10 results about "Matching pursuit" patented technology

The application provides an active noise reduction method and system for road noise and wind noise of an automobile, and the active noise reduction method comprises the following steps: S1, collecting a dynamic parameter signal and a total mixed noise signal during automobile driving; S2, constructing a dynamic calculation model of a Strouhal number according to a Reynolds number, deducing a real-time calculation model of a characteristic frequency of each noise source i according to the dynamic calculation model of the Strouhal number, and outputting a real-time characteristic frequency sequence corresponding to each noise source i; S3, constructing a global signal dictionary D, using an orthogonal matching pursuit sparse representation algorithm, and separating a single noise source signal in the total mixed noise signal by iteratively screening a dictionary atom with the highest matching degree with the characteristic frequency; S4, using a frequency tracking type fast adaptive filtering algorithm to generate a corresponding anti-phase cancellation signal; and S5, outputting the anti-phase cancellation signal in the automobile cabin, so that noise reduction can be performed on each noise source i.

This invention discloses a short-time compressed sampling method and system based on a modulation wideband converter (MWT) to solve the problem of real-time acquisition of short-time burst wideband sparse signals. The method reduces the compression and reconstruction unit of the MWT to multiple short-time segments of the uncovered signal's complete time domain. For each segment, a short-time reconstruction algorithm is used for independent reconstruction. This algorithm integrates principal component analysis for dimensionality reduction, a modified atom matching strategy for selecting conjugate atom pairs, recursive prospect orthogonal matching pursuit for determining the support set, and time-domain support extraction to suppress noise. Finally, the complete signal is recovered through segment reconstruction. This invention significantly reduces reconstruction complexity, improving the signal-to-noise ratio of reconstructed 5GHz wideband signals at a 2.5GHz sampling rate by more than 4dB compared to traditional methods, and exhibits stronger robustness to changes in sparsity and channel number, achieving low-power, high-precision sub-Nyquist sampling.

This invention discloses a sonar signal bandwidth extension method and system based on an adaptive sparse reconstruction approach. It acquires frequency domain data of active sonar echoes through matched filtering and extracts the transmission bandwidth range of the active sonar. A physical perception dictionary containing the physical ripple characteristics of the transmitted waveform is constructed to establish a precise mapping from the sparse target space to the observation frequency band. An adaptive threshold detection mechanism is introduced during the orthogonal matched pursuit iteration process to screen potential targets using the statistical characteristics of local background noise. A meshless global optimization algorithm is used to jointly fine-tune the target position in the continuous time domain. A full-band skeleton spectrum is constructed using precise parameters, and the noise residuals in the original frequency band are back-injected and inversely transformed to obtain a high-resolution time-domain waveform. This invention significantly improves the ability to distinguish closely spaced targets and the detection performance of weak targets in complex underwater acoustic environments.

The application belongs to the technical field of wireless positioning and specifically relates to a reconfigurable intelligent surface assisted SISO system user near field positioning method. Regular multivariate decomposition and orthogonal matching pursuit are combined to obtain initial values of TOA and AOD on the RIS, then, a l1-norm regularization method based on sparsity is used to estimate the distance. Then, the maximum likelihood estimation formula is used to refine the initial estimation. Finally, the extended invariance principle is combined with the least square method to recover the position and clock offset. The obtained parameter estimation performance is close to the Cramer-Rao lower bound of the estimation error.

This application provides a method for diagnosing partial discharge (PD) of substation acoustic signals based on an improved matched pursuit network, relating to the field of substation acoustic signal PD diagnosis technology. The method includes segmenting and preprocessing the initial time-domain signal to obtain preprocessed time-domain segmented signals; performing time-frequency conversion on both noise-free and noisy analog signals to obtain noisy and noise-free time-frequency matrices; constructing multiple training sample pairs; inputting the noisy time-frequency matrix into an improved matched pursuit network to obtain an estimated feature matrix; calculating the loss value between the estimated feature matrix and the noise-free time-frequency matrix; optimizing the network parameters of the improved matched pursuit network to obtain an optimized improved matched pursuit network; inputting the real signal time-frequency matrix into the optimized improved matched pursuit network to obtain and identify a sparse time-frequency feature matrix, thus obtaining the PD type identification result of the current preprocessed time-domain segmented signal; improving the reliability and generalization ability of the diagnosis.

The application relates to a kind of MIMO millimeter wave radar two-dimensional super-resolution angle measurement methods using space-time virtual conversion, comprising: using the orthogonality of transmitting signal, MIMO sparse array is carried out MIMO virtual array transformation, obtain space domain virtual array, obtain the received signal data of space domain virtual array, using the independence of received time domain multiple fast shot data, then carry out time domain virtual transformation, obtain space-time virtual array;Under the antenna layout of space domain virtual array, if the received signal data is single fast shot data, then two-dimensional super-resolution angle measurement is realized using orthogonal matching pursuit (OMP) sparse reconstruction method, if the received signal data is a small amount of fast shot data, then two-dimensional super-resolution angle measurement is realized using sparse bayesian learning (SBL) sparse reconstruction method;If the received signal data is multiple fast shot data, under the antenna layout of space-time virtual array, super-resolution angle measurement is realized using OMP method.The method of the application can increase the effective aperture of array, and significantly improve the angle measurement accuracy of pitch and azimuth dimension.

The application discloses a CoSaMP pipeline defect positioning method based on NRBO adaptive sparsity, and belongs to the technical field of signal processing and nondestructive testing. The method comprises the following steps: performing pretreatment on the original echo signal collected, performing peak value detection on the echo signal, and adaptively completing window segmentation; a dictionary for sparse representation is constructed; a Newton-Raphson optimization algorithm NRBO is used to adaptively select a sparsity parameter k; based on the sparsity parameter k, a compressed sampling matching pursuit algorithm CoSaMP is used to complete sparse decomposition and reconstruction of the echo signal, and defect positioning is realized according to the reconstructed signal. The application realizes adaptive determination of the sparsity parameter through the NRBO, can reduce the subjectivity of parameter selection and improve the calculation efficiency under the premise of effectively preserving the key information of the echo signal, is suitable for defect echo signal processing and pipeline defect positioning scenes, and has good application prospect and popularization value.

The matching tracking method for adjacent time satellite cloud clusters disclosed in the application comprises the following steps: acquiring a first observed cloud image and a first brightness temperature base image at a current time, and a second observed cloud image and a second brightness temperature base image at a next time; performing cloud cluster region identification on the cloud images according to a map at the current time, and performing cloud cluster region identification on the cloud images according to the base image at the next time, to obtain a cloud cluster region set at the current time and a cloud cluster region set at the next time; extracting a first feature parameter set corresponding to the cloud cluster region set at the current time from the first observed cloud image, and extracting a second feature parameter set corresponding to the cloud cluster region set at the next time from the second observed cloud image; performing cloud cluster region matching processing on the cloud cluster region set at the current time and the cloud cluster region set at the next time according to the first feature parameter set and the second feature parameter set, to obtain a target matching cloud cluster pair set; and generating a cloud cluster tracking result according to the target matching cloud cluster pair set. The method can more accurately perform cloud cluster tracking.

This invention relates to the field of power monitoring, specifically to a method and system for separating multi-dimensional harmonic components in power metering. The method includes the following steps: synchronously acquiring three-phase voltage and current observation signals at a fixed frequency, and outputting standardized synchronous observation signals; fitting and stripping strong fundamental components using a recursive least squares method to obtain residual signals containing harmonics across the entire frequency band; performing a multi-scale variable window S-transform on the residual signals to achieve decoupling and sparse characterization of cross-modulation components, and constructing three-dimensional time-frequency and channel tensors; estimating the number of harmonic sources and identifying the mixing matrix based on tensor CP decomposition, and completing sparse reconstruction using orthogonal matching pursuit to restore the full-frequency time-domain signals of each harmonic source; verifying the reconstruction accuracy of the separation results, extracting harmonic features of each harmonic source, matching them with a typical power electronic disturbance source feature library to complete calibration, and outputting the final separation results. This invention is fully adaptable to actual metering conditions in the field and has excellent engineering application value.

The application discloses a priori enhancement block sparse channel estimation method for a millimeter wave massive MIMO system, and comprises the following steps: system initialization, construction of an angle domain dictionary matrix, uniform division of an observation angle range, calculation of a uniform linear array normalized steering vector corresponding to each grid angle and arrangement in columns; collection of a noisy observation matrix of a snapshot, calculation of a sample covariance matrix, extraction of a noise subspace through eigenvalue decomposition, calculation of a MUSIC pseudo-spectrum and normalization, and aggregation of an atomic level pseudo-spectrum into a block level prior weight vector; execution of a priori enhancement block orthogonal matching pursuit iteration, and cyclic execution of the following sub-steps until a stop condition is met. The method fully utilizes the characteristics that a millimeter wave channel has obvious sparsity in an angle domain, is suitable for a communication environment with high user density and large signal-to-noise ratio fluctuation, and realizes a channel estimation method with high detection performance and low calculation complexity under a complex communication environment.