346 results about "Extended Kalman filter" patented technology

A method for the estimation of the state variables of nonlinear systems with exogenous inputs is based on improved extended Kalman filtering (EKF) type techniques. The method uses a discrete-time model, based on a set of nonlinear differential equations describing the system, that is linearized about the current operating point. The time update for the state estimates is performed using integration methods. Integration, which is accomplished through the use of matrix exponential techniques, avoids the inaccuracies of approximate numerical integration techniques. The updated state estimates and corresponding covariance estimates use a common time-varying system model for ensuring stability of both estimates. Other improvements include the use of QR factorization for both time and measurement updating of square-root covariance and Kalman gain matrices and the use of simulated annealing for ensuring that globally optimal estimates are produced.

The invention discloses an online lithium ion battery SOC estimation method based on extended Kalman filter, belongs to the technical field of SOC prediction of a lithium ion battery, and aims to solve the problem that the reliability of the online estimation of a conventional lithium ion battery SOC is low due to influence of initial value selection. The method comprises the steps as follows: a voltage and current relation of a first-order RC (resistance / capacitance) equivalent circuit of a detected lithium ion battery and a voltage and current relation of a second-order RC equivalent circuit are established firstly; a charge-discharge experiment is performed on the detected lithium ion battery to establish a polynomial fitting function of a Kalman filter initial value SOC 0 of the detected lithium ion battery; a covariance P (0) of the Kalman filter initial value SOC 0 and a Kalman filter initial error of the detected lithium ion battery is obtained; and then, battery SOC estimation based on extended Kalman filter is performed, so that the online estimation of the lithium ion battery SOC is realized. The method is used for online estimation of the lithium ion battery SOC.

The invention relates to an SOC estimation method for a series-wound battery pack having an equalization circuit. According to the estimation method, adaptive extended Kalman filtering SOC estimation is carried out on the single batteries with the lowest voltage or the highest voltage in the charge-discharge stage so as to obtain the SOC of the series-wound battery pack. For eliminating influence on the measurement signal from noise, and for analyzing the unstable and sharply-changed voltage and current signals, wavelet transform is performed before the implementation of the adaptive extended Kalman filtering so as to realize denoising and analyzing of the signal; and the condition that the curves of the battery parameters and the open circuit voltage-state of charge (OCV-SOC) are changed along with the changes of temperatures and the changes of the SOC is taken into consideration. By adoption of the estimation method, online updating of the parameters can be performed, so that the SOC estimation precision can be improved; the SOC of the battery pack can be accurately estimated under the premise of ensuring the safety operation of the series-wound battery pack; therefore, the estimation method is applicable to active equalization and passive equalization; and meanwhile, by adoption of the estimation method, the influence from noise can be effectively eliminated, and the voltage and current signals can be effectively analyzed.

The invention relates to an estimation and implement method of state of charge (SOC) of battery for electric car, belonging to the field of electric car intelligent niformation processing technology. Said method utilizes the state space equation of battery which is formed from battery charge state equation based on ampere-hour metering method and measurement equation of battery load votlage, and uses the improved extended Kalman filtering equation to calculate and obtain the stale of charge of battery. Said invented advantage lies in that it has stronger adaptability, can eliminate initial error of SOC, and can raise the convergence of error or reduce speed, at the same time it can modify the change of SOC resulted frmo self-discharge of battery. Said invention is applicable to SOC estimation of cell monomer, module and battery.

The invention discloses an online simultaneous identifying method for load torque and inertia of a motor. According to the method, a model reference adaptive control inertia identification model is utilized to realize online identification of rotational inertia; then the rotational inertia acquired through identification is introduced into an extended Kalman filtering load torque identification model in real time as a control variable, so that decoupling identification of the inertia via the load torque can be realized. The method overcomes the defect of the present online parameter identification model for the motor that the online identification error is great due to intercoupling of the load torque and the rotational inertia, and can carry out accurate online identification to the load torque of the motor on the condition that the rotational inertia is unknown or the rotational inertia changes during operation of the motor. The method has the advantages of simple structure and minor calculation quantity, realizes decoupling of the rotational inertia via the online identification result of the load torque of the motor, and improves the practicability of the online parameter identification system.

The invention discloses a whole attitude angle updating method applied to agricultural machinery and based on a nine-axis MEMS (micro-electromechanical system) sensor. The whole attitude angle updating method applied to agricultural machinery and based on the nine-axis MEMS sensor comprises the following steps: a gyroscope error model, an electronic compass calibration ellipse model and a seven-dimensional EKF (extended Kalman filter) model are established, and parameter vectors of corresponding motion attitudes of a vehicle body are set; data including the acceleration, the angular velocity and the geomagnetic field intensity of vehicle body motion are acquired in real time; the angle, the speed, the location information and the heading angle of the vehicle body are calculated through the established gyroscope error model and electronic compass calibration ellipse model; data fusion processing is performed on the angle, the speed, the location information and the heading angle of the vehicle body through the seven-dimensional EKF model, and the motion attitude angle of the vehicle body is updated in real time. The method comprising the steps has small errors and high precision and is stable and reliable.

The invention declares a correction method used for attitude and course angles of a navigation system. The method comprises the following step of providing an effective method of conducting magnetometer error compensation and attitude angle error secondary modeling and correction, and realizing error correction of a whole system on the basis of an ellipsoid fitting method of improving expanded Kalman filter. The correction method is used for error correction of the attitude and course angles in an inertial integrated navigation and positioning system composed of an inertial measuring unit (IMU), a magnetometer and the like. According to the invention, a magnetic field compensation method is expanded to three-dimensional ellipsoid fitting from two dimensional elliptical fitting; a new ellipsoidal model and an improved extended Kalman filter method are utilized to conduct ellipsoid fitting; through the adoption of the method, the dynamical and real-time compensation for three-dimensional magnetic field interference of a carrier self can be effectively realized, and the accuracy in the measurement of the geomagnetic field is improved, thereby improving the precision of the course angle of the carrier; the error secondary modeling is conducted on the attitude angle information output by the navigation system, and then compensation is conducted on the output attitude angle to improve the precision of the attitude angle in real time.

The invention discloses a method for estimating the longitudinal adhesion coefficient of a road. The method comprises the following steps: preliminarily estimating a longitudinal road adhesion coefficient in real time on the road surface of a flat expressway by using a recursive least squares (RLS) method with a forgetting factor specific to a front-wheel steering four-wheel automobile on the basis of an overall longitudinal dynamics model and a simplified magic formula tire model; further filtering signal noise by using an extended Kalman filter (EKF) algorithm by taking the estimated longitudinal road adhesion coefficient and a tire model parameter as expansion states, and realizing self-adaptive adjustment of a tire model coefficient; finally, acquiring accurate and robust longitudinal road adhesion coefficient estimation in real time. The method can adapt to working conditions of high slip rate and low slip rate of flat expressways at the same time.

The invention relates to a three-dimensional gesture determining and local positioning method of a lunar surface rover, which comprises the following steps: (1) ascertaining the rolling and pitching angles by use of a triaxial accelerometer with sensitivity while the rover is still; (2) determining the drift angle gesture by means of a sun sensor; (3) using the axial gesture and the gyro deviation as the state quantity, the rolling and pitching angles established by the triaxial accelerometer, the drift angle determined by the sun sensor as well as three gyro outputs as the measuring information, building a state equation and a measuring equation, and estimating the triaxial and gyro deviations by means of extended Kalman filter; (4) after compensating the gyro outputs by virtue of the estimated gyro deviations while the rover is in motion, calculating the gesture changes of the rover, finishing the preestimation of the gyro gesture, and fulfilling gesture update; (5) acquiring the information about the rotation speed of the driving wheels of the rover, the rotating angle of the steering wheel, the rotating angle of the left and right rocker arms, and getting the position increment of the rover in the body coordinate system by use of the forward kinematics relationship. The invention has the advantages of high precision of gesture determining and positioning, simple calculation and easy implementation of the engineering.

The invention relates to a direct torque control system of a permanent-magnet synchronous motor. According to the control system, an extended kalman filter (EKF) observer is adopted for stator magnetic linkage and rotation speed estimation, according to a mathematical model of the permanent-magnet synchronous motor, the state variable x of the system equals to [Psi alpha, Psi beta, Omega gamma, Theta gamma] T, the input variable mu equals to [mu alpha, mu beta] T, the output variable y equals to [Iota alpha, Iota beta ] T, and state equations and output equations (11) of the system are obtained. The motor rotating speed is used as the input of an automatic data rate changer (ADRC) speed controller, the measurement value Te of the electromagnetic torque is calculated through the obtained stator magnetic linkage, in addition, the measurement value Te and electromagnetic torque given signals are compared, comparison results are used as the input of a space voltage vector pulse generator, and the output of the space voltage vector pulse generator is connected with a voltage source inverter of the permanent-magnet synchronous motor. The direct torque control system has the advantages of good dynamic performance, better stability performance, high robustness and high anti-interference capability.

The invention provides a phase noise compensation method suitable for large line width and high order modulation CO-OFDM system. The method comprises the following steps: performing channel equalization on training symbol data of a receiving terminal after performing Kalman filter on the same in the frequency domain; setting pilot frequency subcarrier data with certain intervals for each OFDM symbol on a transmitting terminal, and performing preset CPE phase noise estimation and compensation at pilot frequency subcarriers in the frequency domain based on extended Kalman filter (EKF); and finally, converting frequency domain data subjected to CPE phase noise compensation into the time domain, and realizing blind ICI phase noise compensation by using the Avg-BL method, then performing pre- judgment, converting the frequency domain data subjected to the judgment into the time domain, applying time domain data and original time domain data of the receiving terminal to time domain unscented Kalman filter, calculating a final phase noise compensation value, and performing compensation. By adoption of the phase noise compensation method, a better phase noise equalization effect is obtained, and the spectrum utilization rate of the system is improved.

The invention belongs to the technical field of intelligent automobiles, specifically relates to a complex environment radar multi-target tracking and road driving environment prediction method, whichparticularly aims to solve the problems that the position relationship identification of a lane where a target vehicle locates is inaccurate and the robustness and precision of a target tracking algorithm are not high through determination based on a radar original target measurement value in the process that a vehicle having a self-adaptive control function drives in a curve or an intelligent vehicle having an autonomous valet parking function enters and exits from a curved ramp of an underground parking lot. The complex environment radar multi-target tracking and road driving environment prediction method is mainly implemented by present vehicle motion state estimation, millimeter wave radar signal conversion, time synchronization, target motion compensation, data rationality judgment,target measurement value noise reduction, road curvature estimation, target aggregation, target motion attribute and motion state identification, improved adaptive extended Kalman filtering algorithmtracking and data association, road driving environment prediction and key target generation.

The invention relates to an SOC estimation method of a variable length sliding window by identifying battery parameters. The method comprises the steps that the battery parameters are identified in real time by a variable length sliding window least square method, then the battery parameters are used for conducting self-adaptive extended Kalman filter to estimate the battery SOC, and therefore the influence of the battery parameter change caused by battery ageing on estimation precision of the battery SOC is avoided. According to the method, the time-varying battery parameters can be tracked effectively, and no dependency exists to the selection of initial data; the self-adaptive extended Kalman filter is more suitable for the state estimation of a nonlinear time-varying system; a matrix inversion operation is converted into scalar division operation, and better real-time performance is achieved.

The invention discloses a method for predicting the cycle life of a fused lithium ion battery based on an EKF (Extended Kalman Filter) method and an AR (AutoRegressive) model, namely the method for predicting the cycle life of the lithium ion battery. The purpose is to solve the problem that the current methods based on models have a low adaptive capacity to different batteries and different working states. The method comprises the following steps: 1, measuring the capability data of the lithium ion battery to be measured on line, storing the data, and preprocessing the data; 2, based on the EKF method, determining the parameters of the state space model of the lithium ion battery; 3, according to the established state space model of the lithium ion battery, estimating the state of the lithium ion battery to be measured, and utilizing the output of the AR model to update the state of the lithium ion battery to be measured; causing the state space model of the lithium ion battery to obtain the capability data of the battery in each charging and discharging cycle, and comparing the data with the failure threshold of the lithium ion battery to be measured to obtain the residual life of the lithium ion battery. The method is used for predicting the cycle life of the lithium ion battery.

The invention provides an indoor unmanned aerial vehicle accurate positioning and autonomous navigation system. The system comprises an unmanned aerial vehicle; a binocular camera module which is usedfor capturing a ground two-dimensional code label to obtain position information of the unmanned aerial vehicle; an ultra-wideband UWB module which is used for capturing a positioning label on the unmanned aerial vehicle to obtain position information of the unmanned aerial vehicle; an external global high-definition camera module which is used for capturing mark points on the unmanned aerial vehicle to obtain position information of the unmanned aerial vehicle; an extended Kalman filtering module which is used for fusing the obtained position information of the unmanned aerial vehicle, predicting the attitude and position of the unmanned aerial vehicle and updating the attitude and position in real time; an instant positioning module which is used for instantly positioning the unmanned aerial vehicle; a navigation module which is used for providing an autonomous navigation function; a calculation and storage module which is used for calculating and storing position information of theunmanned aerial vehicle; and a man-machine interaction module which is used for providing a man-machine interaction function and setting a flight route of the unmanned aerial vehicle. The invention further provides a method for accurate positioning and autonomous navigation of the indoor unmanned aerial vehicle. The ststem is accurate in positioning, low in cost and reliable in performance.

The invention discloses a rail vertical irregularity estimation method and system based on extended Kalman filtering. The system is characterized in that vibration acceleration sensors and gyroscopes are installed on a train body and a frame, the output ends of the vibration acceleration sensors, the gyroscopes and train speed sensors are connected to a central processing unit, and spatial sampling of train body vertical vibration, train body nodding angular speed, frame vertical vibration, frame nodding angular speed and the like is realized through train speed signals. A vehicle rail coupling system state equation is established according to a kinetic equation for a vehicle rail coupling model; a sensor observation equation is established according to signals acquired by all sensors; a filtering iterative equation is configured to obtain time updating equations and measurement updating equations of state estimation and estimation error covariance; an optimal state at each moment is obtained according to extended Kalman filtering, and rail irregularity estimation is finally obtained after inversion by using the time updating equation of the state estimation. The rail vertical irregularity estimation method and system based on extended Kalman filtering have the advantages of low cost, good engineering feasibility, simplicity in operation and online real-time detection.

The invention discloses an automobile driving state estimation method with the influence of rolling considered. The method includes the steps that with the influence of automobile rolling motion considered, a three-degree-of-freedom motion differential equation which includes automobile mass center deviation, yawing motion and rolling motion and has nonlinear characteristics is established; the nonlinear three-degree-of-freedom motion differential equation is linearized; a state equation about the mass center deviation angle and the yaw velocity and a measurement equation are established, the mass center deviation angle and the yaw velocity are linearized and iterated into an extended Kalman filtering equation to obtain optimal estimated values of the mass center deviation angle and the yaw velocity, and meanwhile the mass center deviation angle and the yaw velocity of the nonlinear three-degree-of-freedom motion differential equation are input into an extended Kalman filtering model. Thus, comparison of the estimated values and actual values can be verified.

The invention discloses a pedestrian dead reckoning method based on low-cost multisensor fusion, and a relevant detailed method process is disclosed so as to solve the defects of being low in accuracy, poor in hardware quality, susceptible to interference and the like of low cost sensors. A method combining EKF (extended Kalman Filter), ZUPT (Zero Velocity Update) and a magnetometer is disclosed for gait detection and velocity error correction, and by use of an accelerometer and the magnetometer for gyroscope error correction, heading angle error correction can be realized. Actual tests show that the method can well meet indoor pedestrian positioning requirements, and the positioning error accounts for about 2% of the total distance. The method can greatly reduce the error brought by the low accuracy of multi sensors by use of multisensor fusion, and can effectively improve the attitude accuracy compared with an inertial measurement unit. The method has high application prospect and value in robots, LBS (Location Basic Service), indoor pedestrian dead reckoning, and the like, and has wide practicability and generality.

A configuration method for the information fusion of a plurality of optical flow sensors and an inertial navigation device is disclosed, which comprises the following five steps of: 1, establishing a linear disturbance kinematical equation aiming at an aircraft needing to be installed with optical flow sensors; 2, arranging the optical flow sensors on the aircraft in all directions and at a plurality of points; 3, establishing the measurement equation of the optical flow sensors according to the installation position and direction of each optical flow sensor on the aircraft; 4, estimating the flight state of the aircraft by utilizing EKF, that is, extended Kalman filter method and UKF, that is, unscented Kalman filter method respectively; and 5, realizing the specific flight mission of the aircraft by the estimated state information. In the configuration method for the information fusion of a plurality of optical flow sensors and an inertial navigation device, a plurality of optical flow sensors and an inertial navigation device are used; and the optical flow sensors and the inertial navigation device are light in weight, small in volume, low in power consumption, convenient to install and arrange on the aircraft, and unable to radiate electromagnetic signal outwards, thereby enhancing the concealment of the aircraft. The configuration method for the information fusion of a plurality of optical flow sensors and an inertial navigation device has practical value and wide application prospect in the technical fields of measurement and estimation for the attitude, flight speed and height of aircraft.

The invention discloses a high-precision indoor fusion positioning method based on GSM / MEMS fusion. The method comprises the following steps: first, performing position fingerprint positioning by adopting searching and matching by utilizing position fingerprint characteristics caused by multipath and non-line-of-sight transmission of a GSM radio signal; then, resolving the walking speed and the course angle of a target pedestrian according to output data of an inertial sensor device, and obtaining relative position information of a user; finally, fusing position results output by the GSM and the MEMS by utilizing an anti-tolerance extended Kalman filter. According to the method, a position fingerprint positioning result in a GSM environment and the output data of the MEMS are combined by introducing the anti-tolerance extended Kalman filtering algorithm, so that the advantages of positioning systems compensate each other; the influence on positioning precision caused by an accumulative error in an MEMS device positioning and RSSI disturbance in GSM positioning can be overcome effectively; a positioning blind spot can be covered in all directions; a high-precision positioning effect is realized in indoor environment.

The invention discloses a star sensor and gyro combination attitude determination method based on SR-UKF(extended Kalman filter) filtering, and belongs to the technical field of high-precision combined attitude determination of cartographic satellite or other space vehicles. The invention aims at providing the star sensor and gyro combination attitude determination method based on SR-UKF filtering. An SR-UKF filtering algorithm is used for star sensor and gyro combination attitude determination; great improvement is realized on the existing conventional EKF filtering method. The method concretely comprises the following steps of 1, simulating the star sensor quaternion number and gyro angle speed; 2,using the error quaternion number and gyro random drift error as state variables; using theSR-UKF algorithm for realizing the fusion processing of the star sensor and gyro attitude information for filtering processing; performing feedback; possibly eliminating the error influence of the star senor and gyro error influence through iteration wave filtering processing; solving high-precision attitude information.

The invention discloses a millimeter wave radar and monocular camera information fusion method for a smart car. The method mainly comprises three stages of in the first stage, firstly installing and adjusting the position of a camera and a millimeter wave radar on the smart car, using a Zhang Zhengyou calibrating method to obtain internal parameters of the camera, and finally projecting an imagingplane of the camera to a vertical view to satisfy the condition that the target information recognized by two sensors is in the same vehicle coordinate system; in the second stage, training an image-based multi-target recognition model by using a deep learning method offline, in the process of driving the smart car online, utilizing the radar and the camera to collect data in real time, converting the identified target information into a unified vehicle coordinate system and eliminating an invalid target; in the third stage, using a global nearest neighbor matching algorithm to judge whetherthe target is consistent or not, and using an extended Kalman filtering wave to track a matched target and an unmatched target to achieve multi-target recognition. The method has high target recognition rate and reliable safety, and can effectively improve the practicality of the smart car to the environment.

The invention relates to the technical field of positioning, and discloses a positioning method, apparatus, a robot, and a computer readable storage medium. The positioning method comprises steps: thefirst pose information of a target object at a last moment predicted by an extended Kalman filter model is acquired; the second pose information of the target object at the current moment acquired bya sensor is acquired; based on the first pose information and the second pose information, the third pose information of the target object at the current moment is predicted through the extended Kalman filter model; and the error value of the third pose information under a preset constraint is determined, the third pose information is corrected according to the error value, and the final pose information of the target object at the current moment is obtained, wherein the preset constraint is determined according to a constraint equation for characterizing the current motion mode of the targetobject. Thus, the positioning accuracy is improved.

The invention discloses a gas state quantitative analyzer based on the extended Kalman filtering theory for simultaneous online estimation of concentration and temperature of gas to be detected. The hardware portion is composed of a light source, a receiving system, an optical detection system and a computer, and the software portion is composed of a gas state space model and an extended Kalman filtering portion. Light wave emitted from the light source passes through the gas to be detected and enters the optical detection system through a receiving system, and the optical detection system acquires transmittance corresponding to each wavelength of the light wave and transmits corresponding light intensity spectral data to the computer. The computer constructs a measurement equation based on gas absorption intensity data by gas state analysis software to obtain a gas state equation, thus inversing the concentration and the temperature of the gas by using the extended Kalman filtering algorithm.

The invention relates to a mixed rapid estimation method for the residual energy of a battery. The prior method can not satisfy online detection requirements and has poor precision. The method comprises the following steps: firstly, estimating the initial residual energy of the battery by an open-circuit pressure method; then, alternately using an extended Kalman filtering method and an ampere hour method to estimate the residual energy of the battery; and directly using the extended Kalman filtering method to estimate the residual energy when the residual energy is very low. The method can conveniently and rapidly estimate the residual energy of the battery and has high velocity of convergence and higher estimation precision.

The invention provides a lithium ion battery cycle life predicating method based on a cycle life degeneration stage parameter ND-AR (neutral density-autoregressive) model and an EKF (extended Kalman filter) method, and relates to the lithium ion battery cycle life predicating method. The method comprises the steps that the volume data of a lithium ion battery to be measured is measured in an on-line way, and the data is stored and is preprocessed; the parameters of an on-line lithium ion battery experience degradation model is determined on the basis of the EKF method; an AR (autoregressive) model of an on-line battery is determined through the preprocessed data by a fusion autoregressive coefficient solving method; a battery with the same model as the lithium ion battery to be measured is subjected to off-line state simulation on on-line condition charge and discharge testing, volume degradation models of the lithium ion battery to be predicated and the battery with the same model as the lithium ion battery to be measured are subjected to correlation analysis, the battery volume data in each charge and discharge circulation is compared with the failure threshold value of the lithium ion battery to be measured, a RUL (remaining useful life) is obtained, and the cycle life predication of the lithium ion battery is completed. The lithium ion battery cycle life predicating method is applied to battery life predication.

The invention discloses a cooperation relative positioning method based on INS and GNSS pseudo-range double difference for a VANET. The vehicle-to-vehicle cooperation relative positioning method is high in positioning accuracy and high in applicability. The invention relates to vehicle positioning problems in GNSS signal non-blind zones, and mainly solves a vehicle relative positioning problem under a condition that a GNSS is low in positioning accuracy and is limited in applications. Firstly, accelerated speed information and vehicle GNSS position information which are provided by an INS and corrected by a high-precision odometer are shared through V2V communication between vehicles, so that corresponding information between the vehicles is shared. Secondly, information of the adjacent vehicle and information of the assigned vehicle are integrated and processed through the extended Kalman filtering technology, wherein the information is received through V2V communication, so that vehicle-to-vehicle relative positioning results of high accuracy are obtained.

The invention discloses a hybrid measurement based power distribution network dynamic state estimation method, which comprises the steps of reading current network parameters and a network topology structure of a power distribution network, and forming a node admittance matrix and a branch-node association matrix; reading SCADA data and PMU data of each node of the power distribution network, configuring a mapping relation between the two kinds of data, and processing differences between the two kinds of data; building a state transition equation and a measurement equation which are adapt to PMU / SCADA hybrid measurement; adding a fault-tolerant mechanism to a traditional extended Kalman filter method, and performing state estimation on the power distribution network by adopting the improved extended Kalman filter method. Dynamic state estimation is applied to hybrid measurement, and the accuracy of state estimation is improved. Meanwhile, an adaptive data detection mechanism is introduced, and the accuracy of dynamic state estimation is improved.

The invention relates to a deep sea broadband target depth estimation method based on a fringe interference structure, which comprises a vertical linear array and is conducive to the extraction of thebroadband interference fringe structure. The deep sea broadband target depth estimation method provided by the invention has the benefits that first, the vertical linear array is laid in water, and abroadband signal transmitted by a motion target is received; meanwhile, in an experimental environment, the simulation of interference strip structures with different sound source depths is performedby utilizing a sound field model, experiment and simulated interference fringe tracking are performed by utilizing an extended Kalman filter, tracked fringe position and quantity information is substituted into a cost function, and the cost function is minimized; at this moment, the corresponding sound source depth is a target estimation depth. The deep sea broadband target depth estimation method provided by the invention has the following advantages that the complex sound field model calculation is not needed; an array beam is utilized for output, so that the signal-to-noise ratio is improved; the robustness is good, and the change along with the environment is small; a hydrophone is positioned at the bottom of a sea and is convenient to lay; a submersible beacon can stably work for a long time.

The invention discloses an UWB (Ultra Wideband) and inertial navigation integrated indoor navigation system, and belongs to the technical field of indoor navigation. The UWB (Ultra Wideband) and inertial navigation integrated indoor navigation system aims to solve the problems that global localization failure exists, and a robot is kidnapped due to the fact that an existing indoor navigation system operates on the basis of an unknown environment. According to the UWB (Ultra Wideband) and inertial navigation integrated indoor navigation system, four UWB (Ultra Wideband) labels, an inertial navigation system and a laser radar are arranged on a mobile robot, and four UWB (Ultra Wideband) base stations are arranged in the operating environment of the mobile robot; the pose of the mobile robotis acquired through the geometric constraint relation among the UWB (Ultra Wideband) base stations, the UWB (Ultra Wideband) labels and the mobile robot; the acquired pose of the mobile robot and thepose acquired by inertial navigation system are integrated by utilizing an extended Kalman filter so as to acquire the global pose of the mobile robot; and then the global pose replaces the pose acquired by utilizing a speedometer in the navigation system, and the the global pose and the laser radar data are integrated for localization. The UWB (Ultra Wideband) and inertial navigation integrated indoor navigation system is used for indoor navigation.