138 results about "Ekf algorithm" patented technology

The invention provides an attitude estimation method of a maneuvering acceleration-assisted extended Kalman filter (EKF) attitude and heading reference system (AHRS). The state quantity of the EKF contains an error of three attitude angles, a null bias error of a three-axis gyroscope and a three-axis carrier maneuvering acceleration error of a carrier system, and the observed quantity of the EKF contains a three-axis acceleration error and a three-axis earth magnetic field error. A strapdown attitude algorithm and the nine-state EKF algorithm are subjected to data fusion to acquire attitude estimation of the AHRS. A single-axis turntable experiment, a vehicle-mounted dynamic experiment and a flight experiment prove that the maneuvering acceleration-assisted nine-state EKF data fusion algorithm has stable attitude angle accuracy under different maneuvering conditions, and the mean square deviation of the attitude angle can be limited within 2 degrees.

The invention discloses a method and system for estimating SOC (State-of-Charge) of a power battery based on dynamic parameters. The method comprises the following steps: carrying out a discharge-standing experiment on the battery, obtaining OCV (Open Circuit Voltage)-SOC characteristic curves of the battery at different temperatures, and fitting out an OCV-SOC relational expression; carrying out a constant current pulse discharge-standing experiment on the battery, recording voltage response during the experiment, and identifying the initial value of the parameter of a battery second-order RC equivalent circuit model by an offline method; carrying out dynamic parameter identification on the second-order RC equivalent circuit model by using a forgetting factor-containing recursive least squares method RRFLS; carrying out online estimation on the SOC of the battery by using an EKF (Extended Kalman Filter) algorithm. The estimation method overcomes the defects of inaccuracy and cumulative error of the initial value of SOC in an ampere-hour integral method, and adapts to the dynamic change of battery characteristics, the battery model is high in precision and convergence speed, and is stable and reliable, and the precision of SOC online estimation is improved. The method and system can be widely used in fields of electric vehicles and energy storage battery management systems.

The invention discloses a method based on segmented expansion Kalman filtering for estimating the state of charge of a lithium battery. The method mainly comprises the steps of firstly, establishing asecond-order RC equivalent circuit model according to a working mechanism of the lithium battery; secondly, according to experimental data of pulse discharge responses of the lithium battery, utilizing a recursive least square method (RLS) to identify model parameters; thirdly, establishing a state space model of lithium battery discretization, adopting a segmented EKF algorithm to estimate the SOC of the lithium battery. According to the method, the suitable expansion Kalman filtering algorithm is utilized while an intensity nonlinearization process is distinguished from the working processof the lithium battery, and the robustness in the process of adopting a traditional EKF algorithm to estimate SOC is effectively improved; the problem is solved that decrease of filtering precision and possible filtering divergence are caused by Taylor series expansion linearization.

The invention relates to the technical field of power batteries of electric automobiles, and discloses a composite estimating method of a power battery SOC based on a PNGV equivalent circuit model. The composite estimating method comprises the following steps that A, the open-circuit voltage of the power battery is detected; B, an open-circuit voltage method is used for calculating the primary SOC (t0) of the power battery; C, in the time period of t0-t1, an EKF algorithm is used for correcting the primary SOC (t0), and an SOC (t1) is obtained; D, in the time period of t1-t2, an improved ampere-hour integral method is used for estimating; E, when the power battery is continuously used, the steps from the step C to the step D are circulated; the t0 represents the primary time, and the t1 and t2 represent the time points after the t0. According to the composite estimating method of the power battery SOC based on the PNGV equivalent circuit model, the correction coefficients of influencing factors such as the charging and discharging currents of the power battery, the environment temperatures, the battery health state are considered, the improved ampere-hour internal method is used, and the defect that the influence from outside factors is large when the ampere-hour internal method is used for estimating the power battery SOC can be overcome. The advantage that an EKF method has a powerful correcting effect on initial errors of the SOC is fully used.

The invention discloses an SOC estimation method based on a Kalman filtering method, and belongs to the field of power battery management. The method specifically comprises the steps that 1, a power battery model is built, and a precise lithium power battery model is built mainly by determining an equivalent circuit model and based on test data; 2, power battery SOC is estimated; 3, EKF algorithmis adopted to estimate an SOC result and conduct error analysis. The method provided by the invention analyzes the working principle of a lithium ionic battery and builds a power lithium ionic batterydivalent RC equivalent model, identifies the parameter models through laplace transformation and a large number of experiment data, and adopts a matlab platform to estimate the power lithium batterycharge state. The emulation result shows that the model has the good adaptability to the complex working conditions of electric automobiles, not only can the precision of SOC estimation be improved, but also the computing quantity is small, and SOC estimation is easy to achieve.

The invention provides a lithium ion power battery SOC estimation method based on neural network optimization EKF. On one hand, a Thevenin equivalent circuit model is built according to nonlinear voltage characteristics of a lithium ion battery, model parameters are determined on the basis of experiments of different SOC points and charge-discharge directions, a state equation and an observation equation are obtained based on a lithium ion battery model, and a calculation process of an extended Kalman filtering-based SOC estimation algorithm is designed; and on the other hand, an error prediction model is built based on a BP neural network, and a measurement noise covariance is corrected in real time in a filtering process, so that a state estimation error introduced due to the fact that amodel error is relatively large and system noise is assumed to be Gaussian white noise is eliminated. According to the method, an SOC estimation result of the EKF is compensated based on various modeling errors; the superiority of combination of the BP neural network and the EKF algorithm is proved; the maximum estimation error is within 0.25%; and the method has relatively high engineering application values.

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 provides a robot non-trace quick simultaneous localization and mapping (SLAM) method based on multiple fading factors, and belongs to the technical field of autonomous navigation of mobile robots. The robot non-trace quick SLAM method comprises the steps that system initialization is conducted to determine the initial position and posture an initial state estimation mean and a covariance matrix of a mobile robot at the k moment,; time updating is conducted, and the predicted position and posture and covariance of particles at the moment of k+1 are calculated; observation data arecorrelated with predicted observation data; the multiple fading factors are calculated according to a particle observation value, position and posture estimation means and covariance of the particlesare calculated in a measurement updating mode, Gaussian distribution functions are constructed, and sampling is conducted; resampling is conducted, a new particle set is obtained, and robot positionand posture estimation is completed; and according to the result after data correlation, a new guidepost is updated through an EKF algorithm, and map features are estimated. According to the robot non-trace quick SLAM method, the error introduced in the linearization process is lowered, the filtering accuracy of a system is improved, the strain adjusting ability of a filter when the system state changes suddenly is improved, and the robustness of the robot navigation and localization system is enhanced.

The invention provides a method for observing a rotary speed of am induction motor of a Kalman filter with index fading factor, belonging to the field of drive control of motors. The method solves the problem that the observing result of state variable is influenced because the matrix error of the noise covariance is great in the standard Kalman filter EKF (Extended Kalman Filter) algorithm which is used for observing the rotary speed of the induction motor in the prior art. In the invention, the method realizes the rotary speed observation on the basis of a rotary speed observer and the Kalman filter is utilized in the rotary speed observer to carrying out error predication; the index fading factor lambda (K+1) is introduced into an error covariance matrix predictive equation of the Kalman filter in the process of realizing the rotary speed observation by operating the rotary speed observer; and the error covariance matrix predictive equation is shown in the specification. The method provided by the invention is suitable for observing the rotary speed of the induction motor.

The invention discloses an RA-Singer-EKF (Random Access-Singer-Extended Kalman Filter) maneuvering target tracking algorithm based on radial acceleration, which belongs to the field of radar maneuvering target tracking. According to the method, the radial acceleration and radial speed information of a maneuvering target can be rapidly and accurately provided, and the tracking performance of a radar on the maneuvering target is improved effectively. The method comprises the following steps of: (I) sampling a radar receiving signal, and obtaining a target radial acceleration and a radial speed by using a matching pursuit (OMP (Operation Management Platform)) method; (II) performing coordinate conversion on the radial acceleration and the radial speed at a data processing stage, and introducing into a measuring equation and a state equation; and (III) realizing maneuvering target tracking by adopting a Singer model and an EKF algorithm. Due to the adoption of the RA-Singer-EKF maneuvering target tracking algorithm, the maneuvering situation of the target can be reflected accurately in real time, the target tracking accuracy is increased, the speed and acceleration estimation accuracies are improved, engineering implementation is easy, and a high engineering application value and a good popularization prospect are achieved.

The invention discloses a robot autonomous navigation method based on heading-assisting distributed type SLAM (Simultaneous Localization and Mapping) and belongs to the field of robot autonomous navigation. According to the method disclosed by the invention, aiming at combining characteristics of a robot movement model and an observation model, factors which have relatively great influences on system performances, including calculation quantity and the like of particle filtering and expanded Kalman filtering, are compared respectively under a framework of distributed type filtering operation, and requirements on the calculation quantity in a real-time system are considered; a filter is designed by adopting a distributed type EKF algorithm, but not adopting a PF algorithm; and in order to guarantee the observability of the system at the same time, a magnetic compass is introduced to be used as an auxiliary sensor except that a traditional speedometer and a laser sensor are used as a sensor combination scheme, so that navigation information of a robot is added into the system. The design disclosed by the invention aims at the observation model of the distributed type filtering structure, and a filtering method is planned again through introducing the navigation information, so that the robot autonomous navigation aim is realized and the stability and precision of the system are improved.

The invention discloses an indoor mobile robot positioning method and apparatus based on EKF / EFIR mixed filtering. The method includes constructing a UWB robot positioning model by taking the position and speed of the x direction and y direction of a target robot, and the x-direction and y-direction positions of each UWB reference node as the state quantities and taking the distance between a reference nodes and a positioning label measured by the UWB positioning labels as the system observed quantities; estimating the position of the robot and the position of the reference node through an EKF / EFIR mixed filter by taking the UWB robot positioning model as the basis and by means of the distance information between the reference node and the positioning label measured by the UWB positioning labels; pre-estimating by means of the EKF algorithm, detecting the EKF algorithm performance, and supplementing the EKF algorithm with the EFIR filter to obtain the optimal position information of the target robot at the current time. The method can improve the positioning precision of the mobile robot in an indoor environment.

The invention proposes a method for estimating SOC of a lithium battery based on the improved EKF algorithm. On the basis of the EKF algorithm, by means of the robust data correction idea, a residualek between the EKF observation variance mid-end voltage estimation value (shown in the description) and an actual measured value yk is tacked as the reference; an influence function is adopted, and athreshold Eta is set; the residual value between the end voltage estimation value and the actual measured value yk is compared with the threshold value Eta; a noise variance Qk of the filtering process is corrected in real time; the weight of the noise Qk estimation error is reduced, and the robust objective function reaches a minimum value; then the observed noise covariance matrix Rk is adjustedthrough dynamic intervals of different SOCs, and the influence of the model error on the SOC estimation accuracy can be reduced.

The invention discloses a novel lithium ion power cell SOC estimation method. The novel lithium ion power cell SOC estimation method is characterized in that a cell equivalent circuit model can be established, and the identification of the parameters of the established cell model can be carried out by adopting the least squares algorithm; according to the cell open-circuit voltage UOCV acquired by identifying the parameters according to the step one and the corresponding SOC relation, a corresponding function can be acquired by adopting a Shepherd model and a Nernst model in a combined manner, and the function is used for the matching of the UOCV and the SOC relation; the state equation and the observation equation of the SOC estimation can be established, and the STF algorithm has the strong robustness related to the model uncertainty and the extremely strong tracking capability related to the breaking state. The verification of the SOC estimation by adopting the EKF algorithm and the STF algorithm can be carried out by the constant current discharging experiment and the UDDS working condition experiment, and according to the result, the accuracy of the SOC estimation is higher by adopting the STF algorithm than adopting the EKF algorithm, and the convergence performance is better.

The invention discloses a cooperative location method based on an EKF (Extended Kalman Filter) and a PF (Particle Filter) under a nonlinear and non-Gaussian condition. The method comprises the following steps of obtaining TOA (Time of Arrival) original data of a target node and each base station and computing to obtain a distance between the target node and each base station; utilizing a Wylie identification method to judge whether an NLOS (Non Line of Sight) error exists; obtaining a difference of the TOA value to obtain a TDOA (Time Difference of Arrival) value and reconstructing a distance different rm1 corresponding to the TDOA value; respectively utilizing an EKF algorithm and a PF algorithm to estimate position coordinates of the target node at the moment tk; carrying out residual weighting to obtain a final estimated value at the moment tk; and carrying out weighted smoothing on the position coordinates at all moments to obtain a final positioning result. In comparison with the EKF, the method is more suitable for the nonlinear and non-Gaussian positioning environment; in comparison with the PF, the use of incorrect data is effectively avoided and the calculated amount is reduced. According to the method, the influence of the NLOS error is effectively reduced, the advantages of the EKF and the F are combined, the defects of the EKF and the F are overcome, and the more precise positioning is realized.

A lithium battery SOC evaluation method of mixed expansion Kalman filtering includes the step of the establishment of a lithium battery equivalent model. In the lithium battery equivalent model, the open circuit voltage (OCV) of the battery is expressed as an electrochemical model, R refers to the battery internal resistance, which is different during charging and discharging and is set to be R+ during charging and R- during discharging, and p1, p2, p3, p4, p5, p6, p7 are the fitting parameters of the lithium battery model. According to the battery charge and discharge experiment data, the battery equivalent circuit model parameters can be estimated. The lithium battery SOC evaluation method also includes the steps of establishing a discrete state space model of the battery system, and using the improved EKF algorithm to estimate the battery SOC. The lithium battery SOC evaluation method overcomes the problems of low filtering precision or generation of filtering divergence in the EKF algorithm due to the uncertainty of the battery model, the unknown noise statistical characteristics and the Taylor expansion linearization.

The invention provides a method and device for positioning particles of a mobile robot. The method for positioning particles of the mobile robot comprises the following steps of: (a) estimating road signs by an unscented Kalman filtering (UKF) algorithm; (b) obtaining approximate distribution and position information of a plurality of sampling particles, and carrying out auxiliary positioning by adopting the unscented Kalman filtering algorithm; and (c) optimizing the sampled particles based on a genetic algorithm in a node. The method for positioning particles of the mobile robot has the advantages of: (1) estimating road signs by the UKF algorithm instead of the EKF (Extended Kalman Filter) algorithm in calculation of road sign estimation, and avoiding huge deduction of a Jocabian matrix; (2) disclosing the filtering and positioning method of particles distributed according to the UKF auxiliary advice; and (3) disclosing the concept of weight value based on topological node importance, improving diversity of particles, and preventing degradation of particles.

The invention aims at providing a target tracking method based on multi-scale dimensional decomposition, and the target tracking method comprises the following steps of selecting a wavelet-base function to decompose a target angle or track measurement data onto a scale, predicting and filtering the measurement data on a low-frequency subspace of each scale by utilizing extended kalman filtering (EKF) algorithm to obtain a rough tracking result of the target on different scales, and further eliminating the influence of noise and wildvalue by utilizing the wavelet threshold algorithm on the high-frequency subspace of different scales; and converging the tracking data on different scales through the wavelet reconstruction algorithm to obtain the precise tracking data of the target. The target tracking method can effectively, accurately, reliably and stably track the target in different complicated environment, the multi-scale EKF algorithm is realized by utilizing field programmable gate array (FPGA) parallel processing structure, the wavelet decomposition and rebuilding, the EKF algorithm of different scales and the wavelet threshold denoising are simultaneously implemented, and the real-time performance of the target tracking is ensured.

The invention discloses a method for estimating the state of charge (SOC) of a lithium battery by an extended Kalman filter algorithm. The method comprises the following steps that S1, an equivalent circuit model of the lithium battery is established; S2, a current source E is replaced with the voltage of circuit (VOC) of the lithium battery, and a first state equation and a first measurement equation of a system are established according to the equivalent circuit model of the lithium battery; S3, the first state equation and the first measurement equation are analogous to an EKF algorithm toobtain a second state equation and a second measurement equation correspondingly; S4, the SOC of the lithium battery is estimated by the EKF algorithm. The method has the advantages that the EKF algorithm does not depend on the setting of an SOC initial value, the filter process can be convergent in a short time even through the set initial value has large difference with a true value, and accurate SOC estimation is achieved.

The invention discloses an evaluation method of state of health SOH of a cell. Through a cell cycle life experiment, effects on the SOH caused by internal resistance, capacity and SOC parameters are obtained. The internal resistance is used as a research standard, and an objective is provided for following algorithm researches. The internal resistance is used as a research object, a cell equivalent circuit module is established, and on the basis of the module, the SOH of the cell is evaluated by use of the D-EKF. Test software in which the VC++ as a development kernel is designed, the D-EKF algorithm is packaged into a software test platform to finish the evaluation of the SOH of a single cell and a display interface of main parameters of the cell is designed at the same time. According to the invention, aiming at the problem of a failure to predict and evaluate life states of each single storage battery, by researching the SOH of the cell, the SOH of the cell is evaluated by use of the D-EKF method; and the test platform based on a VC software environment is designed, so the accuracy of internal resistance value monitoring data and accuracy of evaluation of the SOH of the cell are tested through the test platform at the same time.

The invention discloses an RLS and EKF algorithms based full-vanadium flow battery SOC estimation method, comprising: 1) building a mathematical model for a full-vanadium flow battery; 2) using the RLS algorithm to identify parameters of the mathematical model for the full-vanadium flow battery; 3) using the EKF algorithm to estimate the SOC of the full-vanadium flow battery; and 4) combining the RLS algorithm and the EKF algorithm; updating in real time the model parameters for the full-vanadium flow battery; and according to the updated model parameters, conducting new SOC estimations. The method of the present invention achieves on-line updating of model parameters and accurate SOC estimation without additional system configuration.

The invention provides a lithium battery SOC and SOP estimation method based on a second-order RC model, and the method comprises the following steps: S1, selecting the type and model of a lithium battery, obtaining corresponding technical parameters, and building a battery equivalent circuit model; S2, performing an HPPC experiment on the battery at a specific temperature, obtaining battery characteristic parameters, establishing a relationship between battery open-circuit voltage and SOC, performing parameter identification by combining an optimization method, and feeding back the obtained parameters to the model; S3, carrying out SOC closed-loop estimation by combining an EKF algorithm; and S4, estimating the instantaneous power and the limited power of the battery according to P = U *I. The method is characterized in that an original battery model with current as input and voltage as output is improved into a model with voltage as input and current as output. As for SOP estimation, a known voltage and current calculation model is more suitable, and the problems of tedious steps and large calculation amount of an existing method can be solved.

The invention relates to an identification method of battery pack inconsistency. A frequency division model is adopted. An average battery model and a differential battery model are included. An extended Kalman filter EKF algorithm is combined to realize identification of battery pack inconsistency. The method comprises the following steps of firstly, using a current sensor, a voltage sensor and a temperature sensor to collect work data of a battery pack; further, using the average battery model, combining an EKF algorithm and using a high frequency mode to estimate an average charge state of the battery pack; and finally, near an average SOC of the battery pack, using the differential battery model, combining the EKF algorithm and using a low frequency mode to estimate a difference between a monomer battery SOC and the average SOC of the battery pack. In the invention, the battery pack inconsistency can be accurately identified through a low calculated amount, then battery pack equalization or fault diagnosis is realized, and dynamic performance, safety and endurance of the battery pack are increased.

The invention provides an SOC online estimation method for a storage battery based on an EKF algorithm, and the method comprises the following steps: determining a battery equivalent circuit model, and building a discrete state space model of a battery nonlinear system; building a fitting function relation between the SOC initial value SOC(0) of the battery and an open-circuit voltage initial value UOC(0) through a constant current charging and discharging experiment, solving and obtaining the SOC initial value SOC(0) of the battery; taking SOC(0) as an initial state quantity of input, carrying out the estimation of the SOC of the battery through the EKF algorithm, and generating an SOC estimated value; carrying out the temperature, battery service life and self-discharging effect compensation for the generated SOC estimated value, and outputting a corrected SOC estimated value. Through the optimization of the SOC initial value, the method enables the initial state of the EKF to approach to the real-time state of the storage battery as much as possible, guarantees the convergence speed of the SOC online estimation of the storage battery, and improves the estimation precision through the temperature, battery service life and self-discharging effect compensation.

The invention discloses a polarized light SLAM method based on extended Kalman filter, and belongs to the field of autonomous navigation of unmanned aerial vehicles. The method realizes the determination of the position of an unmanned aerial vehicle and the construction of a surrounding environment map through an EKF algorithm that is distributed extended Karl filter by establishing the state model of the unmanned aerial vehicle and a measurement model based on a laser radar sensor and a polarized light sensor, and the characteristics of matching, complementation and no outside interference ofpolarized light information and laser radar information are used to improve the accuracy of simultaneous positioning and composing of the unmanned aerial vehicle.

The present invention discloses a high-precision positioning method of a mobile robot along a subway track. The method comprises the following steps: performing mobile robot odometer pose estimation,and obtaining robot real-time pose (x1, y1, [theta]); in order to eliminate the accumulated error existed in odometer positioning, realizing a positioning method combining odometer measurement with total station instrument measurement by adopting an extended kalman filter (EKF); establishing a total station instrument CPIII / odometer combination positioning model based on the EKF; and performingmatching and pose updating on total station instrument observation information and odometer status prediction information via the EKF algorithm, and thus realizing periodic correction of the odometeraccumulated error and high-precision positioning of the mobile robot.

The invention discloses a mean orbit element-based relative navigation robust filtering method. The method comprises the following steps: 1, deriving a relative navigation state equation according to relative kinematics of formation flying satellites, measuring the intersatellite real-time relative positions and speeds through using CDGPS, and filtering measured data through an EKF algorithm to obtain a relative mean orbit element; 2, outputting formation configuration parameters after filtering is stable; and 3, carrying out prediction and recursion according to a relationship among the relative positions, the relative speeds and the configuration parameters in order to obtain intersatellite relative positions.

The invention discloses a filtering method for gyroscopic drift under a collaborative navigation condition of multiple unmanned surface vehicles. The filtering method comprises the following steps: two master unmanned surface vehicles provide with high-precision inertial navigation devices alternate to transmit an underwater sound distance measurement signal with a time stamp to a slave unmanned surface vehicle, the slave unmanned surface vehicle uses the speed measured by a Doppler velocity sonar and the course measured by an MEMS (micro-electromechanical system) gyroscope to conduct track plotting, and calculates the distance between the master unmanned surface vehicle and the slave unmanned surface vehicle through multiplying the difference of the transmitting time and the receiving time of the underwater sound signal by the sound velocity, and the position of the slave unmanned surface vehicle is updated and corrected through an EKF (Extended Kalman Filter) algorithm so as to estimate and compensate the gyroscopic drift. The EKF algorithm is used to correct the track plotting, and the initial course deviation and drift of the MEMS gyroscope can be estimated and compensated to improve the locating precision; in order to improve the observability, the two master unmanned surface vehicles alternate to transmit the distance measurement signal to the slave unmanned surface vehicle, the same state is adopted to firstly estimate the initial course deviation and then completely eliminate the course deviation and estimate the gyroscopic drift, and a very good filtering effect is achieved.

The invention discloses a robot absolute positioning precision calibration method based on kinematics and spatial interpolation, and belongs to the field of robot control methods. The method comprises the following steps: firstly, respectively establishing a robot geometric parameter error model and a flexibility error model to obtain a quantitative calculation error delta V; then, measuring an actual tail end pose of the robot to obtain identification experiment data, adopting an EKF algorithm for robot quantitative calculation error delta V parameter identification, correcting robot nominal geometric parameters through robot error parameters obtained through identification, thereby accomplishing first-time positioning error compensation of the robot and remaining robot positioning residual errors; constructing a robot positioning residual error model; carrying out secondary positioning error compensation on the robot; and finally obtaining an expected position of the robot. A variable-node distance interpolation algorithm considering joint influence degree is established, so that absolute positioning precision of the robot can be effectively improved, and defects of an existing method and technology in precision are overcome.

The invention discloses an observation method and an observation system of a diesel SCR (Selective Catalytic Reduction) system input state. The observation method comprises the following steps that Step 1, a state space model is established according to a chemical reaction in an SCR system; Step 2, the state space model is combined with an EKF (Extended Kalman Filter) equation, wherein the EKF equation comprises a prediction equation, namely x(k) is equal to f[x(k-1),u(k)] plus w(k), and a renewal equation, namely z(k) is equal to h[x(k)] plus v(k), x(k) represents a state vector, u(k) represents an input vector, w(k) represents a gaussian process noise, z(k) represents an observation vector, h(x) represents an observation function, and v(k) represents a gaussian observation noise; and Step 3, the EKF algorithm mentioned above is encoded with software, and computational simulation is performed, thereby obtaining an input concentration estimated value. According to the observation method and the observation system, the method expanding Kalman filters to perform input state observation is designed, so that the validity of a designed observer is verified in simulation; in addition, the precision of the estimated concentration is ensured; the expected design purpose is achieved; and an ammonia gas sensor or an NOx sensor at an inlet can be replaced completely.