100 results about "Kalman algorithm" patented technology

The invention is a charge state evaluation method and system of a power lithium ion battery. According to the method, step one is to establish a circuit model of an equivalent battery. Charging and discharging and standing experiments are performed on the battery, and timing sampling is performed so that a voltage time curve is obtained. Model parameters are identified via a formula so that a non-linear relation between an open-circuit voltage OCV and an SoC is obtained. Step two is to obtain an optical estimation value of the SoC by matrixes of state prediction, prediction error variance, filtering gain, state estimation, estimation error variance, etc., according to Kalman algorithm. According to the system, an analog / digital converter, a program storage device, a programmable storage device, a timer and a displayer are respectively connected with a microprocessor. A current sensor and a voltage sensor are respectively connected in a circuit formed by connecting the battery to be tested and a load, and outputs of the current sensor and the voltage sensor are accessed into the analog / digital converter. The programmable storage device stores battery model parameters obtained by the experiments. The program storage device stores estimation program of the method. Estimation precision of the SoC can reach 1%, and the charge state evaluation method and system is more stable; besides, the system provides estimation values of the SoC in real time.

The invention discloses a pedestrian tracking method based on least square locus prediction and an intelligent obstacle avoidance model. The method comprises the steps of: first, utilizing a traditional Kalman algorithm to realize initial stage target tracking, and to overcome deficiencies; in order to solve the problem that the pedestrian tracking lost and fault rate of the Kalman algorithm is higher, a least square method being provided to perform pedestrian motion curve fitting, predicting a pedestrian position in a next video frame, and employing the position as an initial motion target for searching; and finally, tracking pedestrians based on a self-adaption obstacle avoidance method, and improving tracking accuracy.

The invention discloses a real-time navigation system and a real-time navigation method for an underwater structure detection robot. The navigation system comprises a magnetic compass, a gyroscope, an accelerometer, a depth meter and a navigation microprocessor, wherein the magnetic compass, the gyroscope, the accelerometer and the depth meter are used for respectively collecting magnetic field intensity, an angular speed, a linear speed and submerged depth data and transmitting the magnetic field intensity, the angular speed, the linear speed and the submerged depth data to the navigation microprocessor; the navigation microprocessor is used for calculating attitude and position of the underwater robot according to the collected data. The navigation method comprises an attitude algorithm, a speed algorithm and a depth algorithm; according to the attitude algorithm, a complementary filtering method, a quaternion gradient descent method and a Kalman algorithm are combined for obtaining an attitude matrix and an attitude angle; the speed algorithm is used for calculating the speed and the position of the robot by using a three-order upwind scheme with rotary compensation; the depth algorithm is used for processing the data of the depth meter by using a moving average filter algorithm so as to obtain the submerged depth. By virtue of the real-time navigation system for the underwater structure detection robot and the method thereof, the navigation cost is reduced and a relatively good navigation precision is achieved.

The present invention discloses a non-overlapping area pedestrian tracking method based on a deep neural network. The method comprises the following steps of: (1) employing a YOLO algorithm to performdetection of a current pedestrian target in a monitoring video image to segment a pedestrian target image; (2) employing the Kalman algorithm to perform tracking prediction of a detection result; (3)employing a convolutional neural network to extract depth features of images, wherein the images comprise candidate pedestrian images and the target pedestrian images in the step (2), and storing theimages of the candidate pedestrian and features; and (4) calculating similarities of the features of the target features and the features of the candidate pedestrian, and performing sorting of the similarities to identify the target pedestrian. The non-overlapping area pedestrian tracking method can obtain high detection and tracking precision so as to facilitate improvement of pedestrian recognition rate.

The invention provides a power battery health state estimation method. A lithium ion battery second-order Thevenin equivalent circuit model is adopted, and an adaptive unscented Kalman filter (AUKF) algorithm is applied to carry out real-time estimation on a state of a battery. The adaptive unscented Kalman filter algorithm is combined with an unscented Kalman filter algorithm and an extended Kalman filter algorithm, a loop iteration relation is established, the battery state is estimated according to known battery parameters, then the battery state serves as a known quantity to estimate modelparameters, recursive operation is performed in the same manner, and SOC and an ohmic internal resistance of the battery are estimated in real time. And battery SOH can be estimated in real time by using a function corresponding relationship between the ohmic internal resistance and the battery SOH.

The invention discloses a path trajectory adjusting method and device. The method comprises the following steps: obtaining a real-time coordinate value of each target position in a plurality of target positions of a current path and a neighbor coordinate value of a neighbor position adjacent to each target position; adjusting the real-time coordinate value by utilizing a target filter algorithm and the neighbor coordinate value to obtain a first target coordinate value of each target position; and carrying out adjustment on the current trajectory of the current path according to the first target coordinate value of each target position to obtain a first target trajectory of the current path. Through the technical scheme, the real-time coordinate value of each target position can be subjected to initial adjustment through the target filter algorithm, and then, real-time adjustment is carried out again on the first target trajectory through one or more algorithms of a wavelet transform algorithm and a Kalman algorithm to obtain a higher-accuracy higher-precision trajectory of the current path, thereby solving the problem of GPS longitude and latitude coordinate drift.

The invention discloses a lithium battery charge state assessment method. The method includes the first step of setting an initial value and carrying out Cholesky decomposition on each covariance, the second step of state one-step prediction, the third step of covariance one-step prediction, the fourth step of gain filtering, the fifth step of updating the optimized value of a state, and the sixth step of updating filtering covariance. Compared with the prior art, the precision of the method is higher than that of first-order spreading of the Taylor series, effective error information caused by model linearization is fully made use of, and strong robustness for model parameter changes is achieved.

The invention discloses a method for rapidly and accurately forecasting the travel time of vehicles for passing through road sections, and belongs to the field of intelligent transportation. The method includes the steps of (1) obtaining information of all pass stations and history vehicle information in a time period, (2) obtaining vehicle caching of the starting point pass station A, (3) judging whether forecasting of the travel time of the vehicles for traveling from the pass station A to the pass station Bi adjacent to the pass station A is completed or not, (4) conducting finding on a vehicle record with the pass station A as a starting point and the pass station Bi as a terminal point in a license number matching mode, (5) calculating the travel time of the vehicles for traveling from the starting point pass station A to the terminal point pass station Bi in the current time period and the prior time period, (6) forecasting the travel time of the vehicles in the next time period with a Kalman algorithm, (7) calculating a jamming coefficient, and (8) completing the method for detecting and forecasting the travel time. By means of the method, the travel time of the vehicles for passing through the adjacent pass station road sections can be rapidly and accurately forecasted, and the method is suitable for intelligent transportation systems, traffic pass systems, road traffic management systems, traveler information systems and traffic flow information detection systems.

The invention belongs to the battery identification and estimation field, and specifically relates to a method of estimating SOC (State of Charge) and impedance of a lithium battery on line. The method of estimating SOC and impedance of a lithium battery on line can improve the accuracy of a batter model equation so as to improve the accuracy of the Kalman filtering algorithm, by adding the impedance parameters, such as the direct current impedance, the polarized capacitance and the polarized resistance of the lithium battery, into the battery model equation, and can optimize the deficiency that the accuracy of the traditional Kalman filtering algorithm depends on the model accuracy. And at the same time, the method of estimating SOC and impedance of a lithium battery on line enables the dynamically changed impedance parameters to participate in every SOC estimation process, so that the estimated value of the battery residual electric quantity can have higher accuracy. At last, the method of estimating SOC and impedance of a lithium battery on line can obtain the reference value of the current healthy state for the lithium battery by referring to the corresponding battery electrochemistry impedance spectrum, thus being conductive to assisting the user to preferably master the current service life of the lithium battery by estimating the impedance parameters obtained through real time estimation. The method of estimating SOC and impedance of a lithium battery on line can realize high accuracy of estimating SOC and the service life of the lithium battery on line.

The invention discloses a data flow abnormality detection method based on a parallel Kalman algorithm. The data flow abnormality detection method comprises the following steps that 1, measurement data of a sensor in a period of time is acquired; 2, the measurement data is compared with a measurement value in a previous period of time, once a change is generated, an estimation value is calculated through the Kalman algorithm according to the measurement value, an absolute value of a difference between the estimation value and the measurement value is compared with a specified threshold value, and if the absolute value is not smaller than the threshold value, the absolute value is judged to be an abnormal value, and the next step is conducted; 3, the generation reasons of the abnormal value are judged by considering a time influence factor, a space influence factor and other factors such as the flood period, the weather and the human factors which influence abnormality detection and recorded, and information is stored in a database. According to the data flow abnormality detection method, the time influence factor, the space influence factor and the other provenance information influence factor are taken into account; an algorithm task is decomposed and processed in parallel in order to improve the algorithm efficiency, and the detection precision is improved.

The invention provides a deformation monitoring positioning method based on carrier wave phase difference static and dynamic state fusion; the method comprises the following steps: receiving GNSS observation data of a monitoring station and a reference station, making station-star double-difference for carrier wave phase in the observation data so as to form a non-rank defect equation set, using an extended Kalman algorithm to carry out iterative solution according to the least square idea, thus obtaining a floating point position solution of the monitoring station; if a fuzziness integer value is searched by an LAMBDA / MLAMBDA algorithm, a fixed position solution of the monitoring station can be finally obtained. The method periodically adjusts a time update process in the extended Kalmanfiltering algorithm, fuses advantages of a dynamic mode and a static mode of the carrier wave phase difference algorithm, thus ensuring precision deformation monitoring sensitivity and high precisionrequirements. The method can improve the horizontal positioning precision up to 3mm, and can improve the elevation positioning precision up to 5mm; compared with a conventional carrier wave phase difference static state mode method, the method can effectively ensure the monitoring sensitivity, thus keeping the deformation reaction time within a required scope.

The method provides an on-line estimation method and system for the state of charge of a power battery, wherein the on-line estimation method and system are simple and compact in process and high in anti-noise capability. Based on a battery two-order RC equivalent circuit model, a battery time-varying parameter is dynamically tracked through an RLS algorithm with a forgetting factor; a covariancematrix cholesey is adopted to decompose the iterative calculation process, and the calculation amount is small and stable, and a diagonal matrix elements can be used for self-adaptive adjustment of the forgetting factor; aiming at the problem that the one-to-one correspondence relation between the open-circuit voltage and the SOC of certain types of power batteries is not stable, simulation estimation of a hysteresis voltage is introduced and is used for correcting the open-circuit voltage, so that the accuracy of the final mapping relation between the open-circuit voltage and the SOC is guaranteed; an extended kalman filter (EKF) ) is adopted in SOC estimation, and only an SOC single variable electric quantity state equation and an observation equation based on the end circuit voltage areadopted, so that the updating calculation of the redundant state and the hypothesis and estimation of the statistical characteristics of the redundancy state are reduced, and the real-time performance and the response efficiency of calculation are guaranteed.

The invention provides an insulator fault diagnosis apparatus and method based on a fuzzy cerebellar model neural network. The insulator fault diagnosis apparatus is composed of an MCU, a first pre-processing circuit, a second pre-processing circuit, a third pre-processing circuit, an electric field sensor, a leakage current sensor, and a temperature sensor, a remote transceiver module and a remote computer. The electric field sensor, the leakage current sensor and the temperature sensor collect signals; denoising processing is carried out based on a Kalman filter algorithm to obtain a featuresample with fault information; an insulator fault information training sample library is established; classification training is carried out on samples based on FCMAC; training is carried out by using a BP algorithm to obtain a weight value and a threshold value that enable network optimization to be realized; and when a new information sample is inputted into the network, a fault type of an insulator is determined rapidly and accurately. On the basis of combination of the Kalman algorithm and the CMAC, a few of weight values need to be corrected each time, the learning is accelerated, and the certain generalization ability is enhanced. Besides, the efficiency and accuracy of the insulator fault analysis are improved.

The invention discloses a method for estimating the remaining power of an electric vehicle power battery. The method comprises steps that based on the volume Kalman algorithm, a model for estimating the remaining power of the power battery is established; polarization internal resistance Rp(k), polarization capacitance Cp(k), equivalent ohmic internal resistance R0(k), the remaining power SOC (k)and an end voltage Ut(k) of the power battery at the time k are obtained; an open circuit voltage UOC(k) of the power battery at the time k is calculated; a state estimation error delta e(k) and a noise error V(k) are calculated; the BP neural network based on width learning is constructed, the delta e(k) and the V(k) are inputted into the BP neural network, and a variance compensation value deltaQk of process noise distribution at the time k and a variance compensation value delta Rk of observed noise distribution are outputted by the BP neural network; the delta Qk and the delta Rk are utilized to compensate Qk-1 and Rk-1 at the time k-1, and Qk and Rk at the time k are generated; a value of x(k+1) of the model for estimating the remaining power of the power battery is calculated through the volume Kalman algorithm, and the remaining power SOC (k+1) at the final (k+1) time is obtained. The method is advantaged in that the remaining power of the power battery can be rapidly and accurately estimated.

The invention discloses a method for predicting the remaining life of a vehicle lithium battery. The method comprises in combination with the historical data of the capacity of the lithium battery andbased on an extreme learning network model, optimizing the input weight and offset of the extreme learning network by a heuristic Kalman algorithm and constructing a new heuristic Kalman-extreme learning prediction model. The method for predicting the remaining life of a vehicle lithium battery has advantages and effects of greatly improving the accuracy and the real-time performance of the prediction of the lithium battery.

The present invention provides a machine vision-based movement control method, device and system. The movement control method is applied to controlling an intelligent shopping cart to follow a preset tracking target, wherein the intelligent shopping cart is provided with an image pickup device. The method includes the following steps that: the image pickup device acquires a scene image containing a target image, wherein the target image is corresponding to the tracking target in the scene image; the location and size of a preset search frame containing the target image is determined in the scene image according to the location and size of the target image; when the target image suddenly disappears or is displayed incompletely, and the size of the search frame is adjusted, and the location of the search frame is predicted through using a preset Kalman algorithm; and the movement state of the intelligent shopping cart is controlled according to the size of the adjusted search frame and the predicted location of the search frame. With the machine vision-based movement control method, device and system provided by the embodiments of the present invention adopted, the intelligent shopping cart can automatically follow the tracking target.

The invention relates to a supercapacitor charge state estimating method based on the Kalman filtering algorithm, in particular to a charge state estimating method. The charge state estimating method includes the steps of firstly collecting the voltage value and the current value of a supercapacitor in the working state in real time; based on the collected data, carrying out online parameter identification on a supercapacitor model with the least square method; based on the collected data and an obtained circuit model, estimating the charge state of the supercapacitor with the Kalman filtering algorithm. By means of the charge state estimating method, the supercapacitor charge state real-time estimation accuracy can be effectively improved; meanwhile, no large calculation load of a system is caused, and the charge state estimating method has the advantages of being high in stability and reliability and the like. Meanwhile, state variables of the nonlinear system can be accurately estimated through the Kalman algorithm, estimation does not depend on accurate initial value setting, and a system true value can be rapidly approached in the large-deviation original state.

The present invention relates to a clock synchronization method in the Ethernet circuit simulation business, in specific to an adaptive clock method based on the time stamp which faces to the Ethernet simulation operation. The method utilizes the local operation clock and counter to obtain the time stamp information (local time stamp) which represents the remote terminal and proximate operation clocks; the time stamp information is converted into random sequence which meets the kalman process and a measurement equation and the kalman filtering algorithm is used to filter noise from the sequence to obtain the real time stamp; the obtained real time stamp is used for regulating a voltage control oscillator to make local operation clock to track the remote terminal operation clock. The method adopts the kalman algorithm which makes the loop filtration can choose hardware to or software to realize; the application is more flexible; the frequency difference is monotonically decreasing basically without vibration and the lock anchor can be reached fast. Compared with the ordinary buffer zone method and time stamp method, the comprehensive function of the present invention is stronger.

The invention provides a method for improving dynamic gesture accuracy of inertia-geomagnetic combination. The method comprises the steps of firstly, extracting appropriate characteristic from a discrete sampling time sequence output from a three-dimensional accelerometer by an artificial neural network with regard to complicated three-dimensional motion such as human body movement; secondly, accurately estimating amplitude of carrier linear acceleration according to the characteristic by the artificial neural network; and finally, adjusting and expanding observation and process covariance ofa kalman algorithm in real time according to an estimation result, and further preventing the influence of the carrier linear acceleration on the algorithm so that the gesture resolution accuracy of the inertia-geomagnetic combination is improved.

The invention discloses a system and a method for predicating short-time wind power. The method comprises the following steps of adopting a Kalman algorithm to preprocess wind speed data, to enable the data to be smooth and stable; carrying out phase space reconstructing on the preprocessed data, and determining delay time and embedding dimensions; utilizing an Elman neural network to establish a wind speed predicting model to predict the wind speed; according to a power conversion formula, converting the wind speed into power, and outputting the predicted power value. After being proved by multiple experiments, compared with the prior art, the predicting precision is obviously improved.

The invention relates to an improved ultra-wideband and Bluetooth integrated indoor positioning method based on NB-IoT. The method comprises the following steps: S1, carrying out ultra-wideband ranging positioning based on acquired signals and data of an ultra-wideband base station; S2, based on the collected signals and data of the Bluetooth beacons, carrying out distance measurement and positioning of Bluetooth; S3, taking the distance measurement distances from the positioning label to the ultra-wideband base station and from the positioning label to the Bluetooth beacon as input excitationof a Kalman algorithm, and calculating an initial position of a positioning coordinate by using a trilateral positioning algorithm and the first input excitation, and continuously predicting and updating the positioning coordinates according to a continuous input extended Kalman algorithm of subsequent excitation. The invention aims to provide an improved indoor positioning method integrating anultra wide band and Bluetooth based on NB-IoT. The method has the advantages that finally, centimeter-level accurate positioning is achieved on the whole, and positioning services of different scenescan be dealt with.

The invention relates to a collaborative control and target tracking method based on mobile multi-agent formation. The research that a moving object is tracked in a formation mode by using multi-agentcontrol at present lacks. According to the collaborative control and target tracking method, a target motion state is estimated and calculated by using a Kalman algorithm, and thus an estimated trajectory of target motion is obtained; then a consistency algorithm is adopted, and a multi-agent system is controlled to form the formation; and finally, the target estimated trajectory is tracked through a sensor control method. According to the collaborative control and target tracking method based on the mobile multi-agent formation, a Kalman filter under a gaussian noise environment is selectedto estimate target motion, processes of sensor controlling and target tracking are experimented through a consistency theory, combination of a sensor controlling problem and a target tracking problemis deepened in a traditional algorithm, and tracking stability is greatly improved.

The invention relates to a phase noise compensation scheme of a CO-OFDM system, in particular to a novel time domain cubature Kalman phase noise compensation algorithm scheme. According to the scheme,firstly, by utilizing pilot frequency information, CPE phase noise is compensated through extended Kalman and linear interpolation algorithms; a signal after first-order compensation of the phase noise is pre-judged; and then sub-symbol processing is carried out on the pre-judged signal in a time domain. In combination with a time domain signal after the sub-symbol processing, a capacity Kalman phase noise compensation algorithm is carried out on judged data in the time domain to achieve fine compensation of ICI phase noise. The data after the fine compensation is subjected to iterative operation, so that the compensation effect is improved. Simulation analysis shows that when the line width of the phase noise is relatively large, the novel time domain cubature Kalman algorithm can effectively enhance the compensation effect on the ICI phase noise, so that the tolerance of the CO-OFDM system on the line width of a laser is improved, and the performance of the system is improved.

The invention discloses a lithium battery SOC online estimation method based on a backward smooth filtering framework, and the method comprises the following steps: 1, testing a lithium battery so asto obtain a function of an open-circuit voltage with respect to a state of charge, and obtaining an initial SOC of the lithium battery through the open-circuit voltage; 2, establishing a lithium battery equivalent circuit model, and determining a discrete state equation and an observation equation of the lithium battery; 3, carrying out model parameter identification, and identifying the parameters of the equivalent circuit model of the lithium battery; 4, establishing a backward smooth square root cubature Kalman filter; and 5, acquiring real-time voltage and current data of the lithium battery, and estimating the SOC of the battery. According to the algorithm, on the basis of a traditional square root cubature Kalman algorithm, a backward smooth filtering framework is combined, backwardsmooth recursion operation is carried out by utilizing latest measurement information, the influence of factors such as observation noise and observation errors is reduced, and the convergence speed is increased while the estimation precision is improved.

The invention discloses a method for generating atomic time scale through the Algos and Kalman combination. The method includes the steps that firstly, original clock correction data is subjected to outlier detection; TA-K Hadamard variances in first six months are calculated to determine the weight of each clock to calculate the TA-K weight of the current month; then a result obtained through an improved Algos algorithm is used as input of a noise matrix and an initial value of the Kalman algorithm, and the weight of each clock is determined through the Hadamard variances; finally, the clock correction value at the next moment is predicted. By means of the technical scheme, the divergence of the Kalman algorithm can be reduced, and the time scale stability can be good.

The invention discloses a no-key entry and no-key system positioning method, and relates to the technical field of wireless communication and positioning. The method comprises the following main steps: 1, extending the Kalman algorithm distance estimation, and achieving the tracking and positioning of a smart key through extending the Kalman algorithm according to an RSS signal; 2, performing theadaptive updating of the RSS signal distance model parameters, and performing the real-time correction of the RSS signal distance model parameters based on a distance measurement value of the smart key at step 1; 3, performing the positioning based on an improved logistic regression location fingerprint algorithm. The method solves a key problem of the positioning of the smart key of the no-key entry and no-key system based on the extended Kalman filter algorithm and the improved internal and external identification algorithm based on improved logistic regression, and can effectively improve the positioning accuracy and positioning range of the smart key based on low power Bluetooth RSS signal, and improves the robustness of the no-key entry and no-key start system.

The invention relates to a dynamic estimation and intelligent correction method of remaining capacity of vehicle mounted lithium battery system. The method comprises the following steps: incorporating the time integral method, the open circuit voltage method and the lookup table method into the Kalman filter system so as to optimize the Kalman filtering as an extended Kalman algorithm; obtaining the estimated power capacity through the extended Kalman algorithm; measuring the remaining power capacity of the battery resting through the open circuit voltage method; controlling the configuration strategy of the module algorithm through the control of a strategy state machine; and through the algorithm control module, configuring two different basic modules for each working condition so as to realize the dynamic operation of estimation and correction. According to the invention, the method reduces the errors generated from the use of a single method to estimate the remaining power capacity of a battery and the accumulated errors. Therefore, it is possible to provide an optimal error correction scheme based on the different states of the battery as well as the different working conditions of an entire vehicle.

A method for estimating the remaining capacity of a battery based on a threshold extension Kalman algorithm includes the following steps: 1) using an EKF algorithm to obtain estimated values of respective state variables at a current time, the state variables including remaining battery capacity, first RC link terminal voltage, and second RC link terminal voltage; 2) setting the thresholds of state variables in a state equation in combination with short-term historical current data, and determining whether the estimated values of the state variables exceed respective threshold ranges, and if so, restricting the corresponding state variable to the threshold range. Based on the EKF algorithm, the method add thresholds to state variables in the model by using historical data, restricts the range of state changes, and prevents an decrease in the accuracy of SOC estimation caused by the divergence of state variables, and has better robustness than the existing model-based SOC estimation method.

The invention discloses a dynamic compensation method for an MEMS (Micro Electro Mechanical System) gyroscope Z-axis zero offset on the basis of Kalman filtering. The dynamic compensation method mainly comprises the following steps: S1: when an IMU (Inertial Measurement Unit) is under a static state, obtaining carrier gesture data, and calculating to obtain the compensation value of MEMS gyroscopeZ-axis angular speed Rz through the obtained carrier gesture data to serve as an initial compensation value Offset; S2: in actual measurement, reading new carrier gesture data from the IMU; S3: judging whether the carrier is under the static state or not; S4: calculating by a Kalman algorithm, obtaining a new compensation value of the MEMS gyroscope Z-axis angular speed Rz, updating the Offset, and then, jumping to S2 to form circulation. According to the dynamic compensation method for the MEMS gyroscope Z-axis zero offset on the basis of the Kalman filtering, the Z-axis zero offset is dynamically captured, the Kalman algorithm is applied for carrying out dynamic compensation, the accuracy of the MEMS gyroscope is improved, a gyroscope Z-axis error is reduced, and meanwhile, the measurement error of a course angle YAW is reduced.

The invention relates to a real-time interaction technology for an interaction system, in particular to a multi-party remote interaction system. The system comprises a plurality of remote terminals used for acquiring, processing, transmitting and receiving audio / video signals; each terminal comprises a switch used for being in network connection with each terminal to perform data exchange; and a microprocessor unit is connected in series in front of the switch and is used for compressing the audio / video signals. The microprocessor unit is arranged before the switch of the current each terminal, compresses the audio / video signals acquired by other equipment and transmits the audio / video signals to other remote terminals, the microprocessor unit performs high compression processing on the audio / video signals by adopting a Kalman algorithm, and exchange data quantity among remote terminals is reduced; therefore, real-time interactive efficiency among the plurality of terminals is improved.