36 results about "Filter 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 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 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 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 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 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 concerns a method for determining the fouling ratio of at least one filter of a ventilation and / or air handling system(s), comprising the following steps: choosing at least one filter to be analyzed B, determining the theoretical filtration capacity of the filter selected in step A by depending on the theoretical efficiency of the filter and the maximum amount of dust which can be theoretically retained by the filter, C collecting pollution data of at least one air entering the ventilation system and passing through the selected filter, D measuring the air flow rate entering the ventilation system, E calculating the amount of dust retained by the at least one filter selected, from the pollution data of the entering air of step C and from the measurement of the entering air flow rate of step D, F repeating step E for each time step t, G. determining the accumulated dust amount retained by the selected filter from the results of steps E and F. H determining the fouling ratio of the selected filter by comparing the accumulated dust amount determined at step G with the theoretical capacity of the filter determined in step B.

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 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 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.

A joint precoding method for a multi-user MIMO relay system including a direct transmission link is disclosed. In the scene of transmitting / receiving end multi-user MIMO relay communication, with MSMSE as a design criterion, the invention provides a joint precoding method with the centralized optimization of all users of a transmitting end. According to the MMSE receiving filter theory, the optimal expression of a receiving filter matrix is directly solved. The sub-optimization problem of a sub-precoding matrix of a transmitting end user is obtained by solving a K.K.T condition, the sub-optimization problem of a relay transceiver matrix is transformed into a standard SDP problem for optimization. Finally, the sub-precoding matrix of the transmitting end user, the relay transceiver matrix and the receiving filter matrix are jointly iterated to convergence, and an optimized precoding matrix is obtained. A simulation analysis verifies that a mean square error and bit error rate performance of the proposed method are good, and the system performance is optimal especially in the case of a low signal-to-noise ratio.

The invention discloses a downhole equipment detection rule set reduction method based on discrete Calman filter and belongs to coal mine equipment detection methods. The reduction method of safety rule sets includes establishing geographical position coordinates for an entire coal mine; configuring safety GPS locating equipment to a detecting staff, and determining the current state, including the position and speed, of the detecting staff; then on the basis of the current state, predicting the state in the next moment by the discrete Calman filtering algorithm, and selecting a roadway to be selected. A handheld terminal is free of downloading all safety rule sets of all downhole equipment, only the current rule sets are downloaded; reduction can be performed by the discrete Calman filtering theory, the detecting staff arrives at a roadway 1, the handheld terminal downloads a safety rule set 1, next equipment to be detected is predicted according to the discrete Calman filtering theory, the safety rule sets relative to the equipment is pre-downloaded to the handheld terminal, and the detecting efficiency is improved greatly.

The invention relates to a cooperative spectrum sensing method based on an H infinity filter theory for cognition of a wireless network. The cooperative spectrum sensing method comprises the following steps of detecting the local spectrum for cognition users, and sending the sensing data and the local sensing results to a data integration center; enabling the data integration center to build a state space model of channel gain according to the sensing data sent by each cognition user, and using an H infinity filter method to evaluate the change condition of channel shade fading gain corresponding to each cognition user; calculating the testing statistic amount by the evaluation value of the channel shade fading gain corresponding to each cognition user; comparing the testing statistic amount with the setting threshold value, and determining which cognition user has a Byzantine type network attack activity; deleting the user data and the local sensing result of the cognition user with the attack activity from the data integration center; enabling the data integration center or using an integration criteria to perform decision integration on the local spectrum sensing result of the cognition user, so as to obtain the cooperative spectrum sensing result. The cooperative spectrum sensing method has the advantage that the cooperated spectrum sensing on the authorized users is well realized.

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 provides an integrated traffic collision avoidance (TCAS) reshaping and anti-jamming design in which anti-jamming design is systematically carried out from the aspects of software and hardware. In the invention, aiming at the defects that the design of a hardware filter circuit of the existing TCAS system is backward and the like, a novel and more stable hardware filter circuit is designed according to a Bessel capacitive coupling filter theory, and stable power supply and supply circuit are also designed so as to further improve the anti-jamming capability of the hardware; simultaneously, a finite impulse response (FIR) filter with a strict linear phase and a random amplitude characteristic is designed based on a Megacore function library in a large field programmable gata array (FPGA) especially from the aspect of the software so as to process a low-frequency baseband signal, thereby effectively avoiding the baseband jamming. The stability and anti-jamming capability of TCAS to receive the signal are further improved systematically and effectively from software and hardware aspects.

The invention discloses a big data-based complex panel data learning method. While information increment comprised in a new time sequence is fully utilized, data capacity of the time sequence is controlled, so that the calculation cost and the computer storage capacity are controlled; while dimension information comprised in cross section data is fully utilized, support dimension capacity is controlled, so that the calculation cost and the storage capacity are controlled in a computing resource permission range; under the condition of finite panel data capacity, the generalization performanceof learning is ensured, and a generalization theory of an algorithm is given; and complex panel data white noise filtering theory and method are researched to ensure the robustness of a learning model. A dynamic vector fusion technology is adopted; the computing performance is robust; the problem of the high-dimensional complex panel data of the time sequence and the cross section is solved; a linear learning mode is adopted; and online kernel learning for simultaneous control of bidirectional data capacity is realized.

The invention is suitable for the technical field of the battery, and provides a battery parameter acquisition method and device, and terminal equipment. The method comprises the following steps: establishing an equivalent circuit model of a battery; acquiring open-loop voltages at different battery temperatures; performing parameter distinguishing on the equivalent circuit model according to an open-loop voltage curve under different battery temperatures based on self-adaptive H infinite filtering; and acquiring and displaying battery parameters according to a parameter distinguishing result.Offline data and online data are combined by establishing the equivalent model of the battery, the accurate distinguishing on the parameters of the battery equivalent model under different temperatures and different cyclic life can be realized, the parameter distinguishing is performed based on the self-adaptive H infinite filtering theory, the noise interference is effectively overcome, the battery parameters can be accurately estimated in real time, and the problem that the distinguishing result for distinguishing the battery parameters of the battery model at present is inaccurate is effectively solved.

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 radar clutter inhibition filter FIR design method, and aims to provide a parameter adjustable and flexible and practical FIR filter coefficient forming method; the method comprises the following steps: using MATLAB software to design a FIR filter coefficient that suits a radar signal processing board; in a FIR filter coefficient forming process, setting radar system parameters and clutter parameters as variables with adjustable values, and building a normalization ideal FIR filter set frequency characteristics according to the radar system parameters and application environment demands; using the frequency characteristics to build a clutter covariance matrix; using the optimum filtering theory to calculate the FIR filter coefficient of each channel; finally, iterating and optimizing the FIR filter coefficient so as to reach the ideal FIR filter frequency characteristics, and maximizing an output signal to noise ratio. The method can greatly improve the radar clutter inhibition capability, is flexible, high in precision, and can be applied to special radar systems and special usage environments.

The invention discloses a production technology of an anti-mite pillow interior. The technology comprises the following steps of design preparation of mite-separation fabric and preparation of the anti-mite pillow interior. According to the production technology of the anti-mite pillow interior, based on a filter theory, fiber in a filter material is thinner, the specific surface area is larger, the pore diameter is smaller, and the filter precision and the filter effect are better. Therefore, the density of the fabric is increased, invasion of mites or other allergens is prevented, and then the anti-mite aim is achieved.

The invention discloses a permanent magnetic synchronous motor rotor speed estimation method. The method includes establishing a permanent magnetic synchronous motor mathematical model, and performing vector transformation to decouple; performing order reduction on a system model, and establishing a simplifying filter model estimating the rotor speed and position only; adding stator parameters into the system model, and acquiring an augmented system model containing the generator electromechanical state and rotor parameters; finally, according to the established augmented model, acquiring an estimation value of the permanent magnetic synchronous motor rotor speed and stator parameters by the H8 filter theory. By the aid of the method, the rotor speed and stator parameters can be estimated accurately, the calculation amount is reduced, and the system robustness against parameter uncertainty is improved effectively.

The invention discloses a generator dynamic state estimation method based on adaptive traceless H-infinity filtering, which is used for enhancing the robustness of a dynamic state estimator on model uncertainty and improving the dynamic state estimation precision. According to the method, based on the unscented transformation and H-infinity filtering theories, by introducing an adaptive technology, a state estimation error covariance adaptive adjustment updating strategy capable of inhibiting the influence of model uncertainty on a state estimation result is provided. According to the method,the dynamic state estimation precision of the generator can be effectively improved, and the problem that upper bound parameters are difficult to select due to model uncertainty constraint in a traditional robust state estimation method is avoided. According to the method, the actual engineering background is considered, the implementation process is clear, and high engineering application value is achieved.

The present invention provides a low-illuminance image / video enhancement method based on adaptive multi-scale filtering. The present invention is based on the atmospheric light scattering model, adaptive filtering based on regional characteristics, and adaptive brightness restoration theory, in the field of low-illuminance video and image enhancement , the adaptive multi-scale filtering theory and method proposed can improve the brightness of the processed image, and at the same time improve the contrast and sharpness of the image or video, with the classic mean filter, low computational complexity; adaptively Calculation of internal parameters has a wide range of adaptation, and can be applied to color images or grayscale images, and has good versatility.

Urban sewage and industrial sewage are main causes of water pollution, and more than 80% of the sewage in the world has not been treated effectively, which causes severe damages to security of water for human and a water ecological environment, so that how to removing harmful substances in the sewage and turning waste into wealth through a well-adapted sewage treatment means is a common subject that faced by the current society and urgently needing to be solved. A biofilm sewage treatment technology being a result of a sewage soil-filtering theory has been applied for many years, but selection of biofilm formation packing is still a bottleneck problem that affects the efficiency of biofilm filtering via the laminated packing. The laminated graphene packing based biofilm sewage treating method has the advantages that nitrogen elements, phosphorus elements and the like in the sewage can be removed, an eutrophic water body problem is solved, and compared with normal biofilm formation packing, the laminated graphene packing is simple to prepare, low in cost and easy to recycle and has important values in environmental protection application and a theoretical aspect.

The invention relates to a distributed H infinity filter based cooperative spectrum sensing method. The distributed H infinity filter based cooperative spectrum sensing method comprises the steps that each sensing user in a cognitive wireless network firstly collects and receives wireless signals sent by an authorized user, calculates the energy values of the received signals, then builds a state transition equation of wireless channel shadow fading and performs distributed estimation on the wireless channel shadow fading; then each sensing user performs normalization processing on the energy values of the received signals by utilizing the estimation value of the wireless channel shadow fading and then performs local spectrum sensing; each sensing user sends the local spectrum sensing result to a sensing base station; and finally, the sensing base station performs decision fusion based on the local spectrum sensing result sent by each sensing user to obtain a final cognitive wireless network global spectrum sensing result. As the distributed H infinity filter used for estimating the wireless channel shadow fading received by each sensing user is adopted to remove influences from the wireless channel shadow fading to the spectrum sensing result, the distributed H infinity filter based cooperative spectrum sensing method can detect the spectrum of the authorized user equipment in the cognitive wireless network in a better manner.

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 obtainedby 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.