1760 results about "Filter (signal processing)" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention discloses a power supply real-time compensation technology and application based on digital signal processing, and relates to the technical field of power electronics, and the power supply real-time compensation technology comprises a split-phase parallel processing architecture: a three-phase independently configured DSP core, each core integration comprises a real-time harmonic detection module, a dynamic sliding window FFT-RT algorithm is adopted, and the window length is adjustable in a set range; the bandwidth of the self-adaptive variable parameter filter can be dynamically adjusted in a set range; the FPGA coprocessor is specially used for PWM generation; an input stage of the harmonic prediction and decomposition module is synchronously acquired by a multi-source sensor; the processing layer comprises an improved VMD decomposition unit and a variational mode decomposition order; the edge prediction network is used for deploying a lightweight LSTM model, embedding a DSP core and predicting a harmonic spectrum in a future short period; and the multi-target dynamic optimization layer is used for setting a dynamic weight distributor and performing real-time adjustment according to the load sudden change rate. According to the invention, through split-phase parallel architecture, harmonic prediction and dynamic optimization, the response speed, multi-target cooperation and reliability improvement in the field of power supply real-time compensation are realized.

The invention relates to the technical field of oil level monitoring, in particular to an oil level state judgment method and system for an oil conservator transformer, and the method comprises the following steps: collecting an oil level signal through a sensor, carrying out the real-time filtering of the collected oil level signal, adjusting a filtering coefficient through the environmental noise level, and recording and outputting the adjusted oil level signal. According to the method, the filter coefficient is adjusted to adapt to environmental noise changes, high adaptability of oil level signal processing is achieved, the system can recognize specific frequency components related to normal operation and potential fault states of equipment through frequency domain analysis, the monitoring meticulous degree and the early warning capacity are greatly improved, and by dynamically monitoring the frequency component changes, the system reliability is improved. According to the transformer oil level monitoring method, abnormity can be detected at the initial stage, equipment faults are effectively prevented, the real-time monitoring capacity is enhanced, the response speed to complex changes is increased, and comprehensive safety guarantee is provided for transformer oil level monitoring.

The invention discloses a calibration system in an active channel of a waveguide array phased-array antenna, and relates to the technical field of calibration in the active channel. Comprising a calibration judgment module, a waveguide directional coupling module, an amplitude-phase detection module and a digital domain optimization module, the calibration judgment module realizes intelligent triggering calibration through real-time monitoring of a multi-dimensional environment and performance parameters, and ensures that a calibration process is started only when the system state is deteriorated; the waveguide directional coupling module ensures that the calibration signal is accurately injected into each channel, and realizes cross polarization suppression through a polarization grid filter; the amplitude-phase detection module realizes high-precision measurement of amplitude, phase, group delay and polarization parameters of feedback signals of each channel by using superheterodyne and IQ demodulation technologies; and the digital domain optimization module calculates and updates the complex compensation weight of each channel through real-time digital signal processing and a self-adaptive algorithm, and realizes comprehensive and accurate calibration in combination with temperature-frequency interpolation and nonlinear pre-distortion compensation.

The invention relates to the technical field of anti-interference, in particular to a communication signal interference elimination method and system, and the method comprises the following steps: obtaining communication signal time domain data, calculating a signal energy change rate, a signal abrupt change rate and a phase drift rate, marking and distinguishing a signal region according to a calculation result, and obtaining a communication signal non-stationary interval identification result. According to the method, the time domain data of the communication signal is subjected to fine-grained calculation, and the signal energy change rate, the signal abrupt change rate and the phase drift rate are extracted, so that accurate signal region distinguishing is realized, the regression modeling parameter range is adjusted, the influence of instantaneous data of an abrupt change region is reduced, and the stability of signal reconstruction is improved; according to the method, signal-to-noise ratio calculation and interference power fluctuation amplitude evaluation are combined, channel state parameters are dynamically adjusted, interference intensity evaluation is more accurate, the filtering step length, the weight coefficient and the gain coefficient are optimized based on error information, signal processing adapts to different types of interference, and integrity and stability of communication signals are enhanced.

The invention provides a satellite navigation interference detection and suppression system and method, and belongs to the technical field of signal analysis. The sensor module is used for determining a target area where a sensor is installed according to a target detection sky, and deploying and installing the sensor in the target area to obtain original signal data of a satellite; the signal processing module is used for standardizing the original signal data to obtain standard signal data and converting the standard signal data into a first frequency spectrum; the feature recognition module is used for analyzing features of the first frequency spectrum through fast Fourier transform and screening signal points with abnormal fluctuation; the interference analysis module is used for classifying the features of all the signal points and matching corresponding interference categories to obtain all interference categories of the original signal data; and the interference suppression module is used for adjusting parameters in real time to reduce interference by using an adaptive filter according to the dynamic characteristics of the interference category. The reliability of navigation signals is improved, and the anti-interference capability is enhanced.

The invention belongs to the technical field of signal acquisition and processing, and discloses a low-noise biopotential signal acquisition system and a low-noise biopotential signal processing method. An original bioelectricity signal of a human body is collected through an electrode array, and analog-to-digital conversion is carried out after the signal is processed by a multi-stage self-adaptive filtering and amplifying circuit. A digital signal is sequentially subjected to adaptive wavelet transform denoising and empirical mode decomposition to obtain a multi-level intrinsic mode function set, an intrinsic mode function of a characteristic frequency band is extracted from the multi-level intrinsic mode function set, and the signal is reconstructed. Introducing a dual noise reduction mechanism combining adaptive wavelet transform and empirical mode decomposition; a signal quality real-time evaluation system is established, and system parameters including the gain of a variable gain amplifier and the bandwidth of a dynamic band-pass filter are dynamically adjusted through closed-loop feedback. The self-adaptive optimization of the whole biopotential signal acquisition process is realized, various interferences are effectively inhibited, the signal characteristics are reserved, and the signal-to-noise ratio is remarkably improved.

The invention relates to the technical field of piston ring group wear monitoring, in particular to a piston ring group wear life prediction method and system. The method comprises the following steps: collecting working state data of a piston ring group in real time, de-noising by using improved wavelet transform, processing missing data by using a KNN interpolation method, standardizing and normalizing the processed data, adopting a filtering label generation and auditing mechanism to ensure the ordered homogeneity of the data, and constructing an over-limit learning machine model to obtain a piston ring group model; a hidden layer weight and an offset vector are randomly generated to calculate a hidden layer output matrix, a whale optimization algorithm is adopted to optimize model parameters, the prediction precision is improved, then the residual life is predicted, a final prediction result is used for calculating a fault risk coefficient and comparing the fault risk coefficient with a preset threshold value, maintenance suggestions are provided, and real-time data and the prediction result are displayed. And a user is assisted to make a maintenance decision. The advanced signal processing technology and the machine learning algorithm are combined, and the maintenance efficiency of the piston ring set and the reliability of equipment operation are improved.

The invention provides a sea wave magnetic interference suppression method and device for ocean electromagnetic detection, and relates to the technical field of electromagnetic signal processing, and the method comprises the steps: synchronously collecting original electromagnetic signals through a plurality of annularly arranged magnetic sensors, generating interference parameters through employing the calculated interference signal amplitude and phase information, and carrying out the subsequent interference suppression processing. The method comprises the following steps of: calculating a covariance matrix, performing principal component analysis, extracting spatial characteristics of low-frequency magnetic interference, projecting a signal to an orthogonal subspace outside an interference subspace, removing a low-frequency magnetic interference component, retaining a target signal, performing Fourier transform to convert the signal to a frequency domain, analyzing a frequency spectrum by using a band elimination filter, and setting an interference threshold value. According to the identified interference frequency, an interference suppression signal is generated, the interference suppression signal is returned to the time domain through inverse Fourier transform and is superposed with the target signal to generate a standard electromagnetic signal, the magnetic interference in the ocean detection area can be effectively suppressed, and accurate acquisition and analysis of the standard electromagnetic signal are ensured.

A system and method are disclosed for low-light image enhancement using hierarchical adaptive wavelet decomposition with cross-scale feature fusion. The system analyzes a raw input image to determine image characteristics and preprocessing parameters. A hierarchical adaptive wavelet decomposition process creates a variable-depth decomposition tree comprising frequency domain nodes, with decomposition depth determined by local image complexity. Cross-scale feature fusion implements attention mechanisms between nodes at different decomposition levels, enabling bidirectional information flow across scales. A dynamic network pool allocates specialized neural networks to process nodes based on their frequency characteristics, with weight sharing between similar nodes for efficiency. An adaptive reconstruction engine traverses the decomposition tree using learned filters and multi-scale residual learning to produce an enhanced image. The hierarchical approach enables superior low-light image enhancement by allocating computational resources based on content complexity, achieving better quality than fixed decomposition methods while maintaining compatibility with existing image signal processing pipelines.

The invention relates to the technical field of voice processing, in particular to a mixed voice processing method, electronic equipment and a computer readable medium. The method comprises the following steps: collecting mixed voice and environment influence parameters; carrying out bionic frequency domain analysis on the mixed voice to obtain low-frequency attenuation compensation characteristic data; performing multipath effect propagation analysis on the low-frequency attenuation compensation characteristic data through the environmental influence parameters to generate channel distortion data; performing time domain-frequency domain joint deconvolution processing on the mixed voice by using the channel distortion data to generate a direct sound component and a reflected sound component; performing adversarial training based on the direct sound component and the reflected sound component to generate anti-multipath speech enhancement data; and constructing a dynamic frequency compensation filter based on preset environmental acoustic characteristics. Through the multi-stage signal processing, frequency compensation and real-time optimization technology, the output quality of the mixed voice is improved.

An adaptive variable step-size LMS filter based on a hyperbolic tangent function and its filtering method and computer equipment belong to the field of filter technology, and solve the problem that the LMS algorithm improved based on a nonlinear function requires too many manual adjustments of parameters and that the convergence speed and convergence accuracy of the traditional LMS algorithm cannot be satisfied simultaneously. The method of the present invention includes: determining the filter tap length and the number of iterations; initializing the filter parameters; obtaining the filter input signal vector; determining the instantaneous expected signal of the filter; obtaining the instantaneous output signal; obtaining the instantaneous error signal of the filter and the accumulated amount of the error signal based on the instantaneous output signal; obtaining the instantaneous step size using a step-size update formula based on a hyperbolic tangent function; and continuously iterating the filter tap weight coefficient vector to construct an LMS filter using the filter tap weight coefficient vector obtained after the number of iterations. The present invention is suitable for improving the performance of the LMS filter, and the filter of the present invention can be widely used in various signal processing fields.

The invention relates to the technical field of signal processing, in particular to an unmanned aerial vehicle self-noise filtering method and system based on multichannel time-frequency spatial filtering. The method comprises the following steps: acquiring a multi-channel noise-containing sound signal by using a microphone array carried by an unmanned aerial vehicle, and performing short-time Fourier transform to obtain a frequency domain signal; estimating a ratio mask by using a deep neural network, and performing logarithm normalization processing on the frequency domain signal; remodeling the sound signal to match the FFT length of the time-frequency spatial filter, and constructing the time-frequency spatial filter in combination with the multi-channel ratio mask; removing the self-noise of the unmanned aerial vehicle by using the filter and obtaining a target sound signal; and finally, converting the frequency domain signal into a time domain signal through inverse short-time Fourier transform. Through innovative fusion of multi-channel acquisition, deep learning and time-frequency space filtering, the self-noise of the unmanned aerial vehicle in a complex environment is accurately filtered, and the accuracy and efficiency of transformer substation noise monitoring are remarkably improved. Reliable acoustic data are provided for state monitoring of the power equipment, and the method has important engineering application value.

The invention relates to a digital lock-in amplifier based on a frequency adaptive technology and a weak signal acquisition system and method, and the system comprises a front-end signal processing module which is used for obtaining a weak signal to be detected; the frequency self-adaptive tracking module is used for performing fast Fourier transform on the frequency of the weak signal to be measured to obtain the position of the maximum spectral line so as to synthesize a frequency control word, further generate a local reference signal and simultaneously adjust the coefficients of a low-pass filter and a loop filter and the series adjustment mode of a carrier synchronization ring; the multi-rate narrow-band low-pass filter module is used for multiplying the digital signal by a local reference signal and then carrying out filtering and down-sampling on a high-frequency signal to obtain a plurality of mutually orthogonal difference frequency components; the vector operation module is used for carrying out vector operation on the plurality of mutually orthogonal difference frequency components to obtain the phase and the amplitude in the weak signal to be measured; and the man-machine interaction module is used for displaying and storing the phase and the amplitude in the weak signal to be detected.

The invention discloses an offshore wind turbine blade online monitoring method based on voiceprint recognition, which belongs to the field of wind turbine blade acoustic signal analysis, and comprises the following steps: acquiring wind turbine voiceprint data, weather condition data and wind turbine generating capacity data, and constructing a signal processing model; the collected voiceprint signals are subjected to denoising, amplification and feature extraction through a signal amplifier, a filter and a feature extraction unit, a deep learning model is constructed, voiceprint signal parameters, weather data and wind turbine generating capacity are imported into the deep learning model to evaluate future wind turbine blade cracks, a crack evaluation model is constructed, and the wind turbine blade cracks are evaluated. And importing the crack sound source position, the crack depth, the crack length and the estimated damage score into a crack evaluation model to evaluate the crack degree of the fan blade under the future weather condition, grading the fan cracks according to the crack damage score, and giving an alarm. The early warning of the future crack condition of the fan blade is realized by combining the voiceprint signal of the offshore fan blade, the weather condition and the generating capacity.

The invention belongs to the technical field of permanent magnet synchronous motor position-sensorless control, and discloses a surface-mounted permanent magnet synchronous motor full-speed-domain position-sensorless control method and system. In a zero-low-speed area, a high-frequency square wave voltage injection method is adopted for motor rotor position identification, a simplified signal processing mode is adopted, use of a filter is omitted, and position estimation precision is improved; the improved sliding mode observer is adopted to identify the position of the motor rotor in a medium-high speed domain, and aiming at the problems of buffeting and phase delay of a traditional sliding mode observer, a piecewise square change switching function is adopted to replace a sign function to reduce system buffeting, and a synchronous reference system filter is adopted to replace a low-pass filter to reduce system buffeting. The problems of phase delay and amplitude attenuation caused by back electromotive force are avoided; and an improved weighted switching method with continuous and smooth proportionality coefficients is adopted in a switching speed domain to realize stable switching of the rotating speed. The method has the advantages of being capable of accurately estimating the motor rotating speed and the rotor position in the full-speed domain, fast in dynamic response and good in estimation precision.

A multi-dimensional hydropower station control panel cabinet working condition abnormity monitoring method based on sound recognition is characterized in that a sound pickup, a mutual inductor, an infrared image monitor, a switch node sensor and the like are installed at different positions of a control panel cabinet to acquire multi-dimensional data including voiceprint, voltage, current, temperature, images and switch displacement signals; the voiceprint signals are subjected to pre-emphasis, framing, FFT transformation, Mel frequency filter bank application, MFCC feature extraction and the like, and feature vectors are extracted by using UBM, GMM and MAP algorithms. A PLDA algorithm is adopted for data analysis and fault judgment, multi-dimensional data comprehensive scoring is fused, the fault type is determined, alarm information and processing measures are output, and the system has the advantages of comprehensive multi-dimensional data acquisition, advanced signal processing technology, scientific and accurate fault judgment, high practicability and the like.

The invention relates to a method for reducing the effects of noise suppression on the measurement of the phases of satellite navigation signals at the point of their reception, wherein, in the method, a one-dimensional or multi-dimensional antenna array is provided with multiple reception antennas, satellite navigation signals received by the reception antennas are processed in a signal processing unit, the received satellite navigation signals undergo noise suppression in the signal processing unit by means of a spatial filter, i.e., by means of spatial filtering, for example by a power inversion (PI) filter or a projection filter, and after the noise suppression by means of the signal processing unit, the carrier phase of at least one or each satellite navigation signal is determined and is corrected by calculating parameters of one or more eigenbeamformers.

The invention provides a wave radar anti-interference adaptive adjustment method, and belongs to the technical field of interference adjustment, and the method comprises the steps: 1, transmitting a radar signal based on a target wave radar, and collecting an interfered original echo signal from a radar receiver; 2, interference signals in the original echo signals are analyzed and extracted, and the types of the interference signals are identified; step 3, based on the interference signal type matching adaptive filtering coefficient, initializing an adaptive filter to obtain a first adaptive filter; 4, performing time dispersion processing on the frequency of the interference signal by using a frequency hopping waveform technology, inputting the interference signal into the first adaptive filter to obtain a signal adjustment parameter, and configuring to obtain a final adaptive filter; and step 5, adjusting the target wave radar based on the final adaptive filter, and evaluating an adjustment result. Radar interference is effectively suppressed through adaptive filtering and frequency hopping processing, and the signal processing precision is improved.

The invention discloses an automatic control method and system for a bias point of a photoelectric modulator, and relates to the field of signal processing, and the method comprises the steps: generating a composite disturbance voltage signal through a digital signal processor; the bias electrode is injected into the photoelectric modulator and used for collecting the optical signal output by the photoelectric modulator and converting the optical signal into an electric signal; extracting a fundamental component amplitude and phase difference data of a second harmonic component, a third harmonic component and a fundamental wave; dynamic characteristic parameters are calculated through spectral analysis, and the mapping relation between the characteristic parameters and the offset of the offset points is established; outputting the offset direction and the offset level of the offset point through a deep learning model; a bias voltage compensation amount is generated through a fuzzy PID (Proportion Integration Differentiation) controller; and adjusting the bias electrode voltage, smoothing the filter, and eliminating high-frequency jitter. The method has the advantages that high-precision and stable bias point adjustment is realized through the composite disturbance signal, real-time spectrum analysis and deep learning optimization decision in combination with fuzzy PID control and a dynamic closed-loop mechanism, and the system performance and stability are remarkably improved.

The invention relates to the technical field of particle accelerators, in particular to a transverse feedback system with a switchable excitation direction and a control method thereof, and the system comprises a beam position detector BPM which is used for measuring a beam signal; the RF front end is used for receiving the beam signal sent by the beam position detector BPM and carrying out amplification and filtering processing; the ADC module is used for converting the beam signal into a digital signal; the digital signal processing module is used for carrying out digital signal processing calculation to obtain an excitation signal; the DAC module is used for carrying out digital-to-analog conversion; the power divider is used for adjusting the phase of the excitation signal; the exciter comprises four Kickers with electrodes, a power amplifier, a low-pass filter and a matched load and is used for applying a transverse excitation signal to the beam current, and the problems that in the prior art, a transverse feedback system Kicker occupies a large longitudinal space, the system is complex, and precision and efficiency are low are solved.

The invention discloses a high-throughput satellite communication terminal system, which comprises a radio frequency channel unit, a signal processing unit, a terminal processing unit, a power supply management unit and an anti-electromagnetic interference module, and is characterized in that the radio frequency channel unit carries an agile device and a power amplifier and is used for realizing bidirectional conversion between a baseband signal and a radio frequency signal; communication frequencies of a Ka frequency band, a valley value of 5GHz and a peak value of 40GHz are supported, a zero intermediate frequency receiver structure is adopted, image frequency interference is eliminated through one-time frequency conversion to a baseband, and a high-integration design that a radio frequency signal directly enters a frequency mixer after passing through an antenna, a radio frequency filter and a low-noise amplifier is realized. According to the high-throughput satellite communication terminal system, the coverage problem of traditional communication in a blind area of a power grid is solved, safe, efficient and stable transmission of power data is realized, the emergency communication capability and the equipment environment adaptability are remarkably improved, and key technical support is provided for constructing global visual monitoring and intelligent scheduling of the digital power grid.

The invention belongs to the technical field of underwater acoustic engineering and digital signal processing, and particularly relates to a fractional delay FIR (Finite Impulse Response) phase calibration method and system of a multichannel sonar system. Comprising the steps of signal acquisition and reference channel selection; high-precision relative delay estimation; calculating a cross-correlation function of other channel signals and the reference channel signal, performing coarse estimation by searching a peak point of the amplitude of the cross-correlation function, and performing fine estimation on the peak position by using a parabola interpolation method to obtain the delay time of each channel relative to the reference channel; designing an FIR fractional delay filter; digital filtering and phase calibration; verifying a calibration effect; and calculating the phase of each channel signal at the target frequency point after calibration, and counting the standard deviation of each channel signal so as to evaluate the improvement degree of phase consistency. The method aims at overcoming the defect that traditional integral multiple sampling delay correction precision is insufficient, high-precision phase compensation of a sub-sampling point level is achieved, and therefore the performance of algorithms such as beam forming of a multi-channel sonar system is remarkably improved.

The invention discloses a rotor unbalance rapid adjustment method based on Kalman filtering, and belongs to the technical field of rotor dynamic balance. According to the method, a rotor rotating speed signal is obtained through a magnetic encoder, and a piezoelectric vibration sensor is triggered to collect a radial vibration signal. In the aspect of signal processing, a zero phase shift filter is adopted to remove noise interference, and an initial phase and an amplitude are extracted by using FFT (Fast Fourier Transform). And calculating a rotating speed change rate based on the sliding time window, and judging whether the rotor is in a steady-state or transient-state working condition. Under the transient working condition, a state space model containing amplitude, phase and rotating speed is constructed, and unbalance adjustment parameters are predicted through Kalman filtering recursion; and under a steady-state working condition, accurately calculating a correction parameter by adopting a least square method. In addition, a least square fitting model based on a sine function is constructed, amplitude estimation is optimized, and the unbalance calculation precision is improved. According to the invention, the unbalance of the rotor can be quickly and accurately adjusted, and the stability and the service life of a rotating system are improved.

The invention discloses a reflective slot antenna for RFID tag inventory, and relates to the technical field of antenna design, the slot antenna design scheme of the invention greatly enhances the directionality and signal coverage capability of the antenna through a phase control algorithm and slot arrangement optimization, and improves the signal coverage capability of the antenna through adjusting the phase difference and the slot spacing. The radiation intensity of the antenna in multiple directions is enhanced, blind areas are effectively reduced, multi-path interference is effectively identified and suppressed through a multi-path signal processing algorithm and an LMS minimum mean square error algorithm, and the antenna can automatically adjust the phase of reflection current and filter reflection signals, so that the signal quality of a complex reflection environment is guaranteed, and the signal quality is improved. In addition, an adaptive array antenna system and a beam forming technology are introduced, so that the antenna can dynamically adjust the phase and amplitude of an array unit according to real-time data fed back by the RFID tag, thereby forming a directional beam, concentrating energy to a specific direction, and improving the recognition precision in a specific area.

The invention discloses an electroencephalogram signal artifact removal and noise reduction method and system, and relates to the technical field of electroencephalogram signal processing, and the method comprises the steps: receiving an electroencephalogram signal, carrying out the adaptive multi-scale decomposition of the received electroencephalogram signal, obtaining decomposition layers and a mode function, carrying out the artifact marking at each decomposition layer, and recognizing and positioning an artifact source. According to an artifact source positioning result, artifacts are removed through a threshold filtering algorithm, time-frequency characteristics of the electroencephalogram signals are extracted, noise reduction processing is conducted on the extracted time-frequency characteristics through an intelligent noise reduction filter, the quality of the processed electroencephalogram signals is monitored in real time, and processing parameters are fed back and adjusted according to a monitoring result. And outputting the electroencephalogram signals subjected to artifact removal and noise reduction processing, performing quality evaluation on the output signals, and optimizing the processing flow according to the evaluation result. The method solves the problems that in the prior art, the accuracy of signal distortion and artifact recognition and removal is limited, and the flexibility and adaptability of the processing flow are insufficient.

The invention provides a multiphase filtering sampling method suitable for satellite communication. The method comprises the following steps: receiving an original modulation signal and converting the original modulation signal into a digital signal sequence; extracting a signal feature vector and generating a processing configuration parameter; performing orthogonal decomposition to generate I / Q signal pairs and performing oversampling; initializing a multi-phase filter and generating an optimal filter coefficient; carrying out phase calibration and filtering; determining an interpolation coefficient and executing multi-stage integer interpolation and mixed precision fractional interpolation; configuring a dynamic cache system; phase reordering and synthesis are executed; spectral analysis is performed and optimization parameters are generated. According to the invention, high-precision signal processing is realized, the adaptive capability and the efficient resource utilization characteristic are realized, and the performance of a satellite communication system is remarkably improved.

The invention relates to the technical field of biomedical signal processing, and discloses a multi-wavelength PPG signal motion artifact elimination algorithm and system. The method comprises the following steps: acquiring a multi-wavelength PPG signal by using acquisition equipment; inputting the acquired noise-containing multi-wavelength PPG signal into a filter; the filter processes an input signal and then generates a denoised PPG signal; extracting a period of the denoised signal in real time by using an autocorrelation function; inputting the denoised PPG signal and a period extracted in real time into a BSS algorithm to find an optimal noise reference signal; taking the denoised PPG signal and the optimal noise signal as a first input and a second input of adaptive filtering to eliminate motion artifacts; the motion artifacts are eliminated by using the algorithm, and finally the PPG signals with the motion artifacts eliminated are output; the method overcomes the problems of dependence on additional hardware, low correlation of noise reference signals, complex algorithm and the like in the prior art.

The invention discloses a periodogram and incoming wave direction finding fused multi-unmanned aerial vehicle signal identification method and system, and relates to a signal processing technology, and the method comprises the steps: obtaining to-be-identified signals with time domain aliasing and frequency domain non-aliasing; suppressing the noise of the to-be-identified signal by using an average periodogram method and smooth filtering, and calculating a bottom noise value; according to the calculated bottom noise value, setting a judgment threshold to detect a target unmanned aerial vehicle signal; estimating the start-stop frequency and bandwidth of the detected target unmanned aerial vehicle signal; performing signal separation by using an FIR filter according to the estimated starting and ending frequency and bandwidth; and executing incoming wave lateral direction based on the separated target unmanned aerial vehicle signal so as to complete signal identification. According to the method, unmanned aerial vehicle signal detection is completed through an average periodogram method, unmanned aerial vehicle weak signals which are sparsely distributed in space are further obtained through a direction finding method, and weak signal unmanned aerial vehicles which are far in distance are detected.

The invention relates to an ultralow-delay visual positioning system and method based on an event camera. The system comprises an event sensing module, an inertial measurement unit (IMU) and a mixed signal processing unit. The event sensing module has a microsecond-level response characteristic and is integrated with a dynamic bias adjustment and tunable optical filter; the IMU is rigidly connected with the IMU, and the physical center alignment error is less than 0.5 degree; and the hybrid processing unit adopts an FPGA (Field Programmable Gate Array) and ARM (Advanced RISC Machines) collaborative architecture to realize event filtering, time synchronization, motion compensation and tight coupling optimization. According to the method, an event brightness residual error and an IMU pre-integration residual error are combined to construct a joint cost function, a sliding window is dynamically adjusted based on event entropy, and loop-back and closed-loop correction is realized through mutual information criteria. The method can realize six-degree-of-freedom pose estimation as high as 500Hz, has the advantages of low delay, high precision, high robustness and the like, and is suitable for high-speed scenes such as unmanned aerial vehicles, AR navigation, industrial robots and the like.