155results about How to "Improve the output signal-to-noise ratio" patented technology

The invention provides a conversion and calibration algorithm for improving output signal-to-noise ratio of a successive approximation (SAR) analog-to-digital converter (ADC) and the ADC. By adopting the algorithm, the overall output signal-to-noise ratio of the ADC can be effectively improved through a calibration algorithm and the ADC on the premise of not changing equivalent input noise of a comparator in the SAR ADC. In the technical scheme, the last least significant bit (LSB) cell capacitance Cc connected at a fixed potential in the traditional binary-weighted digital-to-analog converter (DAC) capacitance array is taken as the DAC capacitance of an additional period; and a comparison operation is carried out once again after the original SAR ADC comparison period is completed, and then the original ADC quantized result output is calibrated according to the obtained comparison result, thus improving the overall output signal-to-noise ratio of the ADC in the statistical sense.

The invention relates to a method and a device, which conducts super-fast time identification and measurement to seed optical signal. The method essentially includes the following steps: (1) super short laser pulse produced by a laser pulse light source is split into two paths by an optical beam splitting system after energy regulation, collimated beam narrowing and spatial filtering system optimization and selection, among which one path passes a time delay control system and a parameter pump pulse optical path, and the other path passes a seed exciting pulse optical path; (2) the collected seed optical signal and the pump pulse incident into a parameter amplifying system, so that the parameter amplifying system conducts ultra fast amplification to the sample seed optical signal under a linear amplifying state; (3) a parameter amplifying signal detecting system is used to detect, record, refer to and revise the signal, and give corresponding detecting result to ultra fast spectra analysis and time identification and measurement of amplifying the seed optical signal. The invention can amplify the weak seed optical signal and realize detection of ultra fast time identification, and is a novel detecting method in the field of ultra fast spectroscopy.

A navigation-body weak radiation noise signal detection method under a marine environment relates to navigation-body radiation noise signal detection. Euler domain random resonance and Euler domain mixing random resonance models are established. A signal submerged by a strong background noise is processed in a stable state system of an Euler space. The signal is mapped to a period index form. A stable state model possesses a multi-resonant characteristic. Limitations of an approximation heat insulation theorem of a traditional random resonance theory and residence time are broken through, a radiation noise signal characteristic can be highlighted and a signal to noise ratio is increased. An Euler domain non-linear random resonance and mixing random resonance system is used to detect a period signal and a pulse signal radiated by an underwater navigation body. Underwater weak signal detection performance is improved and an output signal to noise ratio is effectively increased. A target signal is detected in a distance range which is greater than 500m. Under a co-existence condition of a low signal to noise ratio of minus 5dB and various kinds of other water sound interferences, existence of a target is detected. Sparse characteristic extraction and degradation treatment of a data amount are performed, and a calculating speed is increased.

The invention discloses a satellite-borne three-channel SAR-GMTI self-adaptive clutter suppression method and relates to self-adaptive clutter suppression. The method comprises the first step of obtaining distance Doppler domain target echo signals after distance compression, the second step of obtaining distance Doppler domain echo signals after distance compression, the third step of obtaining data X1 of a first distance unit to be detected of three channels, the fourth step of obtaining data of multiple Doppler units subjected to self-adaptive clutter suppression in the first distance unit to be detected, and the fifth step of making the number l of the distance unit to be detected to be increased by one, repeating the second step, the third step and the fourth step until l is equal to L, namely completing clutter suppression of L distance units, and then outputting data Y of the L distance units subjected to clutter suppression. The satellite-borne three-channel SAR-GMTI self-adaptive clutter suppression method is used for suppressing ground clutter received by a three-channel SAR-GMTI system.

The invention discloses a radio frequency amplitude keying demodulation circuit with large input dynamic range, which comprises an amplitude detection circuit, a Gaussian filter circuit, a peak detection circuit and two branches of difference amplitude keying demodulation circuits of a comparison circuit. The amplitude detection circuit is formed by a fixed gain amplifier with S poles in series connection and an amplitude detection and conversion circuit. Each branch of the amplitude keying demodulation circuits includes that input signals are amplified to i fuzzy amplitude keying signals through the amplitude detection circuit, the i fuzzy amplitude keying signals are added and converted into high-low voltage amplitude signals, high frequency components are filtered through the Gaussian filter circuit, the filtered signals pass through the peak detection circuit to obtain high-low average amplitude signals, and the average amplitude signals and the signals filtered by the Gaussian filter circuit are compared through the comparison circuit to obtain demodulation signals output by the radio frequency amplitude keying demodulation circuit. The radio frequency amplitude keying demodulation circuit with the large input dynamic range can be flexibly used for demodulating amplitude detection signals with modulation degree ranging from 0.5 to 0.9, thereby being capable of being applied to the front end of a radio frequency receiver with input signals reaching up to 60dB dynamic range.

The invention relates to an interference type optical fiber gyro based on principle of MZ interference, wherein the gyroscopic instrument comprises an illuminating source, a beam divider, an optical supervisory instrument, and an optical fiber sensor coil composed of two optical fiber arms of MZ interference, wherein the light from the light source is connected to the starting terminal and ending terminal of the two optical fiber arms on the optical fiber coils after the beam divider. The forward conduction optical path is separate from the intervened optical path, thus no interference is resulted.

The invention provides a radar-communication integrated realizing method. According to the radar-communication integrated realizing method, based on analysis on the characteristics of the ambiguity function of transmitted signals of a communication system, communication signals are directly transmitted when the number of transmitted code elements in one transmission period satisfies requirement; the communication code elements pass through a pulse type shaping filter; obtained signals are transmitted out through using a transmitter and by means of an antenna; a receiving end carries out radar detection processing on the signals directly using the communication signals; the receiving end carries out demodulation on the signals through a receiver, so that baseband signals can be obtained; the baseband signals pass through a matched filter, so that an output signal to noise ratio is maximum; if a function which is to be realized by the system is a communication function, sampling judgment is performed on the demodulated baseband signals, so that the communication function can be realized through communication code element information; and if a function which is to be realized by the system is a radar function, a certain code element accumulation is performed on the signals, so that high radar resolution can be obtained, and time delay and Doppler frequency are extracted through using related technologies, and a radar detection function can be finally realized.

The invention discloses a sea clutter weak signal detection method based on a two-dimension Duffing oscillator, comprising steps of combining a quantum genetic algorithm and a random resonance algorithm and enhancing a useful object signal of a sea clutter signal to obtain an optimal detection result obtain an optimal detection result, adopting an improved heredity algorithm to optimize the Duffing chaotic oscillator, performing combined encoding on the coefficient to inteliigently obtain the optimal system parameter with an output signal to noise ratio as an evaluation function and with an signal to noise ratio gain as a determination index, performing self-adaptive random resonance processing on the sea clutter data detected by the IPIX radar according to the obtained parameter, and effectively detecting the object signal without damaging the useful signal. The invention is strong in resisting noise and greatly improves the system output signal to noise ratio, which overcomes the problem the traditional random resonance hand-operated parameter configuration or the single parameter optimization searching detection effect is not good. The invention expands the application range of the Duffing chaotic oscillator random resonance on detecting the signal of the small object and has a good application prospect.

The invention discloses a recognition method for cognizing radio signal modulation modes under the low-signal-to-noise-ratio condition. Processing signal-to-noise ratios of received signals are improved by adoption of segmental frequency domain filtering, and according to M power sine wave generation characteristics of four modulation signals including FSK, BPSK, MSK and QPSK, the modulation modes are identified and converted to detection of the number of sine waves. Simulation experiment proves that the method has a good recognition effect, and under the condition that the signal-to-noise ratio is not smaller than minus 1dB, recognition rate of the four signals can be up to over 90%.

The invention discloses a method for denoising a sense signal of a focal plane, which comprises the following steps of: performing wavelet decomposition on the sense signal according to the composition and characteristics of noise in the sense signal of a focal plane detector, decomposing the sense signal of the detector into frequency bands with different energies by the wavelet decomposition, and processing coefficients obtained by decomposing each layer by adopting corresponding threshold values according to the characteristics of the noise in the signal to fulfill the aim of attenuating noise energy.

The invention relates to a mechanical vibration signal feature extraction method based on combination of stochastic resonance and kernel principal component analysis. The method comprises the steps that firstly, the stochastic resonance method is applied for conducting pretreatment on rotor oscillation original signals measured by a sensor, the signal periodicity is improved, and the oscillation signal to noise ratio is improved; then, a time domain feature set is extracted for the pretreated output signals; then the kernel principal component analysis method is adopted for conducting nonlinear feature transformation for the extracted time domain feature set, and therefore the final needed feature set is obtained. The method is applied to feature extraction and failure diagnosis of simulated failure of an engine rotor, the result shows that the feature set extracted through the method is of linear independence, the number of dimensions is smaller, the separability is higher, the precision and efficiency of the failure diagnosis can be effectively improved, and application in engineering practice is facilitated.

The invention discloses a UHF waveband multi-channel radar radial speed detection method based on a tensor product. The problems that in the prior art, the low-speed moving target detection probability and radial speed estimation precision are low under the condition that an antenna bearing aperture is limited are mainly solved. According to the implementation process, the method comprises the steps that a tensor product operation is carried out on data vectors received by a radar to obtain data with a virtual aperture expanded; a tensor product operation is respectively carried out on search clutter guide vectors and search target guide vectors to obtain search clutter guide vectors and target guide vectors with the virtual aperture expanded; the search clutter guide vectors and target guide vectors with the virtual aperture expanded are tensioned into search signal subspace; the radial speed of a target is estimated through signal subspace fitting. The method has the advantages of being high in low-speed object detection probability and radial speed estimation precision, and is used for estimating the low target radial speed under the condition that the antenna bearing aperture is limited.

The invention discloses a parameter wavelet threshold signal denoising method based on an improved artificial bee colony algorithm. The parameter wavelet threshold signal denoising method comprises the steps: firstly obtaining a to-be-denoised signal, carrying out wavelet transformation, and obtaining a wavelet coefficient; designing a new threshold function on the basis of a traditional thresholdfunction, proving the property of the new threshold function through mathematical derivation, and determining threshold parameters to be optimized; improving an original artificial bee colony algorithm; taking a mean square error between the to-be-denoised signal and the denoised signal as a fitness function of the improved artificial bee colony algorithm in S3, and obtaining an optimal thresholdparameter under the condition of obtaining a minimum mean square error; and applying the optimal threshold parameter obtained in the step S4 to the new threshold function in the step S2, performing shrinkage processing on the wavelet coefficient to obtain a new wavelet coefficient, and performing inverse wavelet transform to obtain a denoised signal. According to the parameter wavelet threshold signal denoising method, a smaller mean square error, a higher output signal-to-noise ratio and a larger noise rejection ratio can be obtained.

The embodiment of the invention discloses a frequency diverse array-multiple-input multiple-output (FDA-MIMO) dimensionality-reduction space-time adaptive clutter suppression method and device based on an auxiliary channel. The FDA-MIMO dimensionality-reduction space-time adaptive clutter suppression method can comprises the steps that original data of an FDA-MIMO radar are subjected to primary distance dependence compensation according to Doppler center estimate values of all distance units, and thus primary compensation data are obtained; the primary compensation data are subjected to secondary distance dependence compensation according to the step quantity of the transmitted signal carrier frequency of the FDA-MIMO radar relative to the reference carrier frequency, and thus secondary compensation data are obtained; the secondary compensation data are converted to a Doppler-transmitting-receiving space frequency domain along a clutter ridge, and thus converted data of the Doppler-transmitting-receiving space frequency domain are obtained; an adaptive clutter suppression weight vector is obtained according to the converted data and the minimum variance constraint theory; and the converted data are subjected to clutter suppression according to the adaptive clutter suppression weight vector, and thus output data after clutter suppression are obtained.

The invention discloses an underwater acoustic signal denoising method based on self-adaptive window filtering and wavelet threshold optimization. The method comprises the steps: firstly, Gaussian/non-Gaussian pulse noise in an underwater acoustic channel is described by combining S[alpha]S distribution and a normal distribution model; a median filtering method based on a self-adaptive window is designed, the size of the filtering window is corrected according to the number of noise points in the window, and non-Gaussian pulse noise is restrained; then, based on an improved artificial bee colony method GDES-ABC, threshold parameters of a wavelet threshold denoising method are optimized, and the Gaussian noise suppression capacity is improved. According to the method, Gaussian/non-Gaussianpulse noise in a complex underwater acoustic environment can be effectively suppressed, the receiving capability of underwater acoustic communication signals such as 2FSK, QPSK and 16QAM is improved,and a relatively high output signal-to-noise ratio and a relatively high noise suppression ratio are obtained.

The invention provides a radar pulse signal sampling jitter real-time elimination processing method and aims to solve image jitter and distance shake caused by sampling jitter. The method is characterized in that a pulse phase acquisition and storage module, a system clock and sampling pulse phase relationship decision module and a sampling signal delay and output module are designed in FPGA; values after frequency division clock sampling respectively pass through a shift register; the trigger signal values acquired by a frequency division clock in the shift registers are respectively read bythe system clock and sampling pulse phase relationship decision module, according to the combination relationship of the values in the shift registers, the system clock and sampling pulse phase relationship is determined, and the determination result is sent to the sampling signal delay and output module; appropriate output delay is looked up in a lookup table by the sampling signal delay and output module, output pulses strictly synchronized with the system clock are generated, and thereby the radar pulse signal sampling jitter is eliminated in real time.

The invention discloses a blind equalization method for a wavelet neural network based on space diversity. On the basis of analysis of a space diversity technology and the equalization performance of a wavelet neural network, the method reduces the influence of fading by utilizing the space diversity and overcomes intersymobl interferences by using the stronger approximation capacity of a blind equalizer of the wavelet neural network. The invention overcomes the intersymobl interferences caused by the multipath propagation and the fading characteristic of a channel at a receiving end, improves the communication quality and has high convergence speed and small mean square error. The effectiveness of the method is verified by an acoustic channel simulation result. The method can effectivelyrealize the separation of signals and noise and the real-time restoration of the signals.

The invention relates to a maximum ratio channel equalization method of short wave (SW) multiple carrier-code division multiple access (MC-CDMA), which comprises the following steps: firstly, mapping data frames to be sent in a displacement mapping module into cyclic shift with original direct sequences, and delivering the cyclic shift which is subjected to serial-to-parallel conversion to inverse fast Fourier transform (IFFT) so as to be changed into time domain modulating signals; carrying out serial-to-parallel conversion on modulated parallel time domain signals, inserting pilot frequency symbols into the obtained parallel time domain signals after serial-to-parallel conversion, and sending the obtained parallel time domain signals in which the pilot frequency symbols are inserted to a receiving end by a channel; after the signals which are received by the receiving end and comprise the pilot frequency symbols and the data frames are subjected to the serial-to-parallel conversion, firstly sending to fast Fourier transform (FFT) and then evaluating the transmission functions of the SW channel by the received pilot frequency symbols; obtaining equilibrium factors of maximal ratio combining efficiency (MRCE) through the data frames received by the receiving end in accordance with the evaluated the transmission functions of the channel, and then carrying out the maximum ratio channel equalization; and carrying out correlation on each sub-carrier datum subjected to the maximum ratio equalization and sequences generated by local spreading codes, obtaining dispreading data by judgment, and receiving the next data frame by a receiver continuously. The maximum ratio channel equalization method has a preferable bit error ratio and is used to reach an anti-interference effect.

The invention relates to a long baseline imaging STAP method suitable for a distributed geosynchronous orbit SAR. The method is characterized in that an azimuth spectrum of multi-channel signals is derived through constructing a precise distributed GEO SAR moving target multi-channel signal model under the long baseline, an airspace guiding vector is then modified to match a distributed GEO SAR moving target, and thereby problems of far-field hypothesis failure and bending trajectory caused by the long baseline are overcome. The method is advantaged in that the method has a high output signal-to-noise ratio, and detection performance of the distributed GEO SAR moving target under long baseline conditions is improved.

The invention discloses an improved space-time adaptive monopulse angle measurement method based on Joint Domain Localized (JDL) processing. When a target space frequency and a doppler frequency are mismatched at the same time, a space-time domain dimension-reducing matrix and a target detection space-time guiding vector of a JDL algorithm are iterated and updated by the target doppler frequency and the target azimuth obtained by estimation. Problems that a multichannel airborne radar system has few channels, low doppler resolution and large angle measurement errors are solved. The invention can accurately estimate the target space angle by two steps of iteration, and is easy to implement in engineering.

The invention discloses an endpoint detection method for reducing noise influence, and relates to front-end processing in the field of voice recognition; an improved multi-window spectral estimation spectral subtraction method is used for noise reduction of noisy voice to obtain a high output signal-to-noise ratio, then a sub-band spectral entropy method with variable parameters is used for eliminating the noise influence on each spectral line amplitude, and sub-band spectral entropy is used as a parameter for double-threshold endpoint detection. The method improves the end point detection misjudgment rate in a low signal-to-noise ratio environment, is quick and efficient, is higher in robustness, and has a certain application value in the fields of smart home, industrial control and medical treatment.

The invention discloses a communication signal detection method based on adaptive stochastic resonance. The problem that the system structure parameters and the calculation step size of the stochasticresonance in the prior art are difficult to be selected is solved. The method comprises the following steps of: inputting a communication signal, determining parameters of the particle swarm and initializing the parameters; sampling the received signal in time; establishing a model of adaptive stochastic resonance system; calculating fitness of stochastic resonance system by particle swarm optimization algorithm; determining whether the iteration is terminated; outputting detected signal. A particle swarm is established by taking a stochastic resonance system as a model, Particle Swarm Optimization (PSO) is used to obtain the optimal structural parameters of the adaptive stochastic resonance (ASR) system. The output signal-to-noise ratio (SNR) and bit error rate (BER) are used as fitnessvalues. The weak signal under noise background can be detected optimally. The invention greatly improves the output signal-to-noise ratio, reduces the bit error rate, and is used for digital signal detection.