41 results about "Normalized mean square error" patented technology

The invention discloses a method for estimating the signal-to-noise ratio of time-frequency overlapped signals in cognitive radio. The method includes the following steps that: a normalized higher-order cumulant equation is constructed according to the normalized higher-order cumulant of received signals; all component signal modulation type combinations are traversed, the power of component signals is calculated through the normalized higher-order cumulant equation, and whether the modulation type combinations are correct is judged; and correct component signal modulation type combinations and power are obtained, the noise power of the time-frequency overlapped signals is calculated, so that the signal-to-noise ratio of the time-frequency overlapped signals in the underlay cognitive radio can be estimated. According to method, the normalized mean square error of the estimation of the signal-to-noise ratio is smaller than 0.2 under a high spectrum overlap rate when a signal-to-noise ratio is 0dB. The method has excellent performance in the estimation of the signal-to-noise ratio of the time-frequency overlapped signals in the underlay cognitive radio. With the method of the invention adopted, the measurement of interference temperature can be facilitated, primary users and secondary users can coexist under the interference temperature, and therefore, spectrum efficiency can be improved.

The invention relates to the field of cognitive radio communication and provides a multi-layer distributed combined spectrum sensing algorithm based on a Dirichlet process so as to realize dynamic spectrum sensing. The sensing data acquired by secondary users in a plurality of hierarchical centers is fused to search the optimized sensing information. The automatic data packet is realized by employing the Dirichlet process, a shared hyper-parameter and a corresponding divergent probability in each packet are estimated by a bayesian model, the hyper-parameter is acquired by employing a standard Viterbi algorithm, and the hyper-parameter is compared with a decision threshold value to acquire a final spectrum decision result so as to determine whether a channel is available. Due to the design, the space diversity information of the compressed sensing data is fully considered, and the uncertainty of an individual secondary user on the compressed sensing data is reduced, so that the normalized mean squared error performance is high, the compressed sensing data information in each hierarchical center can be effectively obtained through the algorithm, high accurate detection probability and low false alarm probability are acquired, and the spectrum sensing performance is improved.

The invention discloses a distributed compressive sensing (DCS) improved sparsity adaptive matching pursuit (DSC-Improved Sparsity Adaptive Matching Pursuit, DCS-IMSAMP) reestablishment method. The estimation precision is improved by utilizing the joint sparsity of the signal and importing a dynamic threshold on the basis of the existing DSC sparsity adaptive matching pursuit (DCS-SAMP) algorithm;and the running time is saved by combining the clipping technology and the variable step length. The adaptivity can be ensured in the reestablishment process by using the algorithm, and the lower normalized mean square error (NMSE) and faster running speed can be acquired. The algorithm disclosed by the invention is applied to a channel estimation problem; compared with existing other algorithms,the excellent channel estimation effect can be obtained.

The invention discloses a combined cooperative multi-satellite weak echo signal time delay and doppler frequency shift estimation method. According to the method, multiple nonstop wave signals in a reference channel are separated; adaptive inhibition for nonstop wave signals and multi-path signals in an echo channel is carried out; fourth-order cyclic cumulant mutual fuzzy function processing based on four weighted fractional Fourier transform for the signals in the echo channel and the different nonstop wave signals is respectively carried out to acquire multiple characteristic vectors; after spectral peak extraction for fuzzy function peak values is carried out, corresponding coordinates are time delay and doppler frequency shift estimates; the multiple sets of time delay and doppler frequency shift estimates are converted into distances from targets to receivers and speeds, data fusion is further carried out, the distances and the speed values are converted into multiple sets of time delay and doppler frequency shift estimates after data fusion, and thereby time delay and doppler frequency shift of cooperative multi-satellite weak echo signals can be estimated; when a signal to noise ratio is greater than 10dB, a normalization mean square error of signal to noise ratio estimation is smaller than 1.

The invention discloses a hyperspectral unmixing compressive sensing method based on three-dimensional total variation sparse prior. The hyperspectral unmixing compressive sensing method is used for solving the technical problem that an existing hyperspectral image compressive sensing algorithm in combination with spectrum unmixing is low in precision. According to the technical scheme, a random observation matrix is adopted for extracting a small number of samples from original data as compression data. In the reconstruction process, according to an unmixing compressive sensing model, appropriate spectrums are selected from a spectrum library as an end member matrix in the model, then the three-dimensional total variation sparse prior of an abundance value matrix is introduced, and the abundance value matrix is accurately solved through solving a limited linear optimization problem. Finally, a linear mixing model is used for reconstructing the original data. When the compression ratio of urban data shot through a HYICE satellite is 1:20, the normalize mean squared error (NMSE) is smaller than 0.09, when the compression ratio is 1:10,the NMSE is smaller than 0.08, and compared with an existing compressive sensing algorithm, precision is promoted by more than 10%.

The invention relates to a method for predicating long correlation sequences by utilizing a short correlation model. Aiming at self-similarity network flow, the invention provides an ARMA (autoregressive moving average model) self-similarity sequence predicating method based on EMD (empirical mode decomposition). The method comprises the following steps of: firstly decomposing the self-similarity network flow into a plurality of IMFs (Intrinsic Mode Functions) by adopting the EMD method, wherein due to the narrow-band characteristic of the IMF, the IMF is provided to be a short correlation sequence, so that the problem of modeling predication of the long correction sequences is converted into the modeling and predicating for the plurality of short correlation sequences, and the complexity of the model is effectively reduced; secondly predicating the decomposed IMF sequences by utilizing excellent short correlation modeling predication capacity of an ARMA model; and finally providing a method for improving the predication precision of the model, so as to effectively reducing the normalization error of mean square of the predication result. The method provided by the technical scheme of the invention has the advantages of high predication precision and low complexity, and the predication precision of self-similarity flow is higher than that of a neural network model.

The invention belongs to the technical field of non-stationary signal modulation and analysis, and discloses an LFM signal modulation parameter estimation method under alpha stable distribution noise.The method comprises the following steps of carrying out generalized extension linear chirplet transform on a received LFM signal to obtain a time-frequency analysis image; carrying out Radon transform on the time-frequency analysis image, calculating a maximum value of the time-frequency analysis image, and estimating an angle estimation frequency modulation slope corresponding to the maximum value; and constructing a demodulation reference signal by utilizing the frequency modulation slope, multiplying the demodulation reference signal by an original signal to obtain a demodulation signal,carrying out generalized Fourier transform on the demodulation signal, and estimating an initial frequency by utilizing the position of the maximum value. When a generalized signal-to-noise ratio is larger than 0dB, a normalized mean square error of the frequency modulation slope estimation of the LFM signal is smaller than -33dB; and when the generalized signal-to-noise ratio is larger than -6dB,the normalized mean square error of the initial frequency estimation of the LFM signal is less than or equal to -22.4dB.

The invention relates to a band-limited system timing recovery method based on p-moment. In a feedback phase-locked loop, the pth power of a signal module value is adopted for calculating a timing-error detection output value, and p>0 and p is a variable parameter. The method of the invention can be applied to a timing recovery loop, better residual timing error jitter performance can be acquired, and in an AWGN channel, normalized mean square error performance through timing recovery is close to a theoretical Cramer-Rao bound. The method of the invention can be applied to a band-limited communication system with a small roll-off factor, and can be applied to a satellite communication system which needs to improve the spectrum utilization ratio.

The invention discloses a millimeter wave MIMO channel estimation method based on a MUSIC algorithm and precoding, and the method comprehensively considers the influence of azimuth angle and pitch angle parameters, employs an innovative scheme, and employs a classical MUSIC method to estimate the CSI in an L-shaped array and a UPA. Under the same condition, the channel estimation performance basedon the L-shaped array is superior to that of the UPA. Because there are few methods for researching two-dimensional channel estimation, a channel estimation scheme based on beam training and a channel estimation scheme based on OMP are adopted in the ULA system to carry out performance comparison. Due to the fact that estimation errors of the azimuth angle and the elevation angle can cause normalization mean square errors, under the same condition, the NMSE performance of the UPAs should be higher than that of the ULA. Simulation results show that the proposed method can effectively estimatethe channel state information in the two arrays, even better than the prior art.

The invention relates to a channel estimation algorithm based on an improved GAN (Generative Adversarial Network) in a large-scale MIMO (Multiple Input Multiple Output) system, and provides a channel estimation method adopting an improved GAN (Generative Adversarial Network) in order to improve the performance when channel estimation based on deep learning is carried out in a one-bit uplink multi-user large-scale MIMO system. In the method, a random quantization method is introduced to improve the input of the GAN network, so that the input data is more real; penalty terms are respectively introduced into a generator and a discriminator to generate a new optimization objective function, so that the network optimization direction is correct, and a network structure is determined through model simulation. The GAN network learns non-trivial mapping from quantitative measurement values to channels by using priori channel estimation observation values; and adversarial training is carried out on the generator and the discriminator to predict a more real channel. A numerical simulation result shows that the method obviously improves the channel estimation accuracy of the large-scale multi-input and multi-output system from the angle of a normalized mean square error (NMSE), and the channel estimation accuracy of the large-scale multi-input and multi-output system is improved by the aid of the method in the aspect of the NMSE (Normalized Mean Squared Error) of the large-scale multi-input and multi-output system.

The invention discloses a short-wave digital pre-distortion method based on a non-uniform quantization lookup table, and the method comprises the steps: selecting a pre-distortion structure, collecting a current pre-distortion signal and a feedback signal, and carrying out the synchronous alignment of the currently collected signals; calculating a normalized mean square error of the current inputsignal and the pre-distortion output signal; judging whether the currently calculated normalized mean square error is greater than a preset target value or not, if so, calculating a pre-distortion coefficient by adopting a least square method, updating the lookup table according to the pre-distortion coefficient, and performing pre-distortion on the next round of pre-distortion by adopting the updated lookup table; otherwise, adopting the current lookup table to perform pre-distortion in the next round. According to the method, the memory polynomial model of the non-uniform quantization tableindex is adopted, so that the table index precision is improved, and the performance of a digital pre-distortion system is further improved.

The invention provides an orthogonal signal division multiplexing equalization method based on diagonal block strip matrix enhancement. The method includes: adopting a time-varying channel OSDM systemmodel to obtain a received signal; completing DBB approximation of a composite channel matrix in the received signal based on Doppler expansion of band limit; performing enhancement processing on a DBB approximate structure in the received signal based on a time domain receiving window function; and desiging a low-complexity OSDM equalization algorithm based on a DBB matrix enhancement method ina received signal. According to the method, the normalized mean square error performance of the system is improved, the error code performance of the system is improved, the performance loss caused bythe Doppler effect is effectively reduced, the performance of the communication system is remarkably improved, the calculated amount is greatly reduced, and the method has a good application prospect.

The invention relates to a method for processing a vibration signal on the surface of an oil tank of a power transformer. The method comprises the steps of a) firstly collecting the vibration signal on the surface of the oil tank of the power transformer; b) selecting vibrations x1, j(n) and x2 at a measuring point j on the wall of the oil tank under an approximate voltage condition and differentload conditions, and taking the j(n) as a signal separation object; c) using a time-frequency ratio hybrid blind source separation algorithm for the signal, estimating a hybrid matrix, and obtaining aseparated signal; and d) calculating the separated signal to be compared with the vibration in a no-load state of the measuring point, and calculating a normalized mean square error (NMSE) between the separated signal and the vibration of the measuring point in the no-load state. According to the method, the initial judgment can be provided for equipment maintenance, so that the occurrence rate of major accidents is reduced, the maintenance quantity and the maintenance cost of equipment are reduced, and the normal operation of the power transformer is ensured.