356 results about "Extend kalman filter" patented technology

A horizontal navigation system aided by a carrier phase differential Global Positioning System (GPS) receiver and a Laser-Scanner (LS) for an Autonomous Ground Vehicle (AGV). The high accuracy vehicle navigation system is highly demanded for advanced AGVs. Although high positioning accuracy is achievable by a high performance RTK-GPS receiver, the performance should be considerably degraded in a high-blockage environment due to tall buildings and other obstacles. The present navigation system is to provide decimetre-level positioning accuracy in such a severe environment for precise GPS positioning. The horizontal navigation system is composed of a low cost Fiber Optic Gyro (FOG) and a precise odometer. The navigation errors are estimated using a tightly coupled Extended Kalman Filter (EKF). The measurements of the EKF are double differenced code and carrier phase from a dual frequency GPS receiver and relative positions derived from laser scanner measurements.

The invention provides an indoor WLAN/MEMS fusion cross-stair three-dimensional positioning method. The indoor WLAN/MEMS fusion cross-stair three-dimensional positioning method is mainly characterized in that a WLAN/MEMS fusion positioning robust extended Kalman filter is designed, available information of two systems is fully utilized, and resolution of a high-precision two-dimensional position is realized; a height is calculated by using output of a barometer, a geographic position and stair height information, and a position fingerprint database is selected for positioning a wireless local area network (WLAN), and indoor space cross-stair three-dimensional positioning is realized. The system and method are high in positioning precision by introducing robust extended Kalman estimation, and can be used for effectively overcoming an accumulative error existing in positioning of a micro electro mechanical system (MEMS) sensor and received signal strength (position information and actual height information of the previous time) in WLAN positioning to realize indoor space cross-stair three-dimensional positioning.

The invention relates to an inertia/visual integrated navigation method adopting iterated extended Kalman filter and a neural network, belonging to the technical field of integrated navigation in a complicated environment. The method comprises the steps of when a visible signal is valid, acquiring a dynamic video by utilizing a camera carried by a mobile robot, and determining the speed of the camera by an image characteristic extraction method and a nearest neighbor matching method; optimally estimating the speed and the acceleration of the mobile robot by using the iterated extended Kalman filter; establishing a navigation speed error model of an inertial navigation system by utilizing the neural network; when the visible signal is in loss of lock, compensating the speed error of the navigation system by virtue of the neural network error model which is previously obtained by training. According to the method, the problem that the inertia/visual integrated navigation system can not provide lasting high-precision navigation when the visible signal is in loss of lock can be solved; the method can be applied to long-endurance, long-distance and high-accuracy navigation and location for the mobile robot in the complicated environment with weak light, no light or the like.

A model predictive controller (MPC) for predictive control of nonlinear processes utilizing an EKF (Extended Kalman Filter) and a nominal trajectory generator. The nominal trajectory generator includes another instance of EKF, a linear corrector, and a time-varying deviation model. A nominal control trajectory can be predicted and an optimal solution for the time-varying deviation model can be computed based on an approximation of a system inverse known as signal de-convolution. The EKF can be utilized to estimate a current process state by supplying a measured output and to predict a future nominal trajectory by supplying a reference output. A Kalman smoother can also be utilized for the signal de-convolution in order to obtain enhanced trajectory estimates.

Provided is a power battery SOC estimation method based on backward difference discrete model and a system thereof; the method comprises the following steps: step one, establishing a backward difference discrete model of a power battery, identifying parameters of the backward difference discrete model by a least square method including forgetting factors; step two, on the basis of the backward difference discrete model of the power battery obtained in step one, using self-adaptive extended Kalman filter in combination with a non-linear relationship between an open-circuit voltage and the SOC to complete an effective estimation of the power battery SOC. In the system, voltage and current sensors connected with the power battery are connected with an embedded microcontroller via an analog-digital conversion module. The microcontroller comprises a low-pass filter pre-processing module, a backward difference discrete battery model parameter online identification module, and an AEKF algorithm SOC estimation module. The obtained SOC result is sent to a CAN network of a display device. The power battery SOC estimation system based on backward difference discrete model is simple in structure; the parameter identification speed and precision are increased; the affection to the identification caused by history data is reduced; the calculation is convenient; and the SOC estimation precision is high.

A frequency tracking extended Kalman filter (35), responsive to combustor pressure (30), produces in-phase (36) and quadrature (37) components of the estimated magnitude of the undesirable combustor pressure variations, for which compensation is to be achieved; a bidirectional minimum-seeking algorithm (41) is used to select the phase (42) of a process adjusting input variable (28), such as fuel that is in addition to the main fuel flow used for power control purposes.

The invention discloses a modification-based state-of-charge estimation method for an RC battery model. The modification-based state-of-charge estimation method comprises the following steps: building an RC modification model; identifying parameters of the model; utilizing the extend Kalman filter (EKF) algorithm to obtain a continuous state space function of the battery RC modification model; according to the battery model parameter values of all points SOCs, acquired by the identification method, performing repeated curve fitting; then using the interpolation method to acquire the parameter values of the SOCs in the whole stage. The invention provides the EKF battery state-of-charge estimation method based on the RC modification model by the built RC modification model to overcome the defects of the classical battery state-of-charge estimation algorithm. Compared with a traditional method, the modification-based state-of-charge estimation method has the characteristics of favorable dynamic performance, insensitivity to initial value errors, excellent measurement noise inhibition property and the like.

The invention discloses an inertial measurement unit based on a gyroscope and geomagnetic sensors. The unit is formed by a three-axis MEMS gyroscope and two film geomagnetic sensors. By using the characteristics that the film geomagnetic sensors do not accumulate errors with flight time, the film geomagnetic sensors and the inertial device, i.e. the three-axis MEMS gyroscope are designed in a combining way. The invention adopts a state estimation method, an attitude determination system takes the high-precision film geomagnetic sensors as attitude measurement references and corrects the drift of the three-axis MEMS gyroscope, and a comparatively accurate extended Kalman filter algorithm is adopted to improve the precision of attitude determination. The invention has the advantages of small volume, light weight and low cost, and can be used in the high-speed rotating missile attitude measurement field.

The invention discloses a navigation method based on iterative extended Kalman filter fusing inertia and monocular vision. The method is as follows: A monocular camera and an inertial measurement unitare mounted on the carrier. A message filter in ROS is used to realize that time stamp synchronization of the monocular camera and the inertial measurement unit. The position, velocity and rotation obtained by IMU are taken as the state variables of the system, and the position and attitude information obtained by vision sensor is used as the observation variables to establish the system equation. The position, velocity and rotation obtained by IMU are used as state variables of the system. The information obtained from the two sensors is fused by an iterative extended Kalman filter to realize the real-time state estimation and navigation of the carrier. The invention can maintain high precision in the long-time real-time positioning and navigation process, and has the advantages that thecomputational complexity between frames does not change.

The invention discloses a power lithium battery SOC estimation method based on the self-adaptive Kalman filtering method. The power lithium battery SOC estimation method comprises the following steps:at first, according to the dynamic characteristics of a lithium ion battery, establishing a dual-polarization equivalent circuit model of the battery; then, obtaining data through testing the performance of the composite pulse power, identifying the characteristic parameter of the model, and adopting the least squares fit to obtain a relation curve of the open-circuit voltage and SOC; based on the relation curve of the open-circuit voltage and SOC and the discrete equation of a DP model, establishing a state equation and an observation equation, and substituting the state equation and the observation equation into the EFK algorithm to obtain a system matrix; and finally, adopting the modified self-adaptive extended Kalman filtering algorithm to estimate the battery SOC. With adoption of the power lithium battery SOC estimation method, the problems that the filtering results diffuse and the operation is not stable when the traditional self-adaptive Kalman filtering method or the EFK algorithm is adopted for SOC estimation are effectively solved, and the speed that the SOC estimated value is convergent to the truth value is increased.

The invention discloses an indoor fusion positioning method based on extended Kalman filtering and particle filtering, and belongs to the technical field of wireless sensor networks. The method comprises the following steps: (1) firstly, acquiring signal intensity of WiFi and recording position coordinates to construct a fingerprint database, and then positioning by adopting a weighted k-nearest neighbor method; (2) collecting MEMS inertial sensor data, counting steps in combination with acceleration and a step counting algorithm based on a differential acceleration finite-state machine, fusing multiple sensor readings for course estimation, and estimating step length in combination with Kalman filtering and a nonlinear step length model; (3) using extended Kalman filtering to fuse a positioning result of a WiFi fingerprint method and pedestrian dead reckoning; and (4) correcting the estimated position by combining particle filtering and indoor map information. Through fusion, the problem that the positioning precision of a WiFi fingerprint method is easily influenced by signal fluctuation and the problem that positioning errors of a pedestrian dead reckoning method are accumulatedalong with time increase are solved, and the positioning precision can be remarkably improved.

The invention relates to a wireless locating method which can be used for location with high degree of accuracy in a mixed environment of sight distance and non-line of sight. The method first sets up motion equations and observation equations of wireless location and then expresses state transition probability model of the non-line of sight and the sight distance, which can make use of rectified extended Kalman filter (EKF) to estimate the motion state and the non-line of sight state according to measured values obtained by every base station and then blends the motion state and the non-line of sight state together through the use of a data fusion method to get the estimation of the motion state at the present moment and at last on-line wireless device position solutions can be realized through loop iteration. The method of the invention can effectively solve the non-line of sight influence in wireless location so as to effectively improve the motion state estimation of wireless devices, which has robustness to LOS/NLOS transition probability in different environments. At the same time, the method is suitable for VLSI parallel processing, operand can meet real time requirements, and the method is suitable for different signal measuring methods such as TOA, RSS, etc.

A computational air data method and system for estimating angle-of-attack and angle-of-sideslip of an aircraft utilizing a detailed aerodynamic model of the aircraft, extended Kalman filters, inertial system measurements of body rates and body accelerations, and computation of dynamic pressure.

The invention discloses an inertial navigation system (INS)/wireless sensor network (WSN) indoor mobile robot tight-integration navigation-oriented iterative extended RTS average filtering method. The method comprises the following steps of through iterative extended kalman filter, carrying out data fusion on navigation information extracted from a local relative coordinate system by INS and WSN, improving a navigation information output period on this basis, carrying out iterative extended RTS smoothing on the data in a small period, then carrying out average filtering, and outputting the data. The iterative extended RTS average filtering method effectively improves a precision of navigation on an indoor mobile robot.

The invention discloses an attitude and position estimation method based on vision and inertia information. According to the method, by fusing vision and inertia information of an image, motion state information is saved and updated by virtue of an expanded kalman filter so as to calculate the current accurate attitude and position of equipment, and the motion trail of the equipment within a certain duration of time can be obtained. According to the estimation method disclosed by the invention, the information of the image and a sensor can be flexibly utilized so as to achieve good robustness through mutual complementation of the information, and a situation of tracking losing is avoided. The expanded kalman filter adopted by the method is used for calculating based on an iterative form, and compared with a method of batched calculation, the estimation method can be used for calculating without acquiring all observed quantities, is relatively low in calculated quantity, adaptive to a condition of relatively few calculation resources at terminal equipment and capable of preferably achieving a requirement on real-time performance.

The invention discloses a coal mine disaster relief robot navigation system based on information integration and a method. An ultrasonic wave module 1, a vision module 2 and an inertia module 3 form an information acquisition module, the information acquired by the information acquisition module is processed by an information processing module, so that the path planning is realized; and the improved neural network-extended Kalman filter (NNEFK) method is provided, and the method can effectively solve the problem that the information integration is not matched, improves the convergence and thereal-time property, and reduces the errors of the navigation system and the method by the design of a feedback link. The navigation system and the method can effectively improve the navigation capability of a coal mine disaster relief robot.

The method includes steps: building up kinematical equation of earth satellite orbit based on rectangular coordinate system; building up measurement equation based on angular distance of starlight as measuring quantity; building up discrete type extended Kalman filtering equation to determine whether extended Kalman filtering is divergent; using prognostic filtering residual to determine whether extended Kalman filter is divergent; if yes, then the method carries out estimating statistic characteristics of noise; otherwise, carrying out calculation according to standard extended Kalman filtering program. The invention solves issue that divergence of noise filter caused by inaccurate determination of statistic characteristics of noise influences on navigation accuracy. Advantages are: independent, flexible and simple, and high precision. The invention is especially suitable to earth application satellite needed high navigation accuracy in resource, weather areas etc.

The invention discloses a fused dual-Kalman filter navigation device based on an MEMS sensor and VLC positioning, and a navigation method. The navigation device comprises an MEMS sensor, an inertial navigation system (INS) module, a pedestrian dead reckoning (PDR) positioning module, a visible light communication (VLC) positioning module, an attitude extended Kalman filter (A-EKF), and a location extended Kalman filter (L-EKF); for the A-EKF, an error equation edited mechanically based on the INS is used as a system equation, an observation equation comprises update of observation of an accelerometer and a magnetometer, and attitude information is output to the VLC positioning module and the PDR positioning module so as to correct attitude impact; and for the L-EKF, position information of a two-dimensional plane is used as a system state vector, a pedestrian dead reckoned error equation is used as a system equation, and a VLC positioning result is an observation equation. The navigation device and navigation method solve a problem that VLC positioning is easy to be affected by device attitude and cannot keep positioning continuously if an optical signal is blocked, and eliminate the influence of attitude on VLC positioning.

The invention provides a large line width CO-OFDM phase noise compensation method of time-frequency domain Kalman filtering. The method comprises the following steps: firstly, performing Kalman filtering on receiving end training symbol data in the frequency domain, and performing channel equalization, then dividing each OFDM symbol into a plurality of sub-symbols, performing time domain extended Kalman filtering at a pilot frequency sequence in each sub-symbol to obtain a rough phase noise estimation value of each time domain sampling point, and compensating the rough phase noise estimation value; performing linear interpolation between the rough phase noise estimation values at the last pilot frequency sequence of the adjacent sub-symbol to obtain the rough phase noise estimation value of each time domain sampling point, compensating the rough phase noise estimation value, and performing pre- judgment after performing phase noise compensation by using the Avg-BL method; and finally converting the frequency domain data into the time domain to combine with the initial time domain data after the pre- judgment, performing extended Kalman filtering at each sampling point to calculate a precise phase noise estimation value, and compensating the precise phase noise estimation value. The large line width CO-OFDM phase noise compensation method has the advantages of better phase noise equalization effect, good compensation effect and small calculation complexity.

The invention discloses an adaptive extended kalman filtering-based road slope estimation method which comprises the following steps: acquiring vehicle state data through a data acquisition device, and calculating model parameters according to the vehicle state data and fixed parameters of a vehicle; then building an extended kalman filtering estimation model based on a relation model between a slope and the vehicle state data; finally, improving the kalman filtering estimation model into an adaptive extended kalman filtering algorithm model. According to the method provided by the invention, by the use of an adaptive extended kalman filtering algorithm capable of realizing rapid convergence and real-time estimation, the road slope can be dynamically estimated in real time according to the vehicle driving state data, so that real-time road slope information can be supplied to a driver, and important basis can be supplied for automatic driving assistant decision making, green driving and automatic transmission gear shifting control, to realize safe, economical and comfortable driving; the slope estimation precision under uncertain dynamic noise influence is improved, and the application range is extended.

The invention relates to a parameter estimation OCV (Open Circuit Voltage) based full temperature SOC (State of Charge) estimation method and belongs to the technical field of battery management. Themethod includes the following steps: S1, selecting a power battery to be tested and a required equivalent circuit model, and determining a system state and model parameters that need to be identifiedon-line; S2, constructing an experiment and carrying out a variable power cycle experiment to the power battery, and then recording experiment data; S3, identifying a battery state and the model parameters online on the basis of the recorded operating condition experiment data and an adaptive joint extended Kalman filter algorithm; and S4, establishing an OCV-SOC-T model based on parameter estimation, and realizing SOC accurate estimation of the power battery in the full temperature range on the basis of the model and experiment operating conditions. The model adopted by the invention is simple, the algorithm is not complicated, highly robust on-line estimation of the system state of the battery and the model parameters can be realized, and thus parameter estimation OCV based full temperature SOC accurate estimation is realized.

The invention provides a generator dynamic estimation method considering model parameter uncertainty. According to the method, a generator dynamic state estimation model is established, wherein a classic model of a generator is established, and a dynamic state estimation equation and a measurement equation of the generator are constructed; error analysis is performed, wherein an error variance matrix of measured values and process noise are considered; and adaptive robust extended Kalman filtering dynamic estimation is performed, wherein adaptive robust extended Kalman filtering is adopted toperform dynamic estimation on a power angle and an electric angle of the generator in an electromechanical transient process according to a generator state space model. Through the method, an estimation error upper limit brought by system parameter uncertainty can be effectively defined, and by the adoption of the adaptive technology to perform adaptive estimation on the parameters, the problem that the error upper limit is difficult to select through traditional robust extended Kalman filtering is avoided. Therefore, compared with existing methods, the method has higher robustness and higherestimation precision.