80 results about "Filtering theory" patented technology

The invention discloses a determined method for an initial gesture on the basis of Doppler optical fiber gyro strapdown inertial navigation system, which comprises continuously collecting data which is output by an optical fiber gyro and a quartz flexible accelerometer after being preheated, processing the data of the gyroscope and the accelerometer which are collected, finishing a rough alignment of the strapdown inertial navigation system, entering into an extractive alignment after the rough alignment is finished, establishing a dynamic base error equation of a marine strapdown inertial navigation system, employing an optimal control filtering theory to design an electric filter, and doing a filtering estimation, extracting information of ship hull gesture misalignment angle to correct the ship hull gesture when a combined extractive alignment is finished, finishing extractive initial alignment, simultaneously obtaining shift estimated value of the gyroscope, and realizing a drift course of the initial alignment. The method of the invention can realize accurate estimation to a zero drift of the optical fiber gyro when the requirement of accuracy and rapidity is guaranteed.

The invention discloses a method for detecting braking property of a vehicle in a road test based on improved Kalman filtering, which comprises the following steps: on the basis of referring a 'current' statistical model of an engine driven carrier in the navigation field, establishing a system motion model in the vehicle braking process; according to the Kalman filtering theory, using a speed and an azimuth angle outputted by a single-frequency carrier phase single-point GPS (global position system) receiver as the system observed volumes; and high frequently and precisely calculating a plane motion coordinate and the speed in the vehicle braking process, by utilizing the improved Kalman filtering recursive algorithm, thereby calculating and confirming a vehicle braking distance and a mean fully developed deceleration MFDD.

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 discloses a synthetic aperture radar automatic target recognition method which belongs to the target recognition field and mainly solves the problem that the space complexity of the existing synthetic aperture radar automatic target recognition technology is higher and single classifier has low recognition rate. The method comprises the following recognition steps: preprocessing, extracting characteristics, training classifiers and identifying target, wherein the step of extracting characteristics is to extract PCA characteristics of the synthetic aperture radar image, elliptic Fourier descriptor and two-dimensional Fourier transform; the step of training classifiers is based on the extracted three characteristics to separately use K-nearest neighbor method, support vector machine and MINACE filter theory to train three classifiers; and the step of identifying target is to input the extracted synthetic aperture radar image to be identified in the trained three classifiers for classification and finally adopting the Dempster-Shafer evidence theory to fuse the recognition results of the three classifiers. The method has the advantages of high recognition rate and low space complexity and can be used in the target tracking of the military or civilian field.

The invention discloses a gas density quantitative analyzer based on correct Kalman filter theory, wherein the hardware part is composed of a light source, a receiving system, a spectrum device, a data line and a computer, the correct Kalman filter algorism of the software part is composed of a standard Kalman filter algorism and a noise parameter self-adaptive evaluation algorism. The light of light source is absorbed by object gas into the receiving system to enter into the spectrum device which obtains the transmissivity relative to the wavelength, to output spectrum data via the data line to be fed into the computer. The spectrum data in the computer is built into a measurement equation, which density distribution changes along time, to build a gas density equation, to be treated by correct Kalman filter algorism, to reversely obtain the gas density.

The invention relates to an intelligent control method for coordinating a multiple-robot system. The multiple-robot system consumes excessive energy in the traditional method. The intelligent control method comprises the following steps of: according a kinematic model of a smell source releasing smell molecules, establishing an observation mode of a robot to the position of the smell source; within each sampling period, in case of any smell detected, estimating the position of the smell source according to Kalman filtering theory and the observation value of the robot to the position of the small source; based on the estimation value of the robot detecting the thickest smell source in the group to the position of the smell source, updating the estimation value of each robot to the position of the smell source, and using the estimation of the robot to the position of the smell source as the basis of estimation of the next position for the robot. The invention effectively improves the precision of positioning the smell source, guarantees less consumption of energy of the robot system, and meets the requirement for quick positioning in actual demands.

The invention relates to a method of master-slave object cooperation positioning for cluster space robots, and belongs to the technical field of spacecraft navigation. The method comprises the steps that geometrical relationships among members in a cluster and an object spacecraft are taken full advantage of to establish an indirect measuring equation of relative positions of the members without measuring functions to the object spacecraft; and a master space robot M provides relative position information to a slave space robot Ci at a long-distance stage according to the relative position information of the object spacecraft and the slave space robot Ci to the master space robot M, and adopts a Kalman filtering theory to achieve the cooperation positioning. With the adoption of the method of the master-slave object cooperation positioning for the cluster space robots, the quantity of relative measurement equipment in the cluster can be decreased obviously; the complexity of systems of the members is reduced; and the reliability of the method is improved.

The invention relates to a vehicle roll-over index prediction method based on gravity center height online estimation. The vehicle roll-over index prediction method comprises: establishing a vehicle roll-over dynamic model according to a dynamic base theory; obtaining a parameter identification model related to gravity center height through an adaptive filtering theory, wherein the parameter identification model only requires the lateral acceleration and the relative roll angle sensor signal as the input; based on the parameter identification model, providing an adaptive rate driven by parameter error information by designing a filtering matrix so as to achieve the online real-time adaptive estimation of the gravity center height; and finally obtaining the roll-over index prediction method based on the gravity center height online estimation according to a horizontal load transfer rate basic formula. The vehicle roll-over index prediction method of the present invention has advantages of high reliability, good real-time performance, less signal measurement demand, low cost, and good universality.

The invention discloses a distributed passive radar target detection method under a direct wave-free condition, and belongs to the technical field of distributed passive radar target detection. A conventional passive radar target detection method is based on the classical matched filtering theory, and approximately optimal detection properties can be achieved on premise that a direct wave with a relatively high signal-to-noise ratio can be acquired in real time and an emitted signal can be estimated with high quality by using a direct wave signal received by a reference channel. To solve the problem of detecting a target which cannot receive the direct wave signal in practical, the invention discloses the distributed passive radar target detection method under a direct wave-free condition, and a concentrated target detector under the direct wave-free condition is established, so that target detection can be implemented when the signal-to-noise ratio of the direct wave signal is relatively low or the direct wave signal cannot be received in a multiple-input multiple-output geometric structure, and meanwhile the target matching problem which is very hard to solve among different receiving stations of a distributed passive radar is indirectly avoided.

The invention discloses a fault detection method for Kalman filtering sensor information fusion. The method includes the steps of: 1. according to a Kalman filtering theory, establishing a state equation and an observation equation of a linear dynamic system; 2. according to the observation equation obtained in step 1, making use of a least square process to acquire state estimation, a corresponding mean square error matrix and an innovation sequence; 3. utilizing the known innovation sequence to acquire different channel-normalized new innovation sequences, and composing an innovation matrix of an m channel parallel sensor; 4. acquiring the spectral norms and spectral norm mean value of the innovation matrix according to the innovation matrix obtained in step 3; and 5. carrying out fault detection on the Kalman filtering sensor information fusion. The method provided in the invention adopts mathematical statistics and interval estimation, simplifies complex calculation, and substantially improves the fault detection speed.

The invention discloses a robust adaptive optimal control-based pilot manipulation behavior description method. The method includes the following steps that: an ROCM model is constructed to augment a controlled object; the index function and model parameters of the ROCM model are determined; pilot optimal control gain is calculated; equation discretization is controlled; robust adaptive state estimation is carried out; unmodeled dynamic estimation is carried out; the pilot optimal control gain is corrected; and pilot manipulation vectors are solved. According to the robust adaptive optimal control-based pilot control behavior description method of the invention, characteristics of memory finiteness and high adaptation of human beings are combined, an improved manipulation noise and observation noise model is adopted, robust adaptive filtering theories are introduced, and therefore, the shortcomings of a traditional pilot model can be overcome, and characteristics of human beings such as memory finiteness, unknown environment adaptability, manipulating behavior robustness and coupled manipulation can be embodied.

The invention provides a low-illumination image / video enhancement method based on self-adaptive multiple-dimensioned filtering. Based on an atmosphere light scattering model, self-adaptive filtering based on an area characteristic, and a self-adaptive brightness restoration theory, the self-adaptive multiple-dimensioned filtering theory and method brought forward by the invention in the field of low-illumination video and image enhancement can enable the brightness of a processed image to be improved and improves image or video contrast and sharpness at the same time, a typical mean value filter is employed, and the calculation complexity is low; and internal parameters are adaptively calculated, the adaptation scope is quite wide, the method can be applied to a color image or a gray scale image, and the versatility is good.

The invention belongs to the technical field of communication, relates to an implementation method for a digital channelized filter bank and provides a digital channelized filter bank implementation technology based on low computation burden, low resource consumption and high adaptability of a multi-channel filter and application thereof in implementation of digital channelization. When the required frequency resolution is changed, only coefficient of a lowpass prototype filter needs to be calculated again, and a new filter coefficient is reallocated into a multi-channel filter bank of the original structure according to multi-phase decomposition and multichannel filter theories, and an expression and an implementation structure do not need to be deduced again, so that the implementation method provided by the invention is applicable to platforms such as FPGA and DSP, and digital channelization processing is realized.

The invention discloses a parameter estimation and tracking control method based on a turntable servo system, and belongs to the technical field of parameter identification and electromechanical control. The method comprises analyzing the turntable servo system containing unknown parameters, establishing a mathematical model of the turntable servo system containing unknown parameters according to a mechanism modeling method; updating an adaptive rate by a filtering theory, introducing a performance index function with a forgetting factor, and designing a variable-gain adaptive rate to achieve optimal adaptive parameter estimation by optimizing the performance index function; and achieving position tracking control by using the controller of a sliding-mode control design system using a super-twisting algorithm. The method can achieve the parameter estimation and tracking control of a system and has advantages of (1) reducing a system parameter estimation overshoot and accelerating the convergence of parameter estimation; (2) ensuring that a tracking error converges to zero within finite time; being not required to obtain the derivative information of a sliding-mode variable or not requiring a continuous control law of the sliding mode; eliminating chattering and improving the robustness of the controller.

The invention discloses an attitude heading measurement system and a fusion method thereof, and a storage medium. The fusion method comprises the following steps of acquiring real attitude angles psi,gamma and theta of a car body by weighting of a three-antenna GNSS (Global Navigation Satellite System) and an accelerometer, wherein psi is a heading angle of the car body, gamma is a roll angle ofthe car body and theta is a pitch angle of the car body; acquiring zero offsets deltabx, deltaby, deltabz of a gyroscope; and performing fusion processing on the attitude angles psi, gamma and theta of the car body acquired by weighting of the three-antenna GNSS and the accelerometer and the zero offsets deltabx, deltaby, deltabz of the gyroscope through a Kalman filtering model to obtain an optimal estimation value of the attitude angles of the car body. According to the attitude heading measurement system and the fusion method thereof, and the storage medium, a weighted observation is performed on an output angle of the gyroscope by using the angles output by the accelerometer and a three-antenna GNSS receiver; the angles are fused on the basis of a Kalman filtering theory; the measurement precision is high; and the high requirement of the modern agricultural machinery navigation on the precision of the attitude angle can be satisfied.

The invention discloses a pulse attenuation plunger pump based on a resistor-capacitor (RC) filter theory. The pulse attenuation plunger pump comprises a plunger pump, a load, an oil tank, a front end cover, a shell, a piston and a spring, wherein the oil outlet of the plunger pump is divided into two paths, one path is connected with the oil tank through the load, and the other path is communicated with a high-pressure oil damping hole on the front end cover of the shell through a pressure oil path; the piston and the spring are arranged in the shell from top to bottom; a low-pressure oil damping hole at the lower end of the shell is connected with an oil leakage port of the plunger pump through a low-pressure oil path and then connected with the oil tank. The pulse attenuation plunger pump has the advantages of simple structure, small volume and light weight and can well attenuate flow pulses generated by various reasons inside the plunger pump; the normal work of the pulse attenuation plunger pump is not influenced by the displacement of the plunger pump; and the pulse attenuation plunger pump is suitable for various constant and variable working conditions.

The invention discloses a method for controlling the level of a digital audio signal. The method mainly comprises the following steps: searching the maximum sample absolute value and the average sample absolute value of a current-frame audio signal by taking each frame of audio signal as the unit, comparing the maximum sample absolute value of the current-frame audio signal with the maximum sample absolute value of a previous-frame audio signal, judging the signal strength change tendency so as to obtain different first-order recursive smooth filter parameters, adjusting the maximum sample absolute value of the current-frame audio signal, obtaining an expected gain by utilizing an expected sample absolute value, and finally, obtaining a practical gain adjustment value of the current-frame audio signal in combination with a first-order recursive smooth filtering theory. According to the invention, the strength change of a signal source is judged according to the maximum sample absolute value, the sample absolute average value, the expected level and the pre-set parameters of a voice-frame signal; configuration parameters are updated; the gain value is obtained in combination with first-order smooth filtering; therefore, the algorithm complexity is reduced; the self adaptability of an algorithm is improved; and distortion rate and volume requirements of output audio signals can be satisfied.

The invention provides a low-frequency oscillation signal parameter identification method based on H infinite extended Kalman filtering. During low-frequency oscillation signal parameter identification based on an H infinite filtering theory, the influence on model uncertainty is effectively considered, and parameter identification errors caused by model parameter uncertainty are avoided. Due to the fact that a noise covariance matrix self-adaptive technique is adopted, a covariance matrix is dynamically adjusted, accordingly the proposed method has stronger robustness, and obtaining of more accurate low-frequency oscillation signal parameter identification results is facilitated.

The invention discloses a horizontal array motion aperture synthesis positioning method and a device therefore. The horizontal array motion aperture synthesis positioning method comprises: decomposing the underwater acoustic data collected by the array elements of a horizontal array into a plurality of time units; evaluating the phases of each narrowband time unit laminated spatially between the array elements, to obtain a phase evaluation factor, processing time delay sampling and phase compensation on the array element time units; processing aperture synthesis for the time sequence obtained by different array elements in one narrow band, to form a large aperture virtual horizontal array; according to zero point control full normal mode filter theory, processing zero point control mode filtration on the large aperture virtual horizontal array, to obtain target mode information; using the target mode information to obtain three-dimension positioning result of the target according to maximum likelihood method. The method and device can obtain high positioning accuracy, with simple and practical process. The invention can realize real-time application without much additional computing resource.

The disclosed correction algorithm based on Wiener filtering theory from in-depth analysis of random signal process theory. Under support of modern signal process technique (mainly, adaptive filtering technique), a structure of digital filter is constructed. The invention applies analysis of filter structure to issue of non-uniform correction of not refrigerative infrared focal plane. Advantages are: higher correction accuracy in real-time.

The invention discloses a longitudinal control method for a hypersonic flight vehicle based on online identification of an aerodynamic parameter. The method firstly employs an iteration extension Kalman filtering algorithm combining an extension Kalman filtering algorithm and an iteration filtering theory to perform the online identification on the aerodynamic parameter, and provides a more accurate model for subsequent control; and then, based on the identified aerodynamic parameter, a adaptive filtering back-step controller is designed to precisely controls a longitudinal equation of the hypersonic flight vehicle. Compared with a traditional extension Kalman filtering algorithm, the iteration extension Kalman filtering algorithm employed by the method is higher in precision, and can identify the aerodynamic parameter more accurately. A control strategy proposed by the invention can effectively overcome a phenomenon of "inconsistent parameters of heaven and earth", the accuracy of a model is improved through the online identification, and the pressure of a control system is alleviated.

The invention aims to overcome the defects existing in the design of the existing disturbance observer, and particularly, aims at the problems that the stable operation of a motor is affected by the deviation of a predicted control output voltage from an accurate value due to the voltage disturbance caused by the inductance change in the operation process of the motor. A disturbance suppression method of a permanent magnet synchronous motor is provided by combining an extended Kalman filtering theory, and a disturbance observer based on extended Kalman filtering is adopted, so that system disturbance caused by parameter mismatch can be accurately observed. Compared with a traditional method, the method has the advantages of being simple in design, good in dynamic and static following performance and the like.

The invention provides a hybrid power filter harmonic current detection method based on the generalized weighted morphological filtering theory. The method includes the steps of firstly, selecting main structure elements and driven structure elements; secondly, constructing a master and slave opening-closing iteration morphological filter and a master and slave closing-opening iteration morphological filter; thirdly, adding the product of F[oc](n) and the sine value of the base wave positive sequence voltage to the product of F[co](n) and the cosine value of the base wave positive sequence voltage so as to obtain the direct current component of the base wave positive sequence current; fourthly, conducting dq coordinate inverse conversion and alpha beta coordinate inverse conversion on the direct current component, obtained in the third step, of the base wave positive sequence current so as to obtain a base wave positive sequence current signal, and subtracting an input signal from the base wave positive sequence current signal to obtain a harmonic current signal. According to the method, the harmonic current can be accurately detected, a traditional low-pass filter is replaced by the generalized weighted morphological filtering link, and the minimum error compensation is achieved. Simulation and experiment results show that the method has the advantages of being high in detection accuracy, good in compensation effect, and the like.

The invention discloses a divided lane traffic flow proportion prediction method based on Kalman filtering, and belongs to the technical field of transportation. Recursive cyclicity of the Kalman filtering theory is used, when the traffic flow proportion of a lane i is predicted, traffic flow proportion data of all lanes in the first three moments are called, the defect that a previous method onlyuses the proportion of traffic flow in the first three moments of the lane i when predicting the proportion of the traffic flow in the lane i is overcome, historical data is fully used, and the prediction precision is effectively improved. R language programming is used for obtaining an estimated initial value of a state vector by a at least square method, rapid convergence in the filtering process is facilitated, the deficiency of given initial parameters in the previous method is remedied, and the reliability of a prediction result is higher. According to the divided lane traffic flow proportion prediction method based on Kalman filtering, the process is simple, calculation is facilitated, and the operability is higher.

The invention discloses a road surface unevenness identification method based on a Kalman filtering theory, and belongs to the technical field of vehicle road unevenness identification in vehicle engineering. Firstly, road contour recognition is defined as a semi-vehicle model inverse problem in a state space, and vertical acceleration signals of the vehicle body, front wheels and rear wheels are collected on a calibrated road surface to serve as measurement data of the method; the collected data is inputted into an established road surface unevenness recognition algorithm to obtain dynamic response of the vehicle so as to reversely deduce road surface unevenness information, wherein the road surface unevenness identification algorithm is established based on the Kalman filtering theory. Only one kind of measurement data such as acceleration signals needs to be collected, the acceleration sensor is easy to arrange, the hardware cost is low, and operability is high; the method not only can be used for predicting the road excitation of the vehicle in the early stage of vehicle design, but also can calculate the response of the vehicle to any given vehicle speed.

The invention discloses a tracking controller design method for packet loss network control systems. According to the method, a discrete linear system model with observation is used for describing network control systems which have packet loss caused by equipment failure, network congestion, routing information error and the like; an augmented matrix method is used for establishing an augmented system model related to a reference trajectory; under a limited time domain, an optimum output feedback tracking controller design scheme related to both a state and the reference trajectory is put forward on the basis of a new maximum principle, a differential Riccati equation, a Calman filtering theory and a Lyapunov theory, and optimum estimation of the state is given; and in addition, under a limitless time domain, an optimum tracking controller design method capable of stabilizing systems is put forward according to the Lyapunov theory and an algebra Riccati equation. According to the method, a controller design method with an output feedback form is put forward, so that the stability of systems is ensured and favorable tracking performance is provided.

The invention discloses a radiation source individual characteristic enhancement method based on a time-varying filtering theory, and relates to a radiation source individual characteristic enhancement method. The objective of the invention is to solve the problems of low individual identification accuracy of an existing radiation source and identification failure caused by change of main signal parameters. The method comprises the following steps of: 1, performing time-frequency distribution calculation on a multi-component signal, and extracting time-frequency information to obtain time-frequency information of each signal component in a main signal component; 2, recovering and separating signal components in the main signal components one by one based on a time-varying filtering algorithm of order time-varying short-time fractional Fourier transform to obtain an estimation result of the sum of the main signal components; 3, subtracting an estimation result of the sum of the main signal components from the radiation source signal to obtain a residual component of the multi-component signal; 4, performing feature extraction on the residual component, and constructing a feature vector; and 5, inputting the constructed feature vectors into a classifier, and outputting a classification and recognition result. The method is applied to the field of radiation source individual characteristic enhancement.

The invention relates to a real-time residual life prediction method of a gear based on multi-degradation monitoring, and belongs to the technical field of mechanical reliability. The method comprisesthe implementation steps of: (1), monitoring degradation of a gear in a main test gear box in real time by utilizing an acceleration sensor and a noise sensor; (2), performing characteristic extraction and recession evaluation on the degradation state of the gear; (3), respectively modelling the vibration acceleration and noise of the gear box by adopting kernel estimation and random filtering theory methods, obtaining the residual life probability density function of the gear box, and obtaining a single-degradation residual life edge distribution function; (4), representing the random correlation between the vibration acceleration and the noise of the gear box by utilizing a Copula function, and obtaining a joint distribution function of the residual life of the gear box; and (5), obtaining a residual life joint probability density function thereof according to the residual life joint distribution function of the gear box, and finally, obtaining the residual life prediction value ofthe gear box. The method disclosed by the invention has the advantages that: the degradation state and the real-time residual life of the gear are effectively predicted; and basis is provided for preventative maintenance of the gear.

The invention discloses a GPS (global positioning system) signal high-speed capturing device and method, belonging to the technical field of the satellite positioning and navigation. The matched filtering theory is adopted by a GPS receiver to capture the C / A code to complete related despreading algorithm at high speed in real time, and a sliding correlator is adopted to realize the tracking of the C / A code according to a matched filter. The I and Q baseband signals are synthesized before matched filtering, the influence of phase deviation is eliminated, the problem that I / Q matched filtering is needed to realize direct sequence spread spectrum signal capture by adopting the matched filtering in the prior art is overcome, and a great quantity of hardware resources is saved; and meanwhile, spread spectrum code synchronizing information is ensured to be obtained in a code element cycle, the capturing speed is increased, the production cost of the device is reduced, and simultaneously, the usability of a GPS wireless terminal is enhanced.

The invention relates to a continuous tracking method suitable for target grabbing of an underwater robot, and belongs to the technical field of visual target tracking. The method comprises the stepsof establishing a kernel-related motion model; performing HOG feature extraction; establishing an appearance model; and updating and iterating the model. According to the invention, based on the kernel correlation filtering theory, dense sampling is realized through cyclic displacement of base samples, so that a richer training sample set is extracted, HOG features are extracted, the appearance model of a tracking target is established, a kernel function is introduced, so that the nonlinear regression problem is solved, the calculation efficiency is improved, whether reinitialization trackingis needed or not is judged according to a feedback result, a self-discrimination mechanism based on system confidence is provided, and continuous tracking of the target is realized. According to the method, stable tracking of the underwater target can be guaranteed, shielding and mistaken tracking conditions can be automatically judged, so that re-identification and re-tracking are realized, and continuous tracking and reliable grabbing of the underwater target are further completed.