95 results about "Iterative filtering" patented technology

The invention provides a bearing-only underwater target tracking algorithm based on multilevel information fusion. Three levels are divided. In the first level, a least square method is used for initially tracking and positioning a target, and the estimated state value is calculated as an input value of a second-level AFIMM (Adaptive Fuzzy Interacting Multiple Model). In the second level, the AFIMM is designed, the motion model set of the AFIMM is designed optimally, and a method of a fuzzy adaptive model transition probability is brought forward. In the third level, resampling particle filter is designed as an iterative filtering structure at each step for the AFIMM, and information fusion is realized. During the bearing-only target tracking process for a multi-UUV (underwater unmanned vehicle) cooperative system, through the design of the multilevel information fusion framework, the filter accuracy is higher, and the target of multi-UUV cooperative underwater target tracking can be met.

The invention belongs to the technical field of transfer alignment in inertial navigation, and particularly relates to a method for measuring hull deformation angle based on inertia instruments, such as a gyroscope, an accelerometer and the like, and an iterative filtering algorithm. The method comprises the following steps: 1, evaluating an estimated value (FORMULA) of the deformation angle by utilizing a Kalman filtering method; 2, analyzing the power spectrum of an angular velocity observation residual; 3, using the angular velocity observation residual and a specific force observation residual as Kalman filtering observation variables for iterative filtering; 4, through calculation repetition of the second step and the third step, constantly correcting an estimated result in the first step till a corrected value (FORMULA) of the deformation angle is close to but not equal to a true value (FORMULA). By the method, in allusion to the environmental characteristics that a large ship is low in speed and swings weakly, the measurement accuracy of the deformation angle in a vertical direction can be significantly improved; meanwhile, the method is relatively small in calculation amount, and is suitable for online real-time measurement.

The invention discloses an ore prospecting method utilizing ground high-precision gravity measurement. The ore prospecting method mainly comprises six methods, namely a wave number domain iteration method of a potential field high-order derivative, a potential field separation method based on an iterative filtering method, a TASD method for identifying a boundary of a geologic body, an Euler deconvolution method for potential field data processing, a potential field abnormal cross-correlation imaging technical method and a gravity man-machine interaction profile forward modeling method. The ore prospecting method utilizing ground high-precision gravity measurement has the advantages of processing and explaining data of a plurality of gravity profiles, adopting the explaining methods covering the wave number domain derivative iteration method, the iterative filtering method of field separation and the cross-correlation imaging technology based on residual anomalies to be applied to gravity data, and achieves a good processing result.

The invention provides an iterative filtering method for tracking before multiframe detection. A later target state can be estimated more precisely according to a short track obtained through tracking processing before detection, filtering processing is carried out on the target state, the precision of estimating the target state is improved, a target can be tracked in real time for a long time, and the complete track of the target is obtained. The target state is estimated iteratively, and overmuch memory space and the too large calculated amount are not introduced. According to the method, the precision of estimating the target state is improved, the target can be tracked in real time for a long time, the problem that missing inspection possibly happens to tracking before weak target detection with the low signal to noise ratio is effectively solved through track predication, and the continuity of the track is guaranteed.

The invention discloses a diagnostic method for a pump station unit based on a composite characteristic index and a depth limit learning machine and relates to the technical field of fault diagnosis of hydraulic machinery. The diagnostic method can directly decompose nonstationary time series from original vibrating signals by adopting self-adaptive iterative filtering, so that the nonstationary characteristic of the unit can be extracted effectively. A time domain statistics signal, a frequency domain statistics signal, an energy signal, a sample entropy signal and an arrangement entropy signal are extracted based on each nonstationary signal component. Characteristic learning is performed quickly and effectively by means of the depth limit learning machine, implicit fault information ofeach characteristic is extracted and boundedness of a manual design and characteristic extraction and a complex parameter adjusting process based on an artificial neural network are avoided, so that intelligent diagnosis of faults of a water pump unit is achieved, and therefore the accuracy and the effectiveness of fault diagnosis of the water pump unit are improved.

The invention discloses an image noise filtering method via median and mean value iterative filtering of a minimal cross window, and aims at solving the problems that a present image de-noising method is hard to protect edge details and set parameters during de-noising. The image noise filtering method is realized by the steps that (1) noise points are found via an extremum method, and a binary map whose size is the same with that of an image is constructed; (2) the noise density p is calculated; (3) if p </= 0.525, the iterative median of the minimal cross window is used for filtering, namely, the median of non-noise points in the minimal cross window replaces the value of each noise point in the noise image, iteration is carried out for another two times, whether all the noise points are processed is determined after iteration in each time, and if yes, a de-noising result is output; and (4) if p>0.525, the iterative mean value of the minimal cross window is used for filtering in a way similar to that via the iterative median. The image noise filtering method has the advantages that it is not required to consider the size of windows, operation is easy, the de-noising efficiency is high, details of the image are kept effectively, and the method is more suitable for real-time application.

The invention discloses a ground common-frequency multi-motion radiation source tracking method and system based on time-frequency difference and direction finding, and belongs to the technical fieldof positioning. The ground common-frequency multi-motion radiation source tracking method based on time-frequency difference and direction finding includes the steps: establishing a measurement modeland an iterative filtering model for multi-motion radiation source targets of a dual-satellite time-frequency difference positioning system; determining the iterative stop condition of iterative filtering of the filtering model; and realizing tracking of the common-frequency motion source target in an unknown motion state. The ground common-frequency multi-motion radiation source tracking method and system based on time-frequency difference and direction finding can filter all the fuzzy time difference and frequency difference measurement as the target measurement to avoid understanding the complicated problem of the fuzzy, and can determine the optimal motion target filtering estimation result by setting the weight threshold and realize tracking of the common-frequency multi-motion radiation source. Besides, for UHF and L/S frequency band targets that often receive multiple common-frequency signals at the same time, the ground common-frequency multi-motion radiation source tracking method and system based on time-frequency difference and direction finding has great reference significance for improving tracking of low-frequency band multi-motion radiation sources.

The invention provides image processing equipment and an image processing method. The image processing equipment comprises a filtering device for iteratively filtering an input image, and an iterative filtering stopping device for determining whether iterative filtering is stopped or not according to a change rate of a filtered image obtained by filtering with an iterative frequency in an iterative filtering process.

The invention discloses a method for automatically picking up a seismic reflection event based on Kalman filtering. According to the scheme, the method comprises the following steps of: establishing a state equation and a measurement equation of a Kalman filtering system for picking up seismic reflection event information on the basis of a seismic reflection hyperbolic time-distance equation according to movement rules of seismic reflection waves; detecting the time of arrival of seismic wavelets in each seismic signal, thereby obtaining measurement data for Kalman filtering; and automatically initiating the event according to data in a free measurement point set, and performing gradual iterative filtering according to a sequence of prediction, observation and correction. A data association technology is used for solving the problem of simultaneous tracking of a plurality of events.

The invention discloses a low-illumination color image enhancement method based on a local extreme value, and relates to the field of color image enhancement methods. The method comprises the steps ofstep 1, converting an original color image from an RGB space to a YUV space, and extracting an intensity channel Y of the YUV space as a grayscale image I; step 2, performing iterative filtering on the grayscale image I by using a local extreme value filter with gradually increased cores, and taking a filtering result as an illumination component L of the image; step 3, separating a reflection component R from the grayscale image I according to the illumination component L; step 4, carrying out gamma transformation on the contrast component L, and reconstructing the contrast component L and the reflection component R to obtain an enhanced image. The grayscale image is iteratively filtered by using the local extreme value filter with gradually increased cores, so that the problems of high-brightness region detail loss and low-brightness region contrast enhancement of the enhanced image caused by the existing method are solved, and the color fidelity of the enhanced image, the definition of dark region details and the detail information richness are improved.

The invention relates to a disoperative target trajectory tracking method under multiple observation nodes. The method comprises the following steps: the locating of a disoperative target is realized through a plurality of signal transmitting points and a plurality of observation receiving points in the sea to generate target locating bright points; the target locating bright points near the base line between each pair of signal transmitting point and observation receiving point are rejected; time alignment is performed on the target locating bright points to obtain the target locating bright point in each pulse period; the clustering process is performed on the bright point obtained in each pulse period through clustering analysis, and the bright points which are away from the class center are rejected through Kalman filtering; the bright point obtained in each pulse period is processed, the remaining bright points are averaged, and segmentation fitting is performed on the average value to obtain a target trajectory; segmentation prediction is performed on the target trajectory again, and the bright point which are away from the predicted trajectory are rejected through the Kalman filtering to realize iterative filtering; and the remaining bright points are averaged, and then segmentation fitting is performed through the least square method to obtain the final target trajectory.

The invention discloses a maximum correlation kurtosis deconvolution method without a period. The method comprises steps of firstly, carrying out truncation and mean removal processing of an acquiredvibration signal; performing Hilbert transform of the vibration signal to obtain an analytic signal of the vibration signal; selecting a maximum value point of the part behind a first zero crossing point in the autocorrelation spectrum as an iteration period to carry out deconvolution operation; then, continuously calculating the maximum value point of the part behind the first zero crossing pointin the autocorrelation spectrum of the signal after each iterative filtering as an iterative period to update a filter so as to obtain an optimal filter coefficient; and lastly, performing envelope analysis of the filtered signal, and extracting the fault characteristic frequency from the envelope spectrum. The method is advantaged in that human participation is not needed in the extraction process of the characteristic frequency, automation of fault characteristic extraction and diagnosis monitoring can be achieved, the time is saved, and efficiency is higher.

In a method and an apparatus for filtering a digital image acquired with a medical device by means of a spatial-frequency operator that automatically allow an optimum filtering of the source image, the integral signal-to-noise ratio of the digital image is calculated, a limit value for the integral signal-to-noise ratio of the filtered image is calculated from the integral signal-to-noise ratio of the digital image, the digital image is iteratively filtered with the spatial-frequency operator R with variation of the dosage g and with calculation of the integral signal-to-noise ratio in the filtered image, afterward iteration of the integral signal-to-noise ratio of the filtered image is compared to the limit value for the integral signal-to-noise ratio, the iterative filtering of the digital image with the spatial-frequency operator R is aborted when the integral signal-to-noise ratio of the filtered image reaches the limit value for the integral signal-to-noise ratio or when a range of control (if present) for the dosage g has been exhausted, and the dosage g most recently employed in the iterative filtering is employed as the optimum dosage g_opt for a filtering of the entire digital image under consideration with the spatial-frequency operator R employed.

The invention discloses a data calibration method and device. The method comprises the following steps: establishing a closed loop control system of a filter, performing iterative filtering processing for n times on the to-be-calibrated data by using a self-adaptive filter, wherein the filter parameter used in each iterative filtering processing is the filter parameter obtained after updating the difference between the output data obtained after former iterative filtering processing and the preset reference output data, thereby reducing the error of the output data and improving the data precision.

The invention discloses an optimization method of pipeline surveying and mapping internal inspection IMU data preprocessing. The method comprises the following steps: firstly, determining an estimatedvalue of an initial attitude angle of an IMU by measuring a physical dimension of a ball serving barrel; then performing initial alignment of a static base by using two iterative filtering data to becompared to respectively obtain a course angle and an elevation angle, and calculating alignment errors of the course angle and the elevation angle; and then using a difference value of absolute values of the alignment errors as a criterion to evaluate the effect of the twice iterative filtering. By adoption of the method, the filtering effects of different iterations can be compared to a certainextent to prompt the over-filtering situation and to optimize the preprocessing algorithm. Compared with the manner of directly comparing the matching degree of a solution trajectory and a checkpoint, the method is more efficient, the most of confounding factors are removed from the iteration for terminating the iteration, the method is more convincing, the over-filtering situation is basically avoided, and the optimization of the data preprocessing algorithm is achieved.

The invention provides a joint channel and data estimation method and a system for a fast time-varying OFDM channel. According to the method, the system presets a Doppler model for a wireless channel, an input output model formed by at least one tap sequentially arranged based on preset time delay, and a channel state equation for the wireless channel built by a correlation matrix formed based on the Doppler model and all channel impact responses, wherein each tap in the input output model is provided with the corresponding channel impact response; and the state parameter in the channel state equation is formed by each channel impact response; based on the multiple data from the wireless channel and the input output model, a channel measurement equation based on the state parameter for the wireless channel is determined; and the channel measurement equation and the channel state equation are subjected to iterative filtering estimation, and data estimation corresponding to each data without interchannel interference is obtained. The interchannel interference problem in a fast time varying condition can be effectively solved.

The invention discloses a wavelet denoising method based on an adaptive threshold. The wavelet denoising method comprises the following steps: (1) performing wavelet decomposition on a noise-contaminated signal to obtain wavelet coefficients of each layer and a highest-layer scale coefficient; (2) obtaining wavelet coefficient adaptive thresholds of all layers through iterative filtering; (3) extracting a pulse corresponding coefficient from each layer of wavelet coefficients by utilizing a chi-square energy window method; (4) setting a non-pulse corresponding coefficient in each layer of wavelet coefficient to zero to obtain a processed wavelet coefficient; and (5) performing reconstructing by using the processed wavelet coefficient of each layer and the scale coefficient of the highest layer to obtain a denoised signal.

The invention provides a power cable insulation state on-line monitoring method, which comprises the following steps that: test signal transmitting equipment and test signal receiving equipment are respectively arranged at two ends of a cable to be tested, the transmitting equipment transmits continuous high-frequency test signals, the receiving equipment acquires the high-frequency test signals, and a time domain self-correlation iterative filtering algorithm is adopted to perform signal processing and analysis on the received signal to obtain information such as the position and amplitude of an autocorrelation peak value, so that whether the insulation state of the cable to be tested is degraded or not, the degradation position and the degradation degree are judged, and detection of the insulation state of the cable is realized.