7 results about "Pre whitening" patented technology

This invention belongs to the field of fault diagnosis technology, specifically relating to an online monitoring method for bearing abnormal noise based on acoustic signature frequency feature analysis. The method includes the following steps: acquiring a continuous time-domain acoustic signature signal of the bearing during operation; performing detrending and pre-whitening processing on the continuous time-domain signal to obtain a preprocessed discrete signal sequence; performing a Fourier transform on the discrete signal sequence to obtain the amplitude spectrum; calculating the spectral fluctuation complexity index based on the amplitude spectrum; determining the optimal decomposition level for variational mode decomposition based on the spectral fluctuation complexity index; and using the optimal decomposition level to decompose the discrete signal sequence to obtain several intrinsic mode components. This invention effectively distinguishes between mechanical fault impacts and random environmental interference through adaptive parameter optimization and a dual feature interlocking mechanism, significantly improving the accuracy and robustness of online monitoring.

This invention discloses a dynamic state estimation method for unscented Kalman filtering based on robust MM estimation, comprising the following steps: S1. Establishing a power system state estimation model and discretizing the model, initializing the state vector, covariance matrix, process noise covariance matrix, and measurement noise covariance matrix, and transmitting measurement data in real time through a phasor measurement unit; S2. Linearizing the system measurement function using a statistical linearization method and establishing a batch regression equation based on the data calculated by unscented Kalman filtering; S3. Performing robust pre-whitening processing on the batch regression equation established in step S2; S4. Dispersing the data points in the batch regression equation obtained in step S3 using a linear transformation method; S5. Using the MM estimation method on the batch regression equation to estimate the state vector and update the covariance matrix. This addresses the problem that existing technologies cannot guarantee estimation performance and can be used to estimate the state of nonlinear systems, while also having low collapse points and low estimation efficiency.

The invention provides a far-field system response G matrix measurement method based on adaptive blind source separation. The method comprises the following steps: sequentially and uniformly placing noise sources in a view field at intervals, and respectively measuring to obtain a far-field system response G matrix; after the noise source is placed in the field of view every time, the noise source is measured by the following steps: receiving a microwave radiation signal emitted by the field of view as an observation signal x of a microwave remote sensing image; performing pre-whitening processing on the observation signal x to obtain a processed observation signal, extracting a noise source signal matrix Y from the observation signal, and realizing separation of a noise source and an environment from a signal level; and calculating the actual visibility of the noise source through the obtained signal matrix Y.

The invention discloses an anti-tank missile identification method based on improved orthogonal basis decomposition and autoregression model noise processing. The method comprises the following steps: S1, acquiring an original geomagnetic signal sequence containing target magnetic anomaly and background geomagnetic noise at a preset sampling rate by using a magnetic detection sensor; s2, performing autoregression AR modeling on background geomagnetic noise in the original geomagnetic signal sequence, constructing a whitening filter according to an AR model, and performing pre-whitening processing on the original geomagnetic signal sequence to obtain a whitened signal sequence; s3, correcting the standard orthogonal basis function group according to the transmission characteristics of the whitening filter to obtain a corrected standard orthogonal basis function group matched with the whitened target signal; and S4, inputting the whitened signal sequence into a corrected standard orthogonal basis decomposition unit, calculating a basis function coefficient corresponding to a corrected standard orthogonal basis function group, and calculating an energy function according to the basis function coefficient.

The invention relates to the technical field of satellite communication, and particularly discloses a satellite communication anti-interference method and system based on a kernel method and FastICA, and the method comprises the steps: building a post-nonlinear hybrid model, so as to simulate the nonlinear signal distortion caused by an amplitude limiter in satellite communication, the post-nonlinear mixing model comprises a linear mixing stage and a nonlinear compression stage; a kernel method is adopted to map observation signals to a high-dimensional regeneration kernel Hilbert space, nonlinear features are represented through a quartic polynomial kernel function, and a nonlinear mixing problem is converted into a linear separable form; in a high-dimensional kernel space, based on a FastICA algorithm, a communication signal and an interference signal are separated through a non-Gaussian criterion of maximizing negentropy; in combination with regularization pre-whitening processing and a symmetric fixed point iterative optimization strategy, a separation matrix is updated; and outputting the separated communication signal to realize nonlinear interference suppression.

This invention belongs to the field of wireless anti-interference technology and discloses a suppression-type interference suppression method that integrates spatial feature separation. It effectively suppresses the background noise floor through diagonal loading pre-whitening processing during the snapshot construction stage. Then, through first-order eigenvalue decomposition in the initial subspace segmentation stage, it removes the main components of strong suppression interference. Second-order high-order cumulant feature decomposition is performed on the remaining coarse interference subspace after removing the coarse interference subspace, accurately mining the spatial features of weak targets obscured by strong interference, thus achieving preliminary decoupling between interference and target spatial features. This significantly improves the identifiability of weak target spatial features, accurately separating candidate target subspaces even when the target signal is deeply submerged by strong suppression interference. Furthermore, by using an adaptive density peak clustering algorithm to perform unsupervised clustering of features within the coarse interference subspace, it can automatically distinguish different types of suppression interference, breaking through the array's limitation on the number of interference sources, and also completing the purification and optimization of interference features.

The application discloses a blind source separation method based on Stiefel manifold optimization and information entropy criterion, and relates to the technical field of radar signal processing. The specific implementation of the method comprises the following steps: collecting received signals of multiple samples, performing pre-whitening processing on the received signals by using a pre-whitening matrix to obtain preprocessed signals of the multiple samples; the preprocessed signals of the multiple samples are divided into multiple small sample sets; the small sample sets are used to solve the blind source separation based on the information entropy criterion according to the numbering order of the small sample sets to determine a target demixing matrix; and the antenna directional diagram of an array radar is calculated according to the target demixing matrix. The implementation can effectively separate target echo signals and interference signals from the received signals of the radar, saves the time resources of the radar, reduces the time cost consumed by the radar, greatly improves the solving efficiency of the blind source separation, realizes real-time anti-interference and interference signal filtering of the radar, and has strong robustness and real-time performance.