3132 results about "Target signal" patented technology

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.

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 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 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 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.

The invention discloses a directional broadcast multi-band interference suppression method based on dynamic beam forming, and relates to the technical field of computer processing, and the method comprises the steps: S01, synchronously capturing electromagnetic signals of at least two working bands through a heterogeneous band receiving array synchronized by a hardware-level clock; s02, analyzing space parameters of a target signal and an interference source in the electromagnetic signal in real time; s03, constructing a layered adaptive codebook based on the interference dynamic parameters; s04, fusing the target signal parameter and the interference dynamic parameter by adopting a block matrix acceleration algorithm and an orthogonal decomposition technology, and calculating a beam forming weight; s05, executing operation through the multi-band coprocessor; and S06, establishing an interference source azimuth drift prediction model, and setting a trigger threshold as the coverage tolerance angle theta. According to the method, the anti-interference real-time performance is improved by utilizing layered adaptive wave beams, frequency band isolation, super-resolution multi-path identification and block decomposition.

The invention provides an anti-interference sound signal data extraction method based on energy data perception, and belongs to the technical field of underwater sound signal processing, and the method comprises the steps: carrying out the A / D sampling and digital orthogonal demodulation of a received sound signal, and obtaining a baseband signal; performing low-pass filtering and downsampling on the baseband signal to obtain a preprocessed signal; performing matched filtering on the pre-processed signal by using a reference signal; the key improvement lies in that a local energy estimation function of a preprocessed signal is calculated, and normalization processing is carried out on a matched filtering result and the energy estimation function to obtain a normalized detection signal. The method effectively suppresses the shielding effect of a strong interference signal on a weak target signal under a non-orthogonal reference signal and a strong near-end and weak far-end scene, remarkably reduces the misrecognition probability, and improves the accuracy and reliability of the extraction of a long-distance and low-signal-to-noise-ratio target beacon signal in a complex underwater acoustic environment. The method is suitable for underwater navigation, positioning and communication systems.

The invention relates to the cross technical field of signal processing and artificial intelligence, in particular to a multi-source noise removal method and system based on DAE, and the method comprises the steps: 1, carrying out the data collection and feature extraction of multi-source noise and pure signals; step 2, constructing a de-noising recognition knowledge base based on feature analysis; step 3, constructing a deep denoising auto-encoder model based on a knowledge base; step 4, hierarchical training and optimization guided by a mixed loss function of the deep denoising model; step 5, denoising processing of a target signal and output evaluation based on a discrimination model; according to the invention, noise data in multiple fields such as electromagnetism, remote sensing and biological signals are integrated, a dynamic mixing strategy and a data enhancement technology are adopted, a training set which highly simulates a real environment is constructed, and a unique cross-scene adaptation module can perform adaptive adjustment according to signal characteristics of different application scenes; the problem that a traditional method is poor in scene adaptability is solved.

The invention provides an ultrasonic fingerprint chip and an integral clock generation method and device of the ultrasonic fingerprint chip. Appropriate fingerprint pixel units can be selected from a plurality of fingerprint pixel units to serve as first-class pixel units and second-class pixel units, and the first-class pixel units and the second-class pixel units are respectively connected with a first-class detection circuit and a second-class detection circuit. Wherein a first integrator in the first type of detection circuit is sequentially connected with a first comparator, a time delay regulator, a clock dominant frequency duplicator and a frequency dividing circuit; the frequency dividing circuit is connected with the second type detection circuit; a second integrator in the second type detection circuit is connected with an amplification structure; the amplification structure is also connected with a second comparator; and the second comparator is connected with the time delay regulator in the first-class detection circuit. The circuit can automatically generate and obtain the integral clock signal which is matched with the ultrasonic fingerprint echo signal and is high in precision and good in effect, so that the received ultrasonic fingerprint echo signal is precisely processed, and a target signal with high quality is obtained.

In order to solve the problem that quick response and distortionless recording of ultrahigh-frequency partial discharge signals are difficult to realize when accidental pulses are generated in the prior art, the invention relates to a dual-channel parallel processing architecture for ultrahigh-frequency partial discharge signals, which comprises a signal cloning circuit and a signal processing circuit, the ultrahigh-frequency partial discharge module is used for receiving ultrahigh-frequency partial discharge signals and synchronously outputting the ultrahigh-frequency partial discharge signals as first target signals and second target signals after lossless copying; the low-gain channel circuit is used for performing buffer transmission for maintaining an original bandwidth and a dynamic range on the first target signal to generate a first gain signal; the high-gain channel circuit is used for performing gain amplification on the second target signal to generate a second gain signal; the digital signal processing unit is used for performing synchronous clock control on the low-gain channel circuit and the high-gain channel circuit; and the FPGA chip is integrated in the digital signal processing unit and is used for performing fusion splicing processing on the first gain signal and the second gain signal based on a waveform fusion splicing algorithm and generating a fusion signal.

The invention provides a method for rapidly detecting the content of nitrogen oxides in air. The method comprises the following steps: acquiring original signal data of nitrogen oxides in air in a target detection area in real time through a plurality of nitrogen oxide sensors; performing noise reduction filtering processing on the original signal data to generate target signal data; performing signal drift compensation correction on the target signal data based on a preset sensor calibration model to generate standardized concentration data; performing space-time correlation analysis on the standardized concentration data through a nitrogen oxide concentration prediction algorithm model in combination with environment parameter data collected in real time to generate a target prediction concentration value; and comparing the target predicted concentration value with a preset concentration standard threshold value based on a fuzzy logic judgment algorithm, and constructing a detection report. The whole process from data acquisition to report generation is intelligent, and the detection flexibility is remarkably improved.

The invention belongs to the technical field of automatic control, and particularly relates to a digital control system for strip coiling equipment, which comprises the following steps of: firstly, comprehensively acquiring strip tension data and state characteristic information by combining a tension sensing assembly and image acquisition equipment, dynamically adjusting a braking torque parameter, and then, acquiring a dynamic control signal by taking the linear speed of a coiling machine as a target signal; the speed of the uncoiling machine and the speed of the traction roller are synchronously aligned through a following strategy, then a taper tension control mode is adopted, the tension value is dynamically adjusted in combination with real-time coiling diameter parameters, the tension stability and the coiling regularity in the coiling process are ensured, and finally the strip deviation problem is detected and processed in real time according to a closed-loop deviation correction mechanism. The equipment control strategy in the strip coiling process is deeply bound with uncoiling control, synchronous coordination, coiling control and a deviation correction core module, and the integrated control capability of multivariable coordination and self-adaptive optimization is achieved, so that the automation and intelligence level of the strip coiling process is effectively improved.

The invention discloses a memory signal integrity enhancing system based on multi-dimensional parameter self-adaption, and belongs to the technical field of signal transmission. The system comprises a multi-dimensional signal acquisition module, a multi-dimensional parameter analysis module, a self-adaptive adjustment module, a memory signal optimization module and a feedback monitoring module, the multi-dimensional signal acquisition module is combined with multi-dimensional parameter analysis, signals of a data bus, an address bus and a control bus are synchronously acquired, a dynamic adjustment strategy is generated according to a model, and the self-adaptive adjustment module and the memory signal optimization module are optimized according to the dynamic adjustment strategy. Different optimization means are adopted for different parameter types, a customized optimization system with one parameter and one scheme is formed, it is ensured that the optimized target signal is optimal in amplitude stability, time sequence accuracy, anti-noise capacity and impedance matching degree, the feedback monitoring module is combined with the multi-dimensional parameter analysis module, and the multi-dimensional parameter analysis module is combined with the feedback monitoring module. And through full-dimension index monitoring and dynamic calibration, the continuous adaptability of the system to the running state of the memory and the long-term stability of signal integrity are guaranteed.

The invention provides a hyperspectral anomaly detection method based on double-stage attention guidance and a state space model, which relates to the technical field of hyperspectral image processing and comprises the steps of scene background modeling based on an auto-encoding network, generation of a reconstructed background image and a reconstructed residual image. Carrying out target signal enhancement on the original hyperspectral data based on the reconstructed residual image to obtain attention enhancement data, carrying out target feature depth extraction by adopting a state space model based on the attention enhancement data, generating an abnormal semantic feature image, and fusing a background guide feature image and the abnormal semantic feature image to obtain a hyperspectral image; and an abnormal probability graph is generated through adaptive gating fusion, and collaborative optimization is carried out based on a multi-task loss function. According to the method, the internal contradiction of a single network architecture is fundamentally solved, the prior guiding capability of the reconstruction method and the strong feature representation capability of the state space model are fully combined, and the accuracy and reliability of hyperspectral anomaly detection are remarkably improved.

Disclosed in embodiments of the present application are a signal strength measurement method and apparatus, a system, a storage medium and a product. The method comprises: receiving unmanned aerial vehicle pose data of an unmanned aerial vehicle in an area to be measured, and terminal position data collected by a ground test terminal interacting with the unmanned aerial vehicle; on the basis of the unmanned aerial vehicle pose data and the terminal position data, establishing a plurality of position relationship features between the unmanned aerial vehicle and the ground test terminal; and inputting the plurality of position relationship features into a target signal prediction model to obtain the signal strength of said area.

Apparatus and methods for identifying a wireless signal-emitting device are disclosed. The apparatus is configured to sense and measure wireless communication signals from signal-emitting devices in a spectrum. The apparatus is operable to automatically detect a signal of interest from the wireless signal-emitting device and create a signal profile of the signal of interest; compare the signal profile with stored device signal profiles for identification of the wireless signal-emitting device; and calculate signal degradation data for the signal of interest based on information associated with the signal of interest in a static database including noise figure parameters of a wireless signal-emitting device outputting the signal of interest. The signal profile of the signal of interest, profile comparison result, and signal degradation data are stored in the apparatus.

The invention discloses a high-precision spectral interference signal demodulation method for micro distance measurement. The method comprises the steps of S1, interference spectrum shaping; measuring an interference spectrum and a reference spectrum; selecting a six-item combined cosine window as a primary function of an interference signal to obtain a shaped interference spectrum; s2, performing non-uniform Fourier transform to obtain a frequency spectrum of the target signal; s3, on the basis of a multi-spectral-line interpolation algorithm, interpolation is carried out on the window function to solve a fitting function, and a center frequency k is obtained; and S4, outputting a result. And solving and outputting an optical path difference d. According to the method, a six-item cosine combination window is selected as a primary function of an interference signal, and the problem that the accuracy of signal extraction is influenced by mutual superposition of side lobes and even main lobes of a direct current item and a solved frequency item is solved; an interference spectrum is processed by using a fast non-uniform Fourier transform algorithm, accuracy can still be kept under the conditions that the distance to be measured is increased and sampling is gradually sparse, and the problem of resampling deviation caused by a traditional interpolation method can be solved.

A method for validating operation of a driver integrated circuit includes providing a signal using an output node. The signal is provided using multiple set points in response to a change in state of an input signal. Each set point corresponds to a different phase of a multi-phase transition of the signal. The method includes providing a timer value at an end of a phase of the multi-phase transition and determining whether the signal is in a target signal range of the phase based on the timer value at the end of the phase, a predetermined value defining the target signal range of the phase, and a predetermined time limit for the phase. A current through the output node may be provided using the multiple set points, and a voltage on the output node may have the multi-phase transition.

The invention discloses an infrared satellite data processing system suitable for infrared small target detection, and relates to the technical field of remote sensing satellite data processing and infrared image recognition, and the system comprises an image splicing module which is used for arranging L0-level original infrared images according to a shooting time sequence, and identifying overlapped region feature points of adjacent image particles through an SIFT algorithm, eliminating radiation distortion of an optical lens, and generating a seamlessly spliced comprehensive image by adopting a multi-scale fusion technology. According to the infrared satellite data processing system suitable for infrared small target detection, the quality and consistency of infrared satellite images are remarkably improved through a standardized data processing flow, the identifiability of target signals is optimized from a data source, the false alarm rate under the conditions of a complex background and a low signal-to-noise ratio is effectively reduced, and the detection accuracy is improved. And high-quality input is provided for a subsequent detection algorithm, so that the accuracy and reliability of infrared small target detection are improved.

The invention belongs to the technical field of detection, relates to a free radical detection method, particularly provides a water free radical cation detection method, and aims to realize water free radical cation detection which is efficient, rapid, free of sample pretreatment and convenient to analyze. According to the invention, an LTQ-XL linear ion trap mass spectrometer is used as a detection tool, and a dielectric barrier corona discharge method is adopted to provide a water free radical cation detection method. The method comprises the following steps of: filling argon (Ar) between two discharge electrodes as a working gas, applying a certain voltage to two electrodes of a dielectric barrier discharge ionization source (DBDI), detecting water free radical cations to obtain a corresponding mass spectrum to represent the water free radical cations, and optimizing a target signal to obtain corresponding mass spectrum data. The method can be used for efficiently and quickly detecting the water free radical cations without sample pretreatment and the like, is convenient to analyze and apply, and has remarkable innovativeness and wide application prospects.