4055 results about "Target signal" patented technology

The invention relates to the technical field of radar control, in particular to an anti-interference control method applied to a radar, and aims to solve the problem that an existing radar anti-interference technology is insufficient in anti-interference capability in a complex electromagnetic environment. According to the method, multi-dimensional input data of a radar system are synchronously collected, interference feature extraction is performed on multi-dimensional data streams by using a dual-channel deep network, and interference fingerprint vectors are output to a dynamic feature library in real time. And based on the interference feature priority list, performing cooperative processing, including spatial filtering, frequency domain notch, polarization projection and residual extraction and sparse reconstruction of a target signal. And dynamically adjusting the processing parameters according to the real-time evaluation result. And data distribution processing is carried out on the suppressing interference and the deception interference, and final target trajectory information is output through result fusion. Parallel processing is also realized on the FPGA chip, and the processing speed is improved. On the whole, the anti-interference capability, the stability and the real-time performance of the radar system in a complex electromagnetic environment are improved.

The invention provides a directional energy multi-target interference system based on an ultra-wideband phased array technology, and the system integrates signal perception, target recognition, interference signal generation and phased array control, so as to achieve the precise interference of an unmanned aerial vehicle cluster. The signal sensing module scans an airspace target through the ultra-wideband phased-array antenna array and obtains spectrum characteristics of a target signal. The target identification module analyzes signal features, identifies an individual target and predicts a motion track and a communication protocol of the individual target. And the phased array control module calculates and adjusts antenna beam forming parameters, and dynamically controls the radiation direction, phase and transmitting power of the antenna. The interference signal generation module generates a matched interference waveform based on the target communication protocol and the trajectory information. The ultra-wideband phased-array antenna array adopts a digital beam forming technology. According to the invention, efficient and accurate directional energy interference can be realized under multiple frequency bands, the confrontation capability of the unmanned aerial vehicle cluster is improved, and the method is suitable for defense tasks in electronic warfare and complex electromagnetic environments.

The invention relates to the technical field of probe testing, and discloses a probe testing device and method, and the method comprises the steps: carrying out the synchronous collection of the electric signal response characteristics of a plurality of chip testing probes in a chip contact process, analyzing the signal transmission interference relation between the probes, and obtaining a multi-probe signal coupling feature pattern; identifying a target signal transmission channel to obtain a first test probe configuration scheme; performing test signal input and response acquisition on the chip to obtain electrical characteristic time-frequency data of the chip; and performing multi-probe collaborative analysis and feature extraction on the time-frequency data of the electrical characteristics of the chip to obtain abnormal indication information of the electrical performance of the chip, and adjusting test parameters and contact positions of a plurality of chip test probes to obtain a configuration scheme of a second test probe. Therefore, the test system can intelligently adjust the subsequent test strategy according to the preliminary test result, and the intelligence and automation of the test process are realized.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

The invention provides a GNSS deception jamming detection method and system based on LSTM, and relates to the technical field of global satellite navigation systems. Acquiring a target signal through a receiver in the GNSS tracking loop model; acquiring output information of the tracking loop; selecting four types of basic SQM detection quantities of Ratio, Delta, ELP and PCS as a detection quantity set; generating a characteristic parameter set according to the output information and the detection quantity set; preprocessing the feature parameter set by using a sliding window technology to generate standardized data; and inputting the standardized data into a preset LSTM model to obtain a detection result corresponding to the target signal. The technical effect of effectively identifying the navigation deception is realized.

The invention discloses a distributed polarization detection system and method based on position regulation and control target reconstruction, and belongs to the field of distributed polarization cooperative detection and photoelectronic imaging. The distributed polarization detection system comprises a lighting device, a multi-parameter auxiliary detection objective table and a long-wave infrared polarization detection device; the long-wave infrared polarization detection device is provided with three sub-polarization detection devices, the method is implemented based on a distributed polarization detection system, and polarization information in three different directions can be collected at the same time through the distributed polarization detection system based on position regulation and control target reconstruction. The identification capability of the disguised / similar target at a single angle is improved; and errors caused by time delay are avoided. Polarization information obtained by reasonably distributing detection positions is mutually supplemented, polarization images are mutually fused, target signals are effectively extracted from scattered light to analyze the polarization characteristic difference between interference light and target reflected light, and the resolution, contrast and detection distance of polarization imaging can be improved.

The invention discloses an unmanned aerial vehicle antenna dynamic adjustment method and device under single Beidou support, and relates to the technical field of antenna control, and the method comprises the steps: carrying out the positioning of a target unmanned aerial vehicle, and obtaining a spatial positioning data signal; transmitting and receiving signals to obtain a signal transmitting and receiving record log; analyzing the flight area of the target unmanned aerial vehicle to obtain terrain environment parameters, performing flight analysis, and generating flight attitude change information; performing operation calculation on an antenna module of the target unmanned aerial vehicle, generating an antenna dynamic operation coefficient, and formulating an antenna adjustment strategy; and performing optimization, generating an antenna adjustment optimization strategy, and performing dynamic adjustment. The technical problem of poor communication and positioning accuracy and stability caused by insufficient antenna adjustment precision of the unmanned aerial vehicle in a complex environment in the prior art is solved, and the technical effects of realizing accurate tracking of the antenna of the unmanned aerial vehicle on the target signal source in the complex environment and improving the accuracy and stability of communication and positioning of the unmanned aerial vehicle are achieved.

The invention discloses an underwater acoustic signal denoising method based on a time-frequency adaptive dual-path Conformer network, and the method comprises the steps: carrying out the preprocessing of real marine environment noise and underwater acoustic target signals, and generating a multi-signal-to-noise-ratio noisy data set; performing short-time Fourier transform on the noisy data, and extracting real part and imaginary part features to form a feature tensor; extracting features by using a feature encoder, and generating intermediate feature representation; respectively extracting a time path feature and a frequency path feature through a time-frequency adaptive dual-path Conformer network; performing multi-scale convolution processing and weighted fusion on the extracted time-frequency features by adopting a multi-scale fusion dynamic gating network; performing nonlinear mapping on the fused features by using a feature decoder to generate a mask matrix; and restoring the complex frequency spectrum based on the mask matrix, and restoring the denoised underwater acoustic time domain signal. According to the method, time-frequency information is fully mined in combination with underwater sound noise characteristics, and the underwater sound signal denoising effect is improved.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

The invention is suitable for the technical field of geophysical exploration, and provides a ground-air transient electromagnetic airborne receiving coil motion noise extraction method, which comprises the following steps: designing a receiving device carrying an IMU (Inertial Measurement Unit) sensor, the receiving device comprising a receiving coil, the IMU sensor and a synchronous clock; measuring a vector geomagnetic field in a transient electromagnetic detection area based on attitude angle data of the receiving device; and extracting the motion noise of the airborne transient electromagnetic coil based on the vector geomagnetic field and the attitude angle data. The invention designs a ground-air transient electromagnetic receiving device, and provides a method for extracting motion noise of an airborne receiving coil based on the ground-air transient electromagnetic receiving device, so that the problem that the motion noise is generated due to the fact that the coil cuts a geomagnetic field is effectively solved. Compared with a mode of directly separating the motion noise from the induced electromotive force signal, the method provided by the invention can directly extract the motion noise from the generation mechanism level of the motion noise, and effectively avoids the problem of incomplete noise extraction or target signal loss.

The invention provides a communication and sensing integration method and system, electronic equipment and a storage medium. The method comprises the following steps: under the condition that a target object needs to be sensed, detecting the target object based on a passive detection technology to obtain a detection distance of the target object; and determining a target signal transmission mode adapted to the detection distance for sensing. And according to the target signal transmission mode, performing communication processing based on the target antenna in the communication time slot, and performing sensing processing based on the target antenna in the sensing time slot. Based on the scheme of the application, communication and sensing functions can be realized only by deploying a set of target antennas, so that spectrum and hardware resources are saved. In addition, in the sensing process, the signal transmission mode is adapted according to the detection distance of the target object determined by the passive detection technology, and the sensing precision for the target object can be improved. Meanwhile, the passive detection technology can assist in locking the target object in the sensing process, and the blind area possibly caused by switching the signal transmission mode of the target antenna is made up.

A system can use a plurality of co-located telescopes to generate enhanced telescopic imagery of space objects. The system may be configured to receive telescopic imagery data of a plurality of space objects obtained from the plurality of co-located telescopes. The system can receive at least two imaging criteria. The available imaging criteria can include a target signal sensitivity comprising a minimum signal to noise ratio (SNR), a target number of spectral bands comprising a minimum number of spectral bands, a spectral range associated with one or more of the spectral bands, a location of the plurality of co-located telescopes, a target data cadence comprising a minimum number of frames per minute, a target number of space objects to be tracked, a target minimum spatial resolution, among others. The system can transmit instructions to the plurality of telescopes and generate enhanced telescopic imagery.

The invention relates to the technical field of narrow-band filters, and discloses an ultra-low power consumption narrow-band filter implementation method based on a digital phased array system, and the method comprises the following steps: S1, collecting input signal data, carrying out the adaptive preprocessing, and generating preprocessed signal data; s2, performing digital beam forming processing based on the preprocessed signal data to generate digital beam forming data; s3, performing narrowband filtering parameter matching processing according to the digital beam forming data to generate narrowband filtering parameter data; in the narrowband filtering processing process, disturbance of environmental electromagnetic interference to a target signal is identified and eliminated in real time through adaptive noise suppression and a multi-dimensional signal preprocessing mechanism, so that the system can guarantee the pointing precision of digital beam forming, the pointing offset problem in the space signal capturing process is avoided, the accuracy of narrowband signal extraction is improved, and the accuracy of narrowband signal extraction is improved. And the reliability of filtering output and the signal fidelity are enhanced.

The invention relates to a sleep apnea monitoring method and device, electronic monitoring equipment and a storage medium. The method comprises the following steps: acquiring PPG signal data of a user in a sleep period, performing segmentation processing on the PPG signal data, performing quality scoring on each PPG signal fragment data obtained by the segmentation processing, screening out target PPG signal fragment data of which the quality score is lower than a quality score threshold value, and sending the target PPG signal fragment data to the user; the quality score threshold value dynamically changes based on statistical characteristics of quality scores in different time periods, blood oxygen detection is conducted on the target PPG signal segment data to obtain blood oxygen index data, effective respiratory wave detection is conducted based on the target PPG signal segment data to obtain respiratory index data, and the effective respiratory wave detection is conducted based on the blood oxygen index data and the respiratory index data to obtain the target PPG signal segment data. Whether a sleep apnea event or a hypopnea event is present during sleep of the user is identified. By adopting the method, more accurate sleep apnea monitoring can be realized.

The invention discloses a single-bit direction of arrival estimation method and system with signal amplitude recovery, a terminal and a storage medium, and the method comprises the steps: obtaining each snapshot time signal from a signal source of a uniform linear array receiver, introducing a non-zero threshold value to carry out the single-bit quantization of each snapshot time signal, and obtaining a signal model; reconstructing the signal model, replacing a sign function of the reconstructed signal model with a smooth hyperbolic tangent function, and performing reconstruction to obtain a target signal model; based on the target signal model, constructing a single-bit DOA estimation optimization problem by using a truncated Frobenius norm and an L2, 0 norm, and converting the optimization problem into a target optimization problem by using a semi-quadratic technology; and solving a target optimization problem by using a near-end alternating minimization algorithm and a forward-backward splitting algorithm to obtain a signal source estimation direction, a signal source estimation number and a signal estimation amplitude. According to the method, the amplitude of the signal source is accurately estimated while the prior dependence on the number of the signal source is eliminated.

The invention relates to the technical field of music feedback, in particular to a music feedback training interface interaction method and system based on electroencephalogram data, and the method comprises the following steps: collecting electroencephalogram data of a user based on an electroencephalogram collection device, and removing environmental noise and non-target signals through signal amplification and filtering processing to obtain a user electroencephalogram data set. According to the method, the emotion recognition accuracy and time sensitivity are improved by analyzing the brain wave frequency band intensity, judging the emotion state of the user and recording the emotion change time point, and the emotion recognition accuracy and time sensitivity are improved by matching music tracks of the emotion state, combining real-time monitoring of electroencephalogram reaction and adjusting the rhythm, volume and spatial sound effect setting of music. Dynamic adjustment of music output parameters is achieved, the emotion intervention requirement is optimized, emotion improvement and user satisfaction are evaluated according to brain wave changes of the user, and the improvement effect of music intervention on the psychological state of the user can be measured.

The invention belongs to the technical field of radar signal processing and target identification, and particularly relates to a maritime hybrid target identification method and system based on modal decomposition and reconstruction, and the method comprises the steps: carrying out the variational modal decomposition of a radar echo signal of a maritime hybrid target, and decomposing an original signal into a plurality of intrinsic modal signals; estimating a background noise energy reference and removing noise modals based on the decomposed modal signals, clustering the remaining modals according to the center frequency, and combining the modals with the frequency within the center frequency range into an independent single-target signal which is of the same target and is reconstructed into an independent single-target signal; respectively carrying out time-frequency analysis on each reconstructed single target signal to obtain a corresponding time-frequency diagram, and extracting time-frequency domain features from the time-frequency diagram; and inputting the extracted features into a trained support vector machine classifier to realize automatic identification of the ship target and the floating target. A ship target and a floating target can be distinguished more accurately in a mixture scene, and the stability of tracking identification is improved.

The invention discloses a pipeline intrusion event identification method based on a distributed optical fiber sound wave sensing system, and the method comprises the steps: collecting intrusion sound wave signals of different positions and types of pipelines in actual engineering, carrying out the data preprocessing, and constructing a pipeline intrusion event data set; a multi-scale prime number convolution network model based on dynamic convolution and global attention is constructed, features of different time scales and frequency components are extracted through a multi-scale prime number convolution module, and key intrusion features are highlighted and noise interference is suppressed in combination with dynamic convolution capable of adaptively correcting a convolution kernel and an enhanced global attention module. Outputting an event recognition result; according to the method, the problems that the processing capacity of long sequence signals is insufficient and engineering background noise covers target signal features in pipeline intrusion identification are solved, and the monitoring success rate and accuracy of pipeline intrusion events are improved.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention discloses a bimodal neural network and spectral subtraction combined underwater acoustic signal noise suppression method. Comprising the following steps: firstly, constructing a simulation underwater sound data set containing self-noise; thirdly, constructing a bimodal neural network, then training the bimodal neural network until training is completed, and obtaining a platform self-noise estimation model; inputting a to-be-processed noise-containing underwater acoustic signal into the platform self-noise estimation model, and outputting estimation platform self-noise by the model; and finally, based on the self noise of the estimation platform, noise reduction processing is carried out on the noise-containing underwater acoustic signal to be processed by using the improved spectral subtraction method, and then a noise-reduced underwater acoustic signal is obtained. The method combines an advanced deep learning method and a classical spectral subtraction algorithm, has the capabilities of autonomous learning and efficient noise reduction, remarkably improves the signal-to-noise ratio of the underwater acoustic signal, is suitable for noise reduction of the underwater acoustic signal in a non-stationary and complex marine environment, remarkably improves the low-frequency noise suppression capability and the target signal fidelity, and has a good application prospect. The method is suitable for underwater acoustic target detection, underwater acoustic communication and other scenes.

The invention provides a high-voltage circuit breaker voiceprint denoising method based on data enhancement and a storage medium, and the method comprises the steps: processing a collected original voiceprint data sequence, extracting stable and effective Mel-frequency cepstrum coefficient features, and constructing a two-dimensional feature matrix; a parallel mixed data enhancement strategy is adopted to generate a positive sample pair, and an encoder is trained in combination with a contrast learning mechanism, so that the representation robustness of the model under different voiceprint change conditions is improved. The method comprises the following steps: decomposing an original signal containing noise fringes into a plurality of modal components by using variational modal decomposition, extracting low-frequency effective components, introducing Gaussian white noise, constructing a corrosion target signal as a decoder training target, learning through a denoising automatic encoder, and finally outputting a denoised voiceprint feature signal. The method has the advantages of high robustness, high noise suppression capability, excellent feature expression capability and the like, is suitable for the field of online monitoring and intelligent diagnosis of the state of high-voltage circuit breaker equipment, and has good application prospect and engineering value.

The invention discloses a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention discloses a multi-channel radar foresight imaging method based on sparse and weighted low rank, and solves the problem that the foresight super-resolution imaging effect is limited in a complex imaging scene in the prior art. The method comprises the following steps: establishing an echo signal model in a multi-channel radar single-transmitting single-receiving mode, and obtaining an echo signal corresponding to each target; carrying out pulse compression and correction processing, representing a two-dimensional echo matrix, and carrying out weighting processing on a target signal matrix in the two-dimensional echo matrix to obtain a weighted target signal matrix with a low-rank characteristic; obtaining a foresight imaging model according to the sparsity of the target in the echo signal model and the low-rank characteristic of the weighted target signal matrix; solving the foresight imaging model based on ALM under an ADMM framework to obtain a reconstructed foresight high-resolution image; according to the method, the high-quality and high-resolution reconstructed foresight high-resolution image is obtained through sparse and weighted low-rank dual constraints.

The invention provides a fault diagnosis intelligent analysis method and system for industrial equipment, and a storage medium. The method comprises the following steps: segmenting monitoring time sequence data into at least one sub-sequence based on statistical characteristics and working conditions of the monitoring time sequence data; determining the length of an embedded window based on the local characteristics of the subsequences, constructing a trajectory matrix by using the length of the embedded window, and performing singular value decomposition on the trajectory matrix to obtain a primitive matrix and a corresponding singular value; forming candidate groups for the primitive matrixes of different preset combinations, calculating a separability metric value of each candidate group, and determining a component group based on the separability metric values and the singular values; performing diagonal averaging reconstruction on the selected component group to obtain signal components, and calculating preset characteristic parameters of each signal component; and according to the preset feature parameters, target signal components related to the preset fault mode are screened out, feature vectors representing the fault state are extracted from the target signal components, the feature vectors are input into a pre-configured fault diagnosis model, and fault diagnosis information is output.

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 relates to the technical field of material physical property detection, and discloses an injectable hydrogel wound dressing water absorbability detection method, which comprises: constructing a main measurement and reference dual channel, and synchronously monitoring a mixed conductivity signal and an interference signal generated only by gel self ion leakage; according to the method, the interference is deducted through real-time differential operation, a pure target signal is obtained, and then the water absorption volume of the hydrogel is calculated through inversion based on the law of conservation of mass, a measured object is transferred to an external solution from a gel body, and the interference of a sample is stripped through an endogenous dynamic correction mechanism, so that the accuracy of the water absorption volume of the hydrogel is improved. The determinacy of a measurement result is derived from a physical law instead of model hypothesis, so that double restrictions caused by irregular sample forms and complexity of a chemical environment are avoided, and reliable quantification of the water absorption performance of the in-situ dynamic forming hydrogel is realized.

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

The invention provides a real-time human body detection method and system based on a microwave radar, and is applied to the field of signal data processing. By constructing a sparse reconstruction model and combining multi-dimensional feature decomposition, the method effectively relieves the recognition difficulty caused by target signal aliasing, continuously outputs and receives radar signals, extracts distance, Doppler and angle features, constructs target distribution point clouds in a three-dimensional feature space, further utilizes angle continuity judgment and point cloud density analysis, and improves the recognition accuracy of target distribution point clouds. According to the method, the number and distribution of overlapped targets are accurately estimated, a Kalman filter is introduced for dynamic tracking for a scene in which a continuous area is not formed, continuous separation and pseudo-color image reconstruction of multiple targets are realized, and thus the resolution precision and target positioning capability of a radar in a multi-person close-range aggregation scene are remarkably improved.

The invention relates to the technical field of low earth orbit satellites, and discloses a method and equipment for improving the signal-to-noise ratio of a low earth orbit satellite signal, and the method comprises the steps: carrying out the multi-dimensional signal collection and processing of a received signal of a low earth orbit satellite downlink, and obtaining a signal tensor model; executing structured decomposition based on the signal tensor model to obtain a channel factor matrix group; performing multi-stage sequential processing according to the channel factor matrix group to obtain low-orbit satellite channel state information; inputting the low-orbit satellite channel state information into an interference identification module for feature extraction and interference type analysis to obtain an interference type and probability distribution thereof; performing multi-dimensional adaptive filtering on the received signal to obtain a target signal; and performing modulation coding and beam forming parameter joint optimization on the target signal and the low-earth-orbit satellite channel state information to obtain a low-earth-orbit satellite communication link, thereby solving the problem of large fluctuation of the signal-to-noise ratio in a satellite rapid moving scene, and realizing balance of power efficiency and anti-interference performance at the same time.