8 results about "Blind signal separation" patented technology

The invention provides a signal blind separation and intelligent reconstruction method and system in a complex scene, and relates to the technical field of signal processing, and the method comprises the steps: receiving an aliasing signal, and converting the aliasing signal into a multi-channel signal observation matrix; performing decomposition in a wavelet domain to obtain a wavelet coefficient, matching the wavelet coefficient with the sparse dictionary, and reconstructing a target signal source after optimization; estimating the number of signal sources based on covariance matrix eigenvalue distribution; mapping a signal source to a graph structure domain, extracting space and time sequence correlation through a mixed graph convolutional network, and obtaining a target separation signal through variational reasoning optimization; and finally, carrying out quality evaluation and post-processing to obtain a final reconstruction signal. According to the invention, the signal separation precision and robustness in a complex scene are improved.

The invention discloses a spectrum resource intelligent management method based on blind signal separation and intelligent feature extraction, and belongs to the technical field of satellite communication. The method comprises the following steps: S1, spectrum data acquisition and preprocessing: acquiring spectrum data through high-precision equipment and performing primary processing; s2, blind signal separation: extracting a pure signal from a complex signal by adopting an advanced technology; s3, intelligent feature extraction: capturing spectrum dynamic features and extracting key features by using deep learning technologies such as LSTM and the like; s4, optimizing a spectrum resource allocation strategy, and dynamically adjusting the allocation strategy based on deep reinforcement learning; s5, performing anomaly detection and classification, introducing a deep learning model to quickly identify an abnormal mode, and enhancing the anti-interference capability; and S6, integrating and testing the system, and verifying the effectiveness of the system through simulation and actual measurement. The spectrum utilization rate and the anti-interference capability of the system are remarkably improved, an efficient solution is provided for spectrum management in a complex communication environment, and the method has important application value.

A sound receiving processing method and a sound signal processing apparatus are provided. In the method, multiple input sound signals are received through a microphone array. The input sound signals are combined into a main sound signal based on beamforming. Multiple primary extraction signals belonging to the main sound signal are respectively extracted from the input sound signals. Multiple estimated sound source signals corresponding to the sound sources are separated from the primary extraction signals through blind signal separation. Multiple re-extraction signals belonging to the estimated sound source signals are respectively extracted from the primary extraction signals. A distance of the first source relative to the microphone array is determined according to a power ratio. One of the estimated sound source signals corresponding to the first source is determined according to a distance limit. Therefore, the sound signal from the desired sound source could be obtained.

The application discloses a kind of power grid fault diagnosis method and system based on electric power data analysis, it is related to electric power system fault diagnosis related field, including: the multiple electric power data sources of constructing electric power network, extract multiple electric power data;Multiple electric power data are carried out semi-supervised separation according to fault type using blind signal separation algorithm, obtain multiple separation signals;The fault feature similarity between the pairs of multiple electric power data sources is calculated;According to the cross-source end clustering of fault feature similarity calculation result, the multiple separation signal clusters output are respectively input power grid fault diagnosis model analysis, obtain multiple fault types, send power grid fault diagnosis result after joint identification.Solve the technical problem that existing power grid fault diagnosis exists in mixed fault scene and is easy to produce misjudgment due to signal coupling, leading to insufficient diagnostic accuracy and reliability, achieve the technical effect of accurate separation and identification through complex mixed fault, improve fault diagnosis accuracy and reliability.

The present invention discloses a method and device for separating single-channel aliased electromagnetic signals based on deep learning, including: obtaining a single-channel aliased electromagnetic signal; inputting the single-channel aliased electromagnetic signal into a pre-trained signal processing model, so that the encoder in the signal processing model encodes the single-channel aliased electromagnetic signal. After obtaining the signal encoding, enabling the separation network in the signal processing model to obtain an estimated mask of the single-channel aliased electromagnetic signal based on the signal encoding, and further enabling the decoder to output the separated signal of the single-channel aliased electromagnetic signal based on the signal encoding and the estimated mask. The present invention does not need to obtain prior knowledge of the signal, is suitable for blind signal separation, and extracts common features in a large amount of data through deep learning. Even when the signal is strongly interfered by noise, it still has a good separation effect.

The present application relates to the technical field of communication signal anti-interference, in particular to a satellite navigation anti-interference method based on blind signal separation, comprising the following steps: step one, mixed signal collection: using several single antennas to simultaneously receive mixed signals of satellite navigation signals and interference signals, digitizing and sampling the mixed signals to obtain mixed signal data; step two, anti-interference model construction: establishing a satellite navigation signal anti-interference processing mathematical model according to the number of single antennas and the mixed signals; step three, demixing matrix solution: intercepting mixed signal data samples with a length of from the mixed signal data, and processing the mixed signal data samples by using a blind signal separation method. The present application receives mixed signals of satellite navigation signals and interference signals by using several single antennas, realizes the separation of satellite navigation signals and interference signals by using a blind signal separation method, and extracts navigation information by solving the separated signal data.

The invention discloses a power grid fault diagnosis method and system based on power data analysis, and relates to the related field of power system fault diagnosis, and the method comprises the steps: constructing a plurality of power data source ends of a power network, and extracting a plurality of power data; performing semi-supervised separation on the plurality of power data according to the fault type by adopting a blind signal separation algorithm to obtain a plurality of separation signals; calculating the fault feature similarity of the separated signal pairs among the plurality of power data source ends; and cross-source-end clustering is carried out according to a fault feature similarity calculation result, a plurality of output separation signal clusters are respectively input into a power grid fault diagnosis model for analysis, a plurality of fault types are obtained, and a power grid fault diagnosis result is sent after joint identification. The technical problem that in existing power grid fault diagnosis, misjudgment is likely to be generated due to signal coupling in a mixed fault scene, and consequently diagnosis accuracy and reliability are insufficient is solved, and the technical effect of improving fault diagnosis accuracy and reliability through precise separation and recognition of complex mixed faults is achieved.

An audio signal processing method and a mobile apparatus are provided. In the method, a target direction in multiple sound-reception directions and a target distance corresponding to the target direction is determined according to multiple first audio signals in the sound-reception directions received by an embedded microphone. A target algorithm is selected from multiple blind signal separation (BSS) algorithms according to the target direction and the target distance. The first audio signal received by the embedded microphone at the target direction is set as a secondary signal of the target algorithm, and the second audio signal received by an external microphone is set as a primary signal of the target algorithm. The audio signal of the primary sound source is separated from the primary signal and the secondary signal through the target algorithm. Accordingly, the microphone path merely outputs a single audio signal of the primary sound source.