482results about How to "Achieve estimates" patented technology

The invention discloses an execution function evaluating and training system for autism spectrum disorder children, wherein a problem of evaluating and training the execution function of the autism spectrum disorder children is settled by means of advanced man-machine interaction technology and brain information sensing technology. The execution function evaluating and training system comprises the components of a student intelligent terminal, a portable electroencephalogram, a portable set-top-box, a cloud server and a parent handheld smart phone. The functional units are connected through wireless communication technology, wherein the student intelligent terminal provides a user interaction interface and records a user face expression image and spatiotemporal dynamics behaviors of buttons in real time. The portable electroencephalogram records the brain wave rhythm of the user. The data set-top-box is a system data collecting and analyzing center and analyzes the collected data through an algorithm for realizing sensing and estimating of the execution function on the user. Furthermore the man-machine interaction interface is constructed for realizing execution function training based on the analysis. The parent handheld smart phone acquires the execution function evaluating and training result of a child through the cloud server.

The invention discloses a multiple input multiple output (MIMO) radar array position error self-correcting method based on a genetic algorithm, belonging to an MIMO radar array position error self-correcting method. The method comprises the following steps of: carrying out one-dimensional spectral peak search according to an ideal array element position by utilizing a MUSIC method to obtain the initial value theta k of direction of arrival (DOA); generating an initial group, carrying out individual fitness evaluation according to f(G), and finding out f max; carrying out genetic evolution operations of selection, crossing, heteromorphosis and the like to generate a new generation of search group, and carrying out fitness evaluation to reduce a deviation factor sigma; revising the theta k according to the optimal individual in the population; and carrying out genetic iteration until a greatest iteration number is set. By using the method, online estimation and correction of array position error can be finished while the DOA estimation is carried out, thereby the robustness of estimation of system parameters is improved.

The invention discloses a multiwave beam-based depth-sounding joint inversion method for a sound velocity profile and a submarine topography. The method comprises the following steps of: (1) transmitting multiwave beams to the seafloor of a sounded sea area through a transmitting transducer array of a multiwave-beam depth sounding system, and receiving echo signals through a receiving transducer array of the multiwave-beam depth sounding system; (2) obtaining the arrival angle and the arrival time of echoes according to the received echo signals by using the multiwave-beam depth sounding system; (3) establishing a state space model formed by using a state equation and a sounding equation; (4) obtaining inversion values of the sound velocity gradient and the seafloor depth of the sounded sea area by using a sequential filter method according to the established state space model and the arrival angle and the arrival time of received echoes, and obtaining an estimate of the sound velocity profile of the sounded sea area by using the inversion value of the sound velocity gradient; and further, calculating the water temperature profile of the sounded sea area by using the estimate of the sound velocity profile. According to the multiwave beam-based depth-sounding joint inversion method for the sound velocity profile and the submarine topography, disclosed by the invention, the estimates of the sound velocity profile and the depth of the seafloor can be obtained quickly and accurately.

An embodiment of the invention provides a people flow volume statistical method, apparatus and system. The method includes the steps of acquiring and storing terminal characteristic data sent by an access point (AP) device through a server device, wherein the terminal characteristic data comprises a terminal identifier, a signal strength identifier, time information and position information corresponding to the time information; integrating the terminal characteristic data in accordance with the time information through a multiple point-in-time intersection algorithm, and storing a processing result in a terminal information table corresponding to the AP device. The existing bus-mounted AP device is used for collecting a terminal signal in order to estimate the people flow volume on roads where buses pass by and the number of passengers on the buses, and meanwhile, locations and movement states of terminals can be analyzed on the basis of the terminals and the signal strength of the terminals, which are acquired through different AP devices at different times in different locations, so that the people flow volume statistic can be achieved for providing objective data reference indexes for operators.

The invention provides a trackless positioning navigation method and device. According to the method, the moving distance of a robot in a moving direction between adjacent time points (t-1) and t and the turning rate of the robot on a horizontal plane are measured, a position predication value of the robot is calculated through a preset dead reckoning equation and the measured moving distance and turning rate, local feature information in laser data obtained by measurement of a laser sensor is extracted and fused into a unified global feature map by the SLAM (Simultaneous Localization And Mapping) technology, and the processes of estimating the position of the robot and generating a topological graph are achieved, so that the positioning navigation stability and reliability are improved on the basis of reducing the cost and the operation and maintenance expenses, and the moving needs of the robot in a natural environment are met.

The invention provides a combination automatic control method with a single-joint manipulator under mixed suspension microgravity environments. The combination automatic control method comprises the following steps of 1, enabling a combination to be equivalent to an underwater robot, and establishing a kinematics equation and a dynamics equation; 2, approximating the dynamics equation of the combination by a radial basis function neural network, so as to obtain control force and control torque corresponding to the radial basis function neural network; 3, using a sliding mode control method, so as to obtain control force and control torque corresponding to sliding model control; 4, synthesizing the control force and control torque corresponding to the neural network and the control force and control torque obtained by the sliding model control method, and distributing thrust, so as to obtain a general vector which consists of thrust and joint torque of each propeller; approximating the thrust deviation of the corresponding thruster through the radial basis function neural network, so as to obtain the estimation value of the thrust deviation; 5, combining the results obtained in step 2, step 3 and step 4, obtaining the general vector consisting of the thrust and the joint torque of the corresponding propeller, and further obtaining the thrust and the joint torque of the corresponding propeller, so as to realize the automatic control.

The invention discloses a method for controlling postures of an aircraft converged at a fixed time, relates to an aircraft posture control method, and belongs to the technical field of aircraft control. The method comprises the following steps of (1) establishing a reentry aircraft dynamic model and offering a limited time posture tracking task; (2) performing feedback linearization treatment on a model established in the step (1); and (3) giving out a limited time control law, starting the state of a system from an optional initial value and consulting a trace on tracking within a set time. The method can be used for implementing system error fixed time convergence when part of initial value information is unknown. Moreover, the method has high robustness under the conditions that system parameters are uncertain and external interferences exist.

This is a nonlinear dynamic state estimation method based combined measurement by PMU and SCADA. A static module is established on the history data of SCADA. In case of non-measurement for part of nodes, conduct an accurate measure on adjacent nodes by PMU. An estimation on the non-measured nodes is made by Gauss-Seidel method.

The invention discloses a method used for positioning indoor moving targets by improved unscented Kalman filtering. In order to solve problems of traditional unscented Kalman filtering method that positioning precision is low and real-time performance is poor, the traditional unscented Kalman filtering method is improved by an iteration algorithm. Wherein according to the iteration algorithm, a noise covariance matrix is regulated dynamically by using scale factors, i.e. in filtering processes, filtering values of last moments are continuously substituted into a time updating equation; prior estimation of current states is established; estimated values of state variables of current states and estimated values of error covariance are calculated; and observation noise and systematic noise are updated according to the estimated values so as to ensure convergence and stability of the algorithm.

The invention relates to a combined navigation method based on polarization information. The combined navigation method comprises the following steps: firstly, checking astronomical ephemeris according to carrier positions and moments so as to obtain sun position information; secondly, measuring by using a polarization sensor so as to obtain polarization information, and measuring speed information by using a light stream sensor; thirdly, on the basis of polarization and light stream, establishing a filtering measuring model; and finally, estimating navigation error information by using a kalman filter. By adopting the combined navigation method, a linear measuring model based on single-polarization sensor is established, and the model is simple, small in filtering calculation amount, high in precision and applicable to autonomous navigation of the carrier for a relatively long time.

The invention relates to a displacement estimation method of a bearing-free synchronous reluctance motor, comprising the following steps: constructing a band-pass filter BPF: sending coordinate- transformed suspension winding three-phase detection current into the band-pass filter BPF; extracting suspension winding high frequency induction current ix_i and iy_i through the BPF; then constructing a multiplication demodulator: taking the ix_i and iy_i as a first input signal of the multiplication demodulator; constructing a phase shifter, taking pulsation high-frequency voltage signal ud_i having gone through phase shift as a second input signal of the multiplication demodulator; obtaining I' and I signal after demodulation computation is carried out on the two input signals by the multiplication demodulator; then filtering high-frequency component of the I' and I' signal by a low-pass filter LPF, finally obtaining current components comprising rator radial displacement; afterwards multiplying corresponding scale factor to obtain estimated value of the radial displacement of a controller electric machine rator. With the method and the device for controlling the displacement-free sensor, the displacement sensor is not needed and a DSP is adopted to control an electrical machine with the assistance of a software.

The invention discloses a method for estimating OFDM rapid-varying channels in low-density pilot distribution. The method comprises: multiplexing Gaussian-distribution pilot-frequency data and to-be-transmitted data at a transmitting end according to a time-frequency random insertion mode; performing random down-sampling and multiplexing at a receiving end on a frequency far below Nyquist frequency; transmitting received pilot-frequency data which is obtained through multiplexing and corresponds to a pilot-frequency position to perform compressed sensing channel reconstruction; obtaining S nonzero channel values in a channel delay-Doppler sparse domain; and obtaining the channel parameters of all sub-carriers in a frequency domain through channel coefficient zero padding excepting S nonzero positions, as well as delay-Doppler sparse domain inverse transformation processing. The method has the advantages of performing random down-sampling on the frequency far below Nyquist frequency, utilizing the compressed sensing channel reconstruction to filter noise so as to improve the parameter estimation precision of OFDM rapid-varying channels under pilot-frequency distribution conditions low in signal-to-noise ratio and density and realizing high-precision channel parameter estimation and tracking, along with low estimation-calculation complexity and low bit error rate.

The invention discloses a phonetic gain factor estimation device and method, which comprises the following parts: prior signal-to-noise ratio estimation unit, pure phonetic gain calculation unit, conditional phonetic existence probability estimation unit and gain factor calculation unit. The method comprises the following steps: calculating the prior signal-to-noise ratio of input signal; calculating the pure phonetic gain value according to the prior signal-to-noise ratio of input signal; calculating the existence phonetic probability of input signal; calculating the phonetic gain factor of input signal in connection with the self-keep noise signal reduction factor. The invention realizes the estimation of phonetic gain factor without integral and differential calculation, which decreases the demand of memory capacity and calculation quantity in the integer DSP chip operation.

The invention provides a method and a device for applying reference signal configuration information. The method comprises the steps that: a base station acquires reference signal configuration information, wherein the reference signal configuration information at least comprises one of the following: reference signal configuration information of a local cell, reference signal configuration information of a neighboring cell and reference signal configuration information of a terminal in the neighboring cell; and the base station performs transmission or measurement according to the acquired reference signal configuration information.

The invention discloses a method for observing the longitudinal motion state of an aircraft by using for a time varying measurement delay output signal aircraft. The longitudinal motion state observation method comprises four steps, i.e. firstly, acquiring a kinetic model of the aircraft in a pitching plane; secondly, designing an observer; thirdly, adjusting an observation parameter and checkingthe observation effect; and fourthly, finishing the design. According to the longitudinal motion state observation method, the current time state of the system is reconfigured according to a model ofcontaining certain time varying delayed output and system, and thus the observation of the system state is realized. The observation of a longitudinal motion kinetic model of the aircraft can be realized and the observation of other control objects can also be realized. Therefore, the longitudinal motion state observation method has better practical value and favorable application prospect in thetechnical field of flying control.

The invention relates to a MIMO radar extended target detection method, and the purpose is that multichannel process advantage of a MIMO radar is utilized, so that extended targets in a compound Gaussian clutter can be preferably detected. The core idea of the method comprises: according to target echo received by the MIMO radar, estimation of target motion parameters and clutter parameters are performed, a GLRT basic principle, test statistics is acquired, and the detection is complete at last. Target Doppler mismatching, compound Gaussian clutter shape parameters and the like can influence detection performance if GLRT, so that an MLE method is adopted, estimated values of the target Doppler and clutter shape parameters are obtained, and the target detection is performed. At last, advantages of MIMO radar multichannel processing are used for realizing accumulation of target echo energy, so that performance of a GLRT detector is greatly improved, and the performance improvement relates to the number of MIMO radar transmitting and receiving antenna.

The invention provides a rotor position estimation method, a rotor position estimation device and a motor. The rotor position estimation method comprises the following steps: acquiring a first phase current and a second phase current; acquiring a first phase voltage and a second phase voltage; acquiring a speed reference value; calculating a first back electromotive force and a second back electromotive force, and integrating the first back electromotive force and the second back electromotive force to get a first flux linkage and a second flux linkage; processing the first flux linkage and the second flux linkage through a high-pass filter to get a third flux linkage and a fourth flux linkage; calculating a first component of rotor flux linkage and a second component of rotor flux linkage through the third flux linkage and the fourth flux linkage; calculating the rotor position angle according to the first component of rotor flux linkage and the second component of rotor flux linkage; and calculating the rotor speed according to the first component of rotor flux linkage and the second component of rotor flux linkage. The rotor position estimation method provided by the invention improves problems such as phase angle deviation, sensitivity to parameters, and poor work in a weak magnetic area, and is of higher efficiency and accuracy.

The invention relates to a road gradient and car state parameter combined estimation method based on a parameter estimation error, and belongs to the field of vehicle engineering. The method comprises the steps of firstly establishing a car driving longitudinal kinetic model according to a car kinetics theory and the newton second law; measuring a car speed and a driving force of an engine by virtue of a car-borne sensor, and adopting the measured car speed and the driving force as input quantities of an estimation algorithm; filtering the car driving longitudinal kinetic model, and extracting a parameter estimation error; judging whether an excitation condition is satisfied: estimating an unknown parameter vector by utilizing a vector designing self-adaptive law containing the parameter estimation error if the excitation condition is satisfied; adding random interference to the driving force if the excitation condition is not satisfied, and returning for re-measurement; finally calculating estimated values of a road gradient, car weight, viscous friction coefficient, rolling friction coefficient and air resistance coefficient by virtue of basic mathematical manipulation. By adopting the method, the cost is reduced, the accumulation of measurement noise can be avoided, and the precision is improved; the calculation quantity is reduced.

The present invention discloses an inhomogeneous sensor array broadband signal direction-of-arrival (DOA) estimation method. The method comprises the following steps of: S1, performing sliding windowfast Fourier transform of a sample sequence in an inhomogeneous sensor array, and constructing an actual value weighting sample covariance vector yl; S2, constructing a corresponding over-complete array flow-pattern matrix [Phi]l; S3, employing the actual value weighting sample covariance vector yl and the over-complete array flow-pattern matrix [Phi]l to set an initial value [gamma]init of a space joint sparse representation vector [gamma] and an initial value [lambda]init of a regularization parameter [lambda]; S4, employing the actual value weighting sample covariance vector yl and the over-complete array flow-pattern matrix [Phi]l to update the space joint sparse representation vector [gamma] and the regularization parameter [lambda]; S5, when the [gamma] obtained through the ith iteration and the [gamma]<i+1> obtained through the (i+1)the iteration meet ||[gamma]-[gamma]<i+1>||2/||[gamma]||2<[alpha] or achieve the maximum iteration times Pmax, stopping the iteration, or else, returning the step S4; and S6, searching all the peak values of the space joint sparse representation vector [gamma], and outputting DO estimation values of the broadband signals.