long short-term memory (LSTM) network model; iMU data, VO data and credibility weights are used as input, multi-source data fusion is carried out through extended Kalman filtering (EKF), the optimized position coordinates of the positioning terminal are obtained, and safe distance early warning is carried out based on the position coordinates. Therefore, continuous and stable high-precision positioning and safety management and control under all working conditions are realized through a closed-loop technical path of feature perception, intelligent evaluation and dynamic fusion in combination with an AI model and adaptive Kalman filtering.

The invention provides an aviation permanent magnet synchronous motor control architecture based on master-slave redundancy and model prediction, belongs to the field of aviation electric propulsion control systems, and aims to solve the problems of extreme environment parameter drift, single-fault shutdown, multi-target control conflict, fault diagnosis lag and the like of an existing system. The control architecture comprises a master-slave dual-redundancy control unit, a wide-temperature-range adaptive model prediction control unit, an electromagnetic interference adaptive suppression unit and a multi-dimensional fault tolerance unit. According to the invention, a DSP + FPGA heterogeneous redundant architecture is matched with two-out-of-three voting logic, so that fault non-perception switching is realized; an extended Kalman filter observer is embedded to update motor parameters on line, and take-off / cruise / landing three modes are preset; interference at the frequency band of 100kHz-10MHz is controlled and suppressed through adaptive notch filtering and a sliding mode variable structure; a long short-term memory network is fused to realize accurate diagnosis of early faults, and the system is adaptive to aviation scenes such as electric general aviation aircrafts and unmanned aerial vehicles.

The invention discloses a high-precision beam alignment method for an Internet of Vehicles ground-non-ground converged communication system and a vehicle-mounted communication terminal, and belongs to the technical field of cellular and satellite converged communication. A two-stage control strategy of'coarse tracking + fine tracking 'is adopted. The method comprises the following steps: firstly, based on GNSS / INS fusion positioning and satellite orbit prediction (SGP4 model), quickly calculating and finishing initial coarse alignment of a wave beam; and then, constructing a local beam scanning window, collecting received signal intensities (RSSI) in multiple directions as observed values, introducing an extended Kalman filter (EKF) algorithm, and establishing a state space model for recursive estimation, thereby realizing high-precision and dynamic closed-loop optimization and tracking of a beam pointing angle. According to the invention, the speed and the precision of beam alignment are obviously improved, the anti-interference capability and the link stability of the system in a dynamic environment are enhanced, and the continuous communication service in the sky-ground fusion Internet of Vehicles is effectively guaranteed.

The invention discloses a mutual inductor error self-compensation method based on dynamic flux linkage reconstruction, and relates to the technical field of mutual inductor error self-compensation. According to the method, a weighted double-domain integral noise suppression flux linkage reconstruction algorithm is used for processing, the fast response of time domain integral and the anti-noise capability of frequency domain integral are fused, and then a dynamic error extension state observer is constructed by taking a flux linkage estimation value as input; and estimating system errors caused by nonlinearity, hysteresis and environmental disturbance of the iron core and dynamic characteristics of the system errors based on extended Kalman filtering recursion. And finally, generating a time-varying compensation amount according to the estimated error and the derivative thereof. According to the method, the contradiction between precision and response speed in traditional flux linkage reconstruction is overcome, the problem that a static error model cannot adapt to dynamic working conditions is solved, high-precision real-time self-compensation of nonlinear time-varying errors of the mutual inductor is realized, and the measurement accuracy and dynamic performance of electric energy metering and protection equipment under complex operation conditions are improved.

A monorail hoist positioning method combining rail joint beacons and inertial navigation. An odometry kinematic model for a monorail hoist is established by means of an encoder on the monorail hoist, and an extended Kalman filter is used to fuse a heading angle obtained from a kinematic model solution and a heading angle obtained from a strapdown inertial navigation solution. Beacons are arranged at rail joints for the monorail hoist, and vibration signals are acquired by a vibration sensor. By combining a strapdown inertial navigation system, the encoder, and a rail installation diagram, a rail joint determination model is constructed to perform filtering, so as to obtain impact signals at rail joint beacons that meet a requirement. Beacon identifiers are used to match the identified rail joint beacons against specific beacon coordinates in the rail installation diagram, so as to obtain a time-position coordinate sequence for the monorail hoist arriving at the rail joints. A combined positioning model integrating rail joint beacons and strapdown inertial navigation is constructed to realize integration of local relative positioning and global absolute positioning of the monorail hoist. The method can improve the positioning accuracy of the monorail hoist while reducing positioning costs.

The invention requests to protect a multi-sensor fusion adaptive motion control system and method for a boat-type lotus root harvester, and the system comprises a multi-sensor array which is used for obtaining the position, posture, speed, inclination angle and motor load information of the lotus root harvester, and comprises a GPS, an IMU, a current sensor and an encoder; the microcontroller is used for running an extended Kalman filter (EKF) data fusion algorithm, a multi-mode PID self-adaptive control algorithm, a pure tracking path planning algorithm and a nonlinear model prediction control algorithm, and comprehensively considering path tracking precision, control smoothness and multiple constraint conditions in a prediction time domain through a rolling time domain optimization strategy; generating an optimal control instruction according to the state estimation; the executing mechanism comprises a walking motor and a steering engine and is used for responding to the control instruction and driving the lotus root harvester to move; the invention discloses a Simulink hardware-in-the-loop verification system.

The invention relates to the technical field of satellite communication, and discloses a parabolic antenna pointing prediction and tracking method of an inter-satellite communication antenna servo system, which comprises the following steps of: acquiring current attitude measurement data of an antenna and a signal quality parameter of a communication link; calculating a link health degree index according to the signal quality parameter of the communication link; dynamically adjusting a process noise covariance matrix in an extended Kalman filtering model for state prediction based on the link health degree index; performing optimal state estimation on the pointing state of the antenna at the future moment by using an extended Kalman filtering model of the dynamically adjusted process noise covariance matrix; and generating and executing a servo control instruction according to the optimal state estimation. According to the method, the actual quality parameter of the communication link is quantified into the health degree index to dynamically adjust the process noise covariance of the prediction filtering model, and the robustness and tracking precision of the system under complex disturbance are improved.

The invention provides a wind turbine power prediction method based on multi-modal feature fusion, and relates to the technical field of wind turbine power prediction.The method comprises the steps that multiple types of sensors are used for obtaining multi-source sensor data, and a standardized time sequence data set is generated; establishing a first observation matrix based on the standardized time sequence data set, and constructing a weight sensing fractional order adaptive genetic algorithm to optimize the first observation matrix to obtain a second observation matrix; performing feature fusion and noise reduction on the second observation matrix by applying extended Kalman filtering and combining the working state and physical modeling of the wind turbine; constructing a bidirectional long-short-term memory network, and performing power time sequence modeling based on the multi-dimensional noise reduction feature vector to obtain an output power predicted value; and feeding back an error between an output power prediction value and a true value to a weight sensing fractional order adaptive genetic algorithm to carry out parameter iterative optimization to obtain a third observation matrix, and obtaining a power prediction value of the wind turbine based on the third observation matrix.

The invention discloses a cooperative control method for controlling a suspension load by multiple unmanned aerial vehicles, and the method comprises the steps: firstly constructing a frame based on a trajectory, combining an online kinematics and dynamics motion planner with an airborne trajectory tracking controller, and generating a feasible trajectory considering dynamics coupling and constraint through a finite time optimal control problem (OCP); then, an extended Kalman filter (EKF) estimator is used for fusing data of multiple unmanned aerial vehicle inertial measurement units (IMU) and a system dynamics model, and the load attitude, torsion and the cable state are estimated in real time; and finally compensating the tension interference of the cable in real time through an INDI (Incremental Nonlinear Dynamic Inversion) controller. According to the invention, the agility and robustness of the suspension load system are cooperatively improved through online motion planning and the trajectory tracking controller, the low-speed limitation of traditional cascade control is broken through, an additional sensor does not need to be deployed at the load end, and the practicability and reliability of the system under the scenes of high-speed obstacle avoidance, complex trajectory tracking and the like are remarkably improved.

The invention relates to the technical field of vehicle electric control suspensions, in particular to a chassis prediction control system and method based on binocular vision and multi-domain cooperation, and constructs the chassis prediction control system based on binocular vision and multi-domain cooperation based on an ADAS binocular camera. IMU data of a binocular camera is obtained by using a data acquisition module of the chassis prediction control system, visual observation data of a vehicle body is obtained by using a binocular visual processing module, and then the IMU data and the visual observation data are fused and converted into a three-dimensional image by using modules such as an extended Kalman filtering fusion module and a motion conversion module. The sprung mass vertical acceleration and sprung mass vertical speed of the vehicle four-corner suspension provide accurate suspension four-corner vertical motion state input for an electric control suspension controller, and the accuracy and real-time performance of the electric control suspension for vehicle body posture control are guaranteed. Meanwhile, the problems that a special IMU is difficult to arrange, high in cost and insufficient in precision and IMU resources of an ADAS system are wasted are solved.

The invention discloses a self-supervised ultra-wideband positioning method based on an improved double-delay depth deterministic strategy. The method comprises the following steps: acquiring channel impulse response data CIR and performing preprocessing; an improved double-delay depth deterministic strategy model is constructed to carry out UWB ranging error correction, the improved double-delay depth deterministic strategy model is based on a double-delay depth deterministic strategy gradient model, a double-commentator network structure is designed, a spatial-temporal feature coding layer based on a self-attention mechanism is introduced, and UWB ranging error correction is carried out. Capturing a long-range dependency relationship through a dynamic weight distribution mechanism, and establishing cross-timestamp time-space association feature mapping; a target network delay updating mechanism is introduced, so that the updating frequency of a target network is lower than that of a commentator network and an actor network, and action noise generated by a strategy is regularized; and generating a pseudo tag by adopting extended Kalman filtering, and carrying out iterative optimization to obtain a positioning result. The method is particularly suitable for processing noise and multipath effect in CIR data, and high-precision UWB positioning is realized.