182results about How to "Reduce mean square error" patented technology

The invention discloses an information transmission method for reducing the peak to average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. In the method, after being coded and interleaved, data stream is modulated by a new distorted constellation diagram so as to acquire a frequency domain signal; for the frequency domain signal, a phase rotation sequence is selected to reduce the PAPR of a time domain signal which is subjected to inverse fast Fourier transform (IFFT); at a receiver end, the received signal is subjected to fast Fourier transform (FFT) and channel estimation so as to acquire the frequency domain signal; for the frequency domain signal, recovering is performed by different phase rotation sequences so as to acquire candidate signals; amongthe candidate signals, only the signal which is recovered by the right phase rotation sequence has a minimal mean square error when the signals are subjected to hard decision together with the constellation diagram; and the candidate signal which has the minimal mean square error is selected as the original signal, and the corresponding phase rotation sequence is the recovered side band information. The method can effectively reduce the peak to average power ratio of the OFDM signal, does not need to send the side band information simultaneously, improves the frequency band utilization ratio of systems, and can be applied to various communication systems using OFDM technology.

The invention discloses a face super-resolution processing method based on K neighbor sparse coding average value constraint, relating to the technical field of image resolution processing, in particular to a face super-resolution processing method based on K neighbor sparse coding average value constraint. The method comprises the following steps of: according to prior information of the position of a face block, clustering image blocks of a training sample to obtain a pair of high-and-low-resolution sparse representation dictionaries in relevant positions; performing sparse representation on K neighbor of the input image block with the low-resolution dictionary, thus obtaining sparse coding average values; and realizing the sparse representation of a low-resolution image block based on sparse prior and K neighbor sparse coding average value constraint, realizing the reconstruction of a high-resolution image block through coefficient mapping, and finally overlapping and averaging to obtain a high-resolution face image. According to the method, on the basis of keeping the similarity of the reconstructed face image, the definition of the face image is improved, and the quality of the super-resolution image is enhanced.

The embodiment of the invention relates to an orthogonal frequency division multiplexing (OFDM)-transform domain communication system (TDCS) signal transmission method, an OFDM-TDCS signal transmitter, an OFDM-TDCS signal receiving method, an OFDM-TDCS signal receiver, and a system. The signal receiver performs channel estimation and data symbol detection for the first time on each original frame of a received signal, performs a posterior probability (APP) decoding on the extrinsic information of bits of a complete coded data block, and performs iterative channel estimation and data symbol detection by utilizing an extended frame according to a prior probability log ratio of the bits fed back by the APP decoding to obtain an OFDM-TDCS channel estimation result and a data symbol detection result. The embodiment of the invention reduces a mean square error (MSE) of the channel estimation by using an iterative channel estimation technology, has relatively higher anti-multipath performance and relatively higher anti-fading performance, and enables a coded OFDM-TDCS to reliably transmit data at an extremely low signal to noise ratio and under the cognitive radio (CR) condition of discontinuous or inconsistent frequency spectrums used by the transmitter and the receiver.

The invention discloses a particle swarm optimization based orthogonal wavelet blind equalization method. The method comprises the following steps of: allowing a transmitted signal a(k) to pass through a pulse response channel h(k) to acquire a channel output signal x(k); acquiring an orthogonal wavelet transformation (WT) input signal y(k) through channel noise n(k) and x(k); performing WT on the input signal y(k) to acquire an output signal R(k); taking the input signal y(k) as input of a particle swarm optimization (PSO) algorithm and randomly initializing a group of weight vectors, wherein each particle corresponds to each group of weight vectors one to one; determining a fitness function of PSO through a cost function of an orthogonal wavelet transformation-constant module algorithm (WT-CMA) blind equalization method; when a fitness value is the maximum, finding out an optimal position vector in the group and taking the optimal position vector as an initialization weight vector W(k) of the WT-CMA; and acquiring an equalizer output signal z(k) from the output signal R(k) and initialization weight vector W(k). In the method, the optimal equalizer initialization weight vector is sought through PSO, and the autocorrelation of the signal is reduced by WT. Compared with WT-CMA, the method has higher convergence rate and lower steady-state error.

The invention discloses a pilot frequency distribution method in DCS channel estimation. The method comprises modeling a pilot frequency position optimization problem into a combinational optimization problem with the minimum channel estimation error serving as the starting point; solving the combinational optimization problem through a raised genetic algorithm to obtain an optimal pilot frequency position set enabling the channel estimation error to be the smallest. By the aid of the method, compared with traditional least square channel estimation, lower estimation errors and higher frequency spectrum effectiveness can be obtained on the basis of DCS.

The invention provides an improved coding and decoding device and method based on the linear predictive parameter coding basic principle. Intra-frame and inter-frame correlations of parameters and the correlations among all the parameters are fully utilized, and all the parameters of a coder are compressed by the utilization of the vector quantization technology; while the compression is performed, the auditory perception characteristic of the human ears is fully utilized to perform quantization or inverse quantization on all the parameters by the utilization of different weighing distortion measurements, so that data are effectively compressed on the premise that the auditory sense quality of the human ears is not affected. On the premise that speech quality is ensured, the coding and decoding device and method of ultralow-bit-rate speech achieves coding and decoding of the ultralow-bit-rate speech with algorithm complexity as low as possible while working at the bit rate of 600bps and the bit rate of 300bps.

The invention relates to a multipath channel estimation method which comprises the following steps: roughly estimating a multipath channel to obtain an initial estimated value of a multipath channel time-lay di; calculating an initial estimated value of an amplitude adjustment coefficient Ai from the initial estimated value of the multipath channel time-lay di; refining the multipath channel time-lay di and the amplitude adjustment coefficient Ai by using a matching pursuit (MP) method to obtain refined multipath channel time-lay di and the amplitude adjustment coefficient Ai; and determining an impulse response h(n) of the multipath channel according to the path number M of the multipath channel and the estimated values of the multipath channel time-lay di and the amplitude adjustment coefficient Ai, and obtaining a multipath channel model. The method can better optimize channel parameters, construct more precise channels, and improve the anti-jamming property and robustness of the multipath channel.

The invention discloses a 3D MIMO-OFDM system channel estimation method based on a convolutional neural network. The method comprises the following steps: calculating an LS estimation value according to the pilot frequency value received in the 3D MIMO-OFDM system, and preprocessing the LS estimation value to obtain a real part graphical representation and an imaginary part graphical representation; taking the real part graphical representation and the imaginary part graphical representation as the input of a trained real part CECNN model and a trained imaginary part CECNN model respectively, and outputting a complete channel graphical representation respectively; performing normalized reverse operation on the two complete channel imaging representations respectively to obtain real part data and imaginary part data; splicing the real part data and the imaginary part data to obtain a complete channel response value of the 3D MIMO-OFDM system.

The invention discloses a compressive spectrum sensing method. The method is applied to the spectrum hole detection in a cognitive radio system, and belongs to a novel method for carrying out spectrum sensing by a compressive sensing technology. The method carries out condition setting by aiming at observing data, the detecting efficiency in the compressive spectrum sensing is improved, when acceptable conditions are met, the estimation value is reconstructed, when the acceptable conditions are not met, the observing times are increased, and the adaptive process of the matrix observation is realized. The method realizes the integral optimization through reducing the sparseness among the observing matrix array vectors, reducing the correlation among the line vectors and combining the matrix adaptivity observation in a united way. Compared with the ordinary compressive spectrum sensing, the compressive spectrum sensing method has lower mean square errors generated during the spectrum reconstruction, and the spectrum detecting efficiency is higher under the condition of the same observing times; the observing times required by the method is fewer when the same receiving operation performance.

The invention discloses an OFDM (Orthogonal Frequency Division Multiplexing) precise timing synchronous method based on a Zadoff-Chu sequence, which belongs to the technical field of communication and mainly aims at solving the problem of timing deviation caused by the fact that a path with the maximum energy in a multi-path fading channel of an orthogonal frequency division multiplexing system is not a first path. Channel impulse response is estimated through Zadoff-Chu sequence correlation, and a self-adaptive threshold detection algorithm for withstanding residual frequency deviation is established, then the achieving time of the first path is accurately judged, moreover the self-adaptive threshold is modified aiming at the problem that the false alarm probability is increased because of the residual frequency deviation, and verification shows that in a high speed moving multi-path environment, the algorithm detection probability is high, the mean square error is small, the detection probability can be greater than 99% in an SUI-3 channel when the signal to noise ratio is greater than 3dB, and then the method can be applied to a wideband OFDM communication system in the high speed moving environment.

The invention relates to an algorithm and realization of a self-adaptive parallel interference cancellation multi-user detector. The invention is mainly used on a marine radio navigation system based on a spread spectrum system and realizes the goal of providing a multi-user detector based on a self-adaptive parallel interference cancellation multi-user detection algorithm under time-varying channel conditions, wherein the multi-user detector has the advantages that the system error rate is effectively reduced and the system performance is practically improved. The invention is mainly formed by two modules, i.e. a self-adaptive module and a parallel interference cancellation module, wherein the self-adaptive module can enable a system to adapt to a time-varying channel through adjusting the parameter of the system in real time; and the parallel interference cancellation module reconstructs interference signals and cancels the interference signals in the received signals to reach the goals of reducing the error rate and improving the system performance.

In a non-equilibrium system, estimation of the frequency, the amplitude and the phase of noncircular signals is a quite important nonlinear problem. According to the method, the original Smart DFT technology (SDFT) is extended so that the technology is enabled to be applied to real value sinusoidal signals and is also enabled to process complex value noncircular signals. The mean square error of the model can be reduced by applying the least square framework based on the linear prediction (LP) property between the continuous DFT fundamental components so that the improved complex value least square algorithm (CLS) can be obtained. Meanwhile, the invention also provides a complex value improved Pisarenko harmonic decomposition algorithm (CRPHD). The interference of noise can be removed by the method and accurate frequency estimation can be acquired, and the method can be effectively applied to the non-equilibrium three-phase power system containing the noise.

The invention relates to a design method for associated intersection groups division based on hierarchical clustering, and belongs to the technical field of methods for the associated intersection groups division. The method comprises the following steps of: (1) quantitatively calculating the degree of traffic demand, and dynamically determining key intersections by using a farthest point queuingmodel improved by a least squares method; (2) quantifying the influence of cyclic variation of the intersection signals on the traffic correlation between the intersections to improve a calculation model of existing correlation degree through cyclic variation influence coefficients by an inverted S-curve function; and (3) using a hierarchical clustering idea to judge the relationship between the degree of association between each adjacent intersection in the intersection groups and a threshold value, thereby determining the associated intersection groups division and a critical path searchingalgorithm in the intersection groups. According to the design method for the associated intersection groups division based on the hierarchical clustering, multiple intersections in a road network aredivided into a plurality of associated intersection groups, the traffic correlation within each group is high, and the correlation between groups is low. Regional coordination control is transformed into critical path coordination control to prepare for the coordination control for multiple intersections in the road network.

The invention belongs to the field of wireless communication and relates to an iterative method based on channel estimation errors and data detection errors. The method includes that pilot frequency is inserted according to a comb mode during data sending, and data are sent on different antennae; the data are sent to receiving antennae through a wireless channel, and the channel is subjected to modeling at a receiving end by a basis expansion model (BEM); channel BEM coefficient is subjected to modeling by an autorregressive (AR) model; a filter is subjected to initialization and a time updating equation is calculated; filter noise removal is performed and channel estimation is performed; an estimated value of a channel matrix is calculated; a covariance matrix of the channel estimation errors is calculated to perform successive interference cancellation (SIC) data detection. By means of the iterative method based on the channel estimation errors and the data detection errors, a channel estimation and joint detection algorithm is provided, error information in channel estimation and data detection is fully utilized, the accuracy of channel estimation is improved, and correction of data detection is enhanced.

The invention provides a target detection method based on two-dimensional sliding window robust space-time self-adaptive processing, which can increase the accuracy of target detection. The target detection method comprises the steps of : step 1, receiving echo data through an airborne radar; step 2, carrying out space-time sliding window processing on the echo data; step 3, establishing a target apparent steering vector error boundary matrix; step 4, calculating an autocorrelation matrix of projectional component of the echo data after space-time sliding window processing on an orthogonal complementary space of a subspace formed by the target; step 5, calculating a correlation matrix after diagonal loading of the autocorrelation matrix; step 6, solving a weight vector; step 7, judging whether a module value of a column vector obtained through multiplying a conjugate transpose matrix of the boundary matrix by the weight vector is less than 1, if so, increasing loading factors and then executing step 5 and step 6, otherwise, regarding the weight vector as the optimal weight vector; and step 8, constructing a filter by utilizing the optimal weight vector so as to filter the echo data after space-time sliding window processing, and acquiring target echo data.

The invention discloses a pitch angle control method and a pitch angle controller of a wind generating set. Aerodynamics analysis is carried out on dynamic properties of the wind generating set, a practical dynamical model is set up; according to the practical dynamical model, a reference model with ideal dynamic properties is generated, input of the reference model is the pitch angle and output of the reference model is the rotation speed of a generator; according to the reference model, state predicting is carried on the wind generating set, the pitch angle control law of the wind generating set is determined and the pitch angle control law is used for adjusting the pitch angle of the wind generating unit. Thus, when the practical generator revolution speed output by the wind generating set exceeds the rated generating set revolution speed, the practical generator revolution speed output by the wind generating set is adjusted to be the rated generator revolution speed. Therefore, when the wind speed changes, the generator revolution speed can be well maintained around the rated revolution speed, errors of mean square are small, parameters can be conveniently adjusted, the fluctuation of the revolution speed of the generator is little and control precision and reliability are high.

The invention discloses a machine self-learning construction knowledge atlas training method based on a neural network. The method comprises the following steps of acquiring a statement based on a natural situation which is sent by a user, using a voice noise reduction algorithm to carry out filtering noise reduction on an input statement, and determining a matched feedback statement; if the matched feedback statement does not exist, according to a neural network dialogue model, giving an answer of the statement sent by the user, which includes the following steps of constructing a coding layer of a user sending statement model to be a first neural network; analyzing a user sending statement in the first neural network and acquiring a first intermediate vector used for expressing a user sending statement meaning; and constructing a decoding layer of a dialogue generation model to be a second neural network, analyzing the intermediate vector in the second neural network, and acquiring a vector group used for expressing a statement answer. In the invention, a threshold voice noise reduction algorithm is used to acquire a small mean square error and a signal to noise ratio of a reconstruction voice signal is increased.