80 results about "Gaussian signal" patented technology

The invention provides an adaptive optimal ad-hoc network method based on multiple robots and a Gaussian signal. The method comprises the following steps that S1.a master robot draws a two-dimensional map of the entire work environment and adopts laser and a camera to draw the two-dimensional map of the environment; S2.the master robot collects WiFi signal sample points of a base station in a process for drawing the two-dimensional map or a random walk process and then carries out modelling on a WiFi signal to form a WiFi signal model; and S3.an optimal relay position of each robot is searched based on the WiFi signal model established in the step S2. The method has the beneficial effects that the influence of the environment on a wireless signal is fully considered, and the spatial position of each relay robot can be automatically scheduled in real time according to the position change of nodes of a communication network in the environment, so that the communication network can obtain the best communication quality quickly.

The invention discloses a matrix-transformation-based method for underdetermined blind source separation. The method comprises the following steps of: gradually transforming a mixed matrix to obtain a transformed matrix, then acting the transformed matrix on an observation signal to gradually remove each source signal, and constructing multi-stage binary shielding templates by zero points generated in each observation signal to gradually separate the source signal. By the method, the requirement on sparsity of the source signal is reduced; aliasing of at most M-1 paths of source signals is prevented (wherein M is the number of sensors); source signals for forming each time frequency point can be clearly known; the underdetermined separation problem of music signals and noise signals is solved; the statistical property requirement on the source signal is low; the underdetermined separation problem of Gaussian signals and related signals is solved; processing stages are adjusted according to the requirement of separation precision; when the processing level is more, the separation result is better; the separation process is realized by aid of the matrix transformation; and the complexity is relatively low.

The invention discloses an underdetermined blind source separation (UBSS) method based on a maximum matrix diagonal rate. The method comprises the following steps of: constructing inverse matrixes of C*M / N M*M-dimensional sub matrixes of a mixed matrix (wherein M and N are respectively the number of sensors and the number of source signals); multiplying the inverse matrixes by observation signal vectors to acquire initial estimation signal vectors; and sequentially calculating the covariance matrix, the solid matrix, the absolute value matrix and the diagonal rate of each initial estimation signal vector, selecting the initial estimation signal vector corresponding to the maximum diagonal rate as estimation of a source signal vector, and thus realizing underdetermined separation of sourcesignals. By the method, the requirement for source signal sparseness is reduced, aliasing of road source signals is allowed at each time frequency point at most, and the underdetermined separation problem of music signals and noise signals is solved. The requirement for the statistical property of the source signals is low, and the underdetermined separation problem of Gaussian signals and related signals is solved. In addition, by the method, processing of each time frequency point and each sub matrix can be executed in parallel, and hardware implementation is facilitated.

A LIDAR device and method for determining the range of a target surface using a threshold detector circuit that differentiates the laser return signal to define a differentiated signal. The signal level crossing point or threshold is representative of the peak amplitude of the return signal. The device and method compare the signal level crossing point to a predetermined threshold level to determine the range of the target surface in a LIDAR system.

The invention relates to a blind image separation method based on frequency-domain sparse component analysis. In the currently-provided blind source separation algorithm, an independent component analysis method with better separation effect has a blind source separation premise that source signals are not in Gaussian distribution, are mutually independent, and can not thoroughly separate sub-Gaussian signals in image signals. The sparse component analysis is a novel blind source separation technology developed in recent years, by applying the technology, source signals are extracted by utilizing the sparse properties of the signals and the better separation effect is obtained. The image signals which do not satisfy sparse conditions can not be separated by applying a traditional sparse component analysis model. In the invention, the images are converted into the frequency domain from the space domain by combining the characteristic that the images are sparse in the frequency domain space and utilizing sparseness algorithms, such as wavelet transform, and the like; a sparse component analysis model is educed in the frequency domain; and a hybrid matrix estimation method and a source signal estimation method based on linear-clustering sparse component analysis are provided; therefore, the source images are extracted. Experiments prove that the method of the invention has the separation precision up to 100 percent and is superior to other separation methods.

The invention discloses a nested array direction-of-arrival estimation method based on off-grid sparse Bayesian learning. The method comprises: step one, matching filtering is carried out on a narrowband Gaussian signal received by a nested array to obtain a data vector x(t) including DOA information at a t time; step two, with the x(t), a received data covariance matrix R^x under a T snapshot number is calculated and vectorization is carried out on the R^x to obtain a one-dimensional data vector Y^; step three, K^ grid points theta^={theta^i}<K^>i=1 are divided uniformly in a range [-pai / 2,pai / 2], a counting variable I of the number of times for iteration is set to be 1, a variance vector delta and an e angle offset vector beta are initialized, and a measuring matrix phi (theta^, beta) isconstructed; step four, a variance vector theta and an angle deviation value beta in a (K^+1) dimension are updated by using an EM criterion; step five, the grid theta^ is updated by using the beta value obtained at the step four; step six, whether the counting variable I of the number of times for iteration reaches an upper limit L or the delta converges is determined; if not, the counting variable I of the number of times for iteration conforms to a formula: 1=1+1, wherein the beta is equal to 0; updating is carried out on the phi (theta^, beta) by using the updated grid theta ^, and the step four is performed; and step seven, spectral peak searching is carried out on the variance vector delta to obtain angles corresponding to K maximum points, so that a final estimation value of the target angle is obtained.

The present invention discloses a blind image quality evaluation method based on combining a gradient signal and a Laplacian of Gaussian (LOG) signal. The method comprises: firstly extracting a gradient modulus signal and a LOG signal of an image; and normalizing the two signals by using a joint adaptive normalization process. Based on the two characteristic representations, three blind image quality evaluation methods which can predict image quality degradation are proposed. In each of the three methods, model training is performed on a public image subjective quality evaluation database by using a method that supports vector machine regression. A large number of experimental results prove that the three algorithms proposed by the invention all have good predict performance on the blind image quality evaluation. Compared with other existing algorithms, the model 3 method has the best quality evaluation performance and optimal database stability. Furthermore, the three algorithms are high in operation efficiency.

The invention discloses a complex signal time delay estimation method based on least square sample fitting. In the method, cross-correlation cost functions are firstly given, a sinc interpolation formula is used for estimating a correlative data value by a finite quantity of samples, the cost function of a model is minimized through an LS (Least Square) criterion, an almost-unbiased estimation value is obtained, and a CRLB (Cramer-Rao Lower Bound) expression of an algorithm is given. An experiment indicates that accuracy on the basis of the least square sample fitting is higher by aiming at aGaussian signal, and the estimation performance of a time delay estimation algorithm based on the least square is obviously higher than the estimation performance of a traditional time delay estimation algorithm under a condition of a certain signal-to-noise ratio or certain signal length. Particularly, the performance of a cross-correlation algorithm and an MMSE (Minimum Mean Square Error)-basedalgorithm is obviously lowered for the randomness of the Gaussian signal. The method is an effective time delay estimation algorithm.

The invention relates to an exponential function echo cancellation method based on one-norm and zero-attractor, and discloses a zero-norm sub-band echo cancellation method. The method comprises the following steps: A, remote signal sampling; B, echo signal estimation; C, echo signal cancellation; D, filter tap weight coefficient updating; and E, assuming that n is equal to n+1, and repeating steps B, C and D to achieve real-time echo cancellation. According to the method disclosed by the invention, a one-norm of a weight coefficient vector is adopted in the derivation of a weight coefficient vector updating formula, the norm relates to FORMULA, wherein gamma is a proportion parameter of the one-norm of the weight coefficient vector, zero-attractor Rho (n) is obtained from the derivation, Rho (n) is equal to b.sgnW(n), which means that the speed that the weight coefficient vector is updated to zero is faster under sparse systems; a method for taking an exponential function as a cost function is adopted during weight update, and thus the cost function is changed to FORMULA when updating the weight coefficient vector, and a new step length factor is introduced to ensure that output signals of a filter can obtain faster convergence and lower steady state maladjustment under Gaussian signals and the sparse systems.

The invention discloses a communication radiation source individual identification method and system. According to the technical scheme, the method comprises the steps of: receiving communication radiation source signal, carrying out radio frequency preselection amplification, carrying out frequency mixing, carrying out intermediate frequency filtering, carrying out A / D conversion, carrying out digital orthogonal demodulation, carrying out rectangular integration double-spectrum transformation, dividing rectangular integration double-spectrum characteristic vectors into a training sample set and a testing sample set, constructing a rectangular integration double-spectrum characteristic dictionary, carrying out non-linear transformation, carrying out mapping to a cooperation projection matrix, constructing a classifier, obtaining classification residual errors, and using the type corresponding to the smallest classification residual error as type of a communication radiation source individual. According to the invention, a small sample problem in a communication radiation source individual identification process is solved, the time complexity of the algorithm is lowered, and the phase and amplitude information distortion problem in the process when an existing method based on the time domain, frequency domain or time frequency domain is used to process non-stable or non-Gaussian signals is adopted.

The invention provides a continuous random measurement matrix-based continuous variable quantum key distribution method. The method comprises the following steps of first, Gaussian modulation of a coherent state by a sending end, wherein the sending end prepares true random numbers of Gaussian distribution and prepares the coherent state, and encodes the coherent state through strength and a phasemodulator according to an element of a Gaussian random number set; second, transmission of a Gaussian signal, wherein the sending end transmits the encoded coherent state signal to a receiving end through a quantum channel; third, demodulation of a continuous random measurement matrix by the receiving end, wherein the receiving end prepares a plurality of binary variables that are distributed continuously and randomly; and fourth, data consultation and privacy amplification. Through adoption of the method, matrix comparison is prevented so that some original data strings are not discarded, further, the method is easy to realize in the prior art. Due to use of continuously distributed phase angles, requirements on performance of a digital-analog converter and a phase modulator are relatively lowered, so that engineering is easy to realize.

Disclosed is an ultra broadband pulse signal modulation and demodulation method in fractional fourier transform field, which relates to an information modulation and demodulation technology in an ultra broadband system. The invention solves the problem of low transmission efficiency in the PAM, PPM modulation methods which use one channel of pulse as the information transmission carrier during communication. The process of the modulation is: divide the multi-ary digital information source into two parallel channels of digital information; and then through the transmitting antenna, transmit the transmitting pulse which is obtained by respectively modulating and adding the two channels of digital information with a real chip signal and a gaussian signal under the control of a transmitting clock. The process of the demodulation is: filter the received pulse signal and sample the signal under the control of the receiving end clock; then transform the signal to p order fractional fourier transform field; and then map the two channels of digital signal which is obtained by performing correlation demodulation on the transformed signal with the real chip signal and the gaussian signal into one channel of multi-ary digital information. The gaussian signal can also be real chip signal. The ultra broadband pulse signal modulation and demodulation method of the invention can be applied in ultra broadband pulse communication system.

The invention discloses an analog digital mixed pulse amplitude analyzer and an analysis technology thereof. The analog digital mixed pulse amplitude analyzer comprises a detector, a fast channel circuit, a slow channel circuit and an FPGA (Field Programmable Gate Array), wherein the fast channel circuit is connected with the output end of the detector through a differentiating circuit, and is used for carrying out amplitude comparison on an exponential signal output by the differentiating circuit, and generating rectangular pulse signals; the slow channel circuit is connected with the output end of the detector through an integrating circuit, and is used for splitting a Gaussian signal output by the integrating circuit, and acquiring baseline values of nuclear pulse signals; the FPGA is connected with the fast channel circuit and the slow channel circuit, and is used for controlling working timing of the fast channel circuit and the slow channel circuit, measuring a time interval of two rectangular pulse signals, determining an optimal baseline value and judging whether signal accumulation occurs or not according to the time interval, and eliminating the counting rate loss brought by the signal accumulation through counting rate correction if the signal accumulation occurs. The analog digital mixed pulse amplitude analyzer has the advantages of fast processing speed, less dead time, low power consumption, high counting rate and the like.

The invention discloses a distributed compressed forwarding system of a Gaussian source and an optimization method of the system. In the system, the source sends analog Gaussian signals, distributed compressed coding is carried out in relays, and digital transmission is carried out. A theoretical analysis frame of the system is provided by considering that reception signal to noise ratios of different relays satisfy certain proportional relation and a reception end has different receiving signal to noise ratios for different relay signals in an additive white Gaussian noise channel. For a multi-relay distributed source coding problem, a CEO theory is used to establish a rate distortion function of a multi-relay network, transmission rates of the multiple relays are obtained, a Shannon channel capacity theory is combined, the compressed transmission rates of the relay are linked to the capacity of the channel from the relays to the channel, an optimized design equation of the system is provided, and an optimal solving algorithm is provided. Under the condition that the total power is limited, power distribution is carried out between the source and the relay network based on the signal to noise ratios, and the signal to noise ratio performance of the reception end of the system is maximized.

The present invention is directed to a method and system for capturing an information signal from within a noisy background utilizing a non-Gaussian model for the a priori statistics of the information signal conditioned on other a priori quantities. A specific implementation utilizing a Gaussian Mixture Model (GMM) is described. The GMM implementation includes Wiener filtering as a special case, and includes methods for adaptively tracking multiple properties of the input noise and the information signal, including noise PSD, information signal PSD, information signal spectral amplitude, and probability of information signal presence versus time and frequency.

The invention relates to a novel high-resolution orientation-estimating method based on a Cauchy Gaussian model. By the method, the requirements of the underwater wideband high-frequency three-dimensional imaging of a buried mine detecting sonar on a target orientation resolving power are met. The method comprises the steps of: by using the Cauchy-Gaussian signal model, expressing an orientation spectrum as a regularized space-domain Fourier transform constraint optimizing problem; providing a spectral estimator tolerant to model parameters; by seeking sparse distribution features of a source signal in a space, realizing high-resolution spectral orientation estimation within a single-frequency-domain snapshot; and resolving coherent sources without decorrelation. The method has the advantages that: by the method, the high orientation resolution is achieved within the single snapshot through the constraint optimization of a cost function; the tolerance to parameters of a signal probability distributing model is high, so that the method is practical in engineering; the numerical simulation and sea trial data analyzing results prove that the method is applicable for the high target orientation resolution of a small-aperture subarray in shortage of the snapshot.

The invention provides a laser stripe axis extracting method in fitting multiple Gaussian signals. According to the method of the invention, through using a set of Gaussian signals with different expectations and methods, the Gaussian signals are combined through different positions and weights, thereby maximally fitting distribution of pixel values in a certain line of a laser line image. When an optimal fitting combination is found, optimal estimation to the center of the laser line can be obtained according to the position of each Gaussian signal in fitting. The laser strip axis extracting method can be used for accurately estimating axis of the laser strip and improving 3D scanning reconstruction precision on condition that pixel value distribution of the laser strip image cannot satisfy symmetry and heterogeneity.

The invention provides a state estimation method for a nonlinear dynamic system under non-Gaussian noise. According to the method, a nonlinear dynamic system model comprising a process noise signal and a non-Gaussian measurement noise signal is adopted; the real-time information of the system state is estimated based on the nonlinear dynamic process model and the measurement feedback signal to obtain a state estimation signal, and output feedback is performed; and the state estimation signal is used as the controller input at the next moment, so that the influence of the non-Gaussian measurement noise signal on the system performance is weakened, and the accuracy of state estimation is improved. According to the method, the model prediction information and the non-Gaussian signal measuredby the sensor are combined, and the current optimal state of the system is estimated by adopting the newly proposed extended Kalman filtering algorithm based on the dynamic data correction technology,so that the limitation on the measurement noise distribution in the existing Kalman filtering technology is broken through; and a selectable scheme is provided for solving the state estimation problem of the nonlinear dynamic system under the condition of non-Gaussian measurement noise.

The invention discloses a neural fuzzy Wiener-Hammerstein model identification method based on a combined signal source. The method comprises the steps of: constructing a single-input single-output Wiener model which is formed by connecting an input dynamic linear link, an output dynamic linear link and a static non-linear link in series; combining Gaussian signals and binary signals to form a multi-signal source as the input of the model; separating and identifying the dynamic linear link and the static nonlinear link in series by multi-signal sources. The neural-fuzzy system is used to approximate the static nonlinear link, wherein, if the input is a Gaussian signal, the Wiener-Hammerstein series module is separated effectively according to an assigned theorem, and the input product model of the input dynamic linear link and the output product model of the output dynamic linear link are separated correctly according to the non-excitation characteristic of the binary signal, so as toobtain the constituent variable parameters of the static sub-linear link. The identification method of the invention greatly simplifies the identification process of the model and has high predictionaccuracy of the model.

The invention discloses a mechanical vibration state identification method based on statistics in different orders and a support vector machine. The mechanical vibration state identification method includes the following steps of (1) using a vibration measurement device to collect vibration data of a mechanical system and subjecting the vibration data to segmentation and de-meaning pretreatment; (2) calculating third-order statistics and fourth-order statistics of each segment of the vibration data after pretreatment, and using the third-order statistics and the fourth-order statistics as two feature vectors; estimating fraction low-order statistics of each segment of the data after treatment, i.e., a feature index alpha and a dispersion coefficient gamma as another two feature vectors; (3) classifying and judging vibration states of the mechanical system by means of the support vector machine on the basis of the four feature vectors. The mechanical vibration state identification method based on statistics in different orders and the support vector machine has the advantages that under the concept of non-Gaussian signal processing, two kinds of statistical methods of high-order statistics and fracture low-order statistics in a feature extracting method are combined, vibration signal features can be more comprehensively extracted, and the problem of performance degradation of a system under a non-Gaussian condition in traditional second-order statistics based methods is solved.

The invention relates to a non-linear dynamic system signal processing method based on sampling rejecting particle filter algorithm, which comprises the following steps that: the state space of a particle description state system is adopted, the state space model of a non-linear dynamic system is set to be: xk=f(xk-1)+vk-lzk=h(xk)+nk, wherein xk and zk respectively refer to the state and the observation value of the system at k, f(xk-l) and h(xk) respectively refer to the state transition equation and the observation equation of the system, and vk-l and nk respectively refer to the system noise and the observation noise; and for each newly sampled particle, firstly the probability that the particle is accepted is calculated and then whether the particle is accepted is judged. The non-linear dynamic system signal processing method based on sampling rejecting particle filter algorithm can effectively reduce particle weight degradation, has good particle diversity, and effectively process non-linear and non-gaussian signals.

The invention discloses a distributed compression forwarding system of a multi-relay network under a Gaussian source and a design method. A source sends a simulated Gaussian signal, performs distributed source coding at a relay node, and performs the digital transmission. Since the simulated signal is transmitted, the index for system performance evaluation is the signal-to-noise ratio. A theoretical analysis frame of the system is proposed in consideration that the sending power of each relay node is the same and all noises powers are the same under an additive Gaussian white noise channel, a rate-distortion function of the multi-relay network is established by use of the CEO theory, and the connection between a sensing network and a digital communication network is established in combination with a Shannon channel capacity theory. An optimal design theory method of the system is proposed, and the power distribution is performed between the sensing network and a communication network based on the signal-to-noise rate under a condition that the total power is limited, thereby enabling the signal-to-noise rate to achieve the maximum. The theoretical analysis and the simulation result show that the optimal compression forwarding system provided by the invention can provide better signal-to-noise ratio in comparison with an amplification forwarding system.

The invention discloses a nuclear pulse signal digital Gaussian forming method based on an analog CR-RC circuit. The nuclear pulse signal digital Gaussian forming method based on the analog CR-RC circuit includes the steps that according to the analog CR-RC circuit, the step response of the CR-RC circuit is derived; the impulse response of a digital Gaussian forming system is derived with a step response invariance method; after digital nuclear pulse signals pass through the digital Gaussian forming system, Gaussian signals are formed by the digital nuclear pulse signals. With the method, the digital Gaussian forming demand of the nuclear pulse signals is met, the signal waveform after forming is a good Gaussian waveform, and certain capacity for separating pileup nuclear pulse signals is achieved.

The invention relates to a global optimal particle filtering method and a global optimal particle filter. The problem of particle filter processing nonlinear and non-Gaussian signals is effectively solved. The main technical means is to use the Lamarck genetic natural law to construct a global optimal particle filter comprising: generating an initial particle set; using Unscented Kalman Filter to perform importance sampling on the initial particle set to obtain sampled particles; performing floating-point number encoding for each of the sampled particles to obtain an encoded particle set; setting an initial population; using the initial population as an original trial population to sequentially perform a Lamarck overwriting operation, a real number decoding operation, and an elite retention operation; using the real-number optimal candidate particle as a prediction sample for a next moment, and obtaining a state estimation value of a system. The invention is applicable to machine learning.

The invention relates to an error correction method for the measuring distance accuracy of a laser radar by a laser radar echo signal and distance. The method comprise the following steps that: a laser radar emits a light pulse and the light pulse is detected and received by a photoelectric detector to obtain a Gaussian signal; a measuring error is corrected according to different signal strengthinformation obtained by different distances; and since the object measuring distances are different, the surface reflectivities of the measured objects are different and the tilt angles of the measures object are different, so that the energy change of the light pulse returning to the optical detector after irradiation on the object surface is affected. A problem that various factors, causing themeasured return power changes, of the object change in the laser radar measurement process and thus the accuracy of the measurement result is affected is solved. With the correction system, the measurement result is closer to the real distance and the measurement accuracy is improved.

The invention discloses a nonlinear normalization based IQA (image quality assessment) method of a Laplace-Gaussian signal. The method comprises steps as follows: redundancy elimination expression is performed on images firstly and is completed through two processes including LOG filtering and nonlinear normalization; the two processes are used for eliminating first-order and second-order statistical redundancy in the images and reducing high-order statistical redundancy respectively; two computing methods are proposed to predict the subjective quality or the distortion degree of the images, and the two computing methods are marked as NLOG-MSE (mean square error) and NLOG-COR (correlation) respectively; according to NLOG-MSE, mean square errors between the original images subjected to redundancy elimination expression and test images is computed to obtain the distortion measurement of the test images, and according to NLOG-COR, correlation between the original image and test image in each point and redundancy elimination expression is computed to predict the image quality. The experimental result proves that the two computing methods have good prediction performance in the aspect of IQA, the NLOG-MSE method has simple computation, and the application of the NLOG-MSE method in other fields is greatly facilitated.

The invention provides an improved EMD algorithm based on polynomial, which comprises the following steps: 1, selecting a window with the start point of the signal as the center point, and carrying out polynomial fitting on the signal value in the window; 2, taking the center point of the window as an average point to construct a discrete Gaussian signal; 3, multiplying that fitting sequence valuein the window and the correspond Gaussian signal value and summing to obtain the denoising value of the center signal point of the window; 4, sliding the window in steps of 1; repeating Step 1-3, completing denoising of all signal points of that signal; 5, Empirical mode decomposition being performed on the de-noised signal to obtain a reconstructed signal. The invention improves the traditionalEMD algorithm, decomposes the EEG signal with the algorithm of the invention, obtains the intrinsic mode function signal with relatively concentrated frequency, distinguishes the intrinsic mode function of each frequency band, greatly suppresses the problem of the end effect when decomposing, and improves the problem of the mode aliasing at the same time.

The invention discloses a short noise continuous variable quantum key distribution monitoring method with safety. The method comprises the following steps of 1, transmitting a Gaussian signal; 2, randomly measuring data or short noise variance; 3, performing real-time evaluation on the short noise variance; 4, evaluating a monitoring error; and 5, calculating a safe secret key rate. The short noise continuous variable quantum key distribution monitoring method has untrahigh safety; and through real-time monitoring to the short noise variance, all existing attacks to local oscillating light are resisted. Besides, because of limited length of monitoring data and limited extinction ratio of a strength modulator in a real condition, a safety monitoring system has a monitoring error. The monitoring error is calculated and is furthermore used as a system over-noise, and furthermore the safe secret key rate with safety is calculated. Furthermore the short noise continuous variable quantum key distribution monitoring method realizes easy realization. Through adding a strength modulator in a receiving-end signal path, safety monitoring is realized and realization cost is saved.

The invention discloses an IRS-assisted MISO system performance optimization method for hardware distortion, and the method comprises the steps that a multi-antenna base station wide linear precodes messages of M information users, generates a baseband transmission signal, processes the baseband transmission signal into an asymmetric Gaussian signal, and generates an output signal through a high-power amplifier; with the assistance of an intelligent reflecting surface, the multi-antenna base station transmits the output signal generated by the high-power amplifier in a broadcasting manner, and controls the phase shift of a reflecting element in real time through a controller on the intelligent reflecting surface; the rates of M information users are decoded; and by taking the rates of the M information users as performance evaluation, a base station beam forming vector and a phase shift vector at an intelligent reflecting surface are optimized under the condition of satisfying the total power constraint of the base station, and the minimum reachable rates of the information users are maximized to complete performance optimization. According to the invention, the IGS is used for transmission, so that the reachable rate of the information user is further improved.