The invention discloses an energy-efficient-based detecting method of a multi-task Bayes compressed sensing broadband spectrum Specific to the sparsity of a main user signal at a empty frequency domain in the course of recognizing a wireless network, the detecting method obtains the estimated sparse signal through layering Bayes analysis hierarchical prior model on the basis of a signal reconstruction of Bayes compressed sensing By applying a space correlation of a multi-cognitive user sensing signal, the sparse signal reconstruction and broadband compressed spectrum detection under the transmission conditions of multiple users and tasks is realized A cognitive base station carries out the multi-task Bayes compressed sensing parameter estimation on the basis of expectation maximization algorithm and related vector machine model Under the condition of satisfying a certain detecting performance and Bayes compressed sensing reconstruction mean square error, the node sensing energy consumption is minimized The energy-efficient-based detecting method effectively guarantees the effectiveness of node energy of multi-node and multi-task broadband spectrum detection

Energy-efficient-based detecting method of multi-task Bayes compressed sensing broadband spectrum

The invention discloses a threshold shrinkage iterative differential reconstruction method based on a compressed sensed signal, which comprises the steps of: firstly, aiming at a compressed sensed result y=[phi][theta]+[phi]n+w=[phi][theta]+e including a noisy signal, constructing a recovery target function (with reference to the specification) of the compressed sensed signal; then, carrying out solving model conversion on the recovery target function of the compressed sensed signal to obtain a new recovery target function; and finally, carrying out differential reconstruction on the changed recovery target function, and carrying out iteration for K times to obtain an optimal solution of a recovery signal. The threshold shrinkage iterative differential reconstruction method disclosed by the invention has excellent recovery capacity for an ideal noise-free sparse signal subjected to compressed sensing, and also has high robustness on the noisy signal subjected to compressed sensing; moreover, robustness can be further improved by increasing iteration times and the line number of a measurement matrix, so that recovery errors are greatly reduced; and meanwhile, the threshold shrinkage iterative differential reconstruction method disclosed by the invention also has the advantages that a convergence rate is in direct proportion to the iteration times, and a computing speed is relatively high.

Threshold shrinkage iterative differential reconstruction method based on compressed sensed signal

The invention belongs to the voice signal compression field and particularly relates to a voice compression method base on monobit compression perception. The method is advantaged in that, 1, voice signal sampling rate requirements are not high, AD for voice sampling is not a voice signal sampling bottle neck problem. A scene which needs utilizing compression perception for sampling to reduce an AD sampling rate is a high speed sampling scene, voice signal sampling does not belong to high speed sampling, sampling devices and sampling structures of an original system can be unchanged, only software modification is needed, and system reconstruction cost is greatly reduced; and 2, through the popular mp3 technology at present, 10-times compression can be realized, through the voice compression method base on monobit compression perception, 32-times voice compression can be easily realized, and the timbre can further reach standard mp3 timbre substantially. A recording system in the telecommunication field needs large-scale voice data storage, and the voice compression method base on monobit compression perception is quite convenient.

Voice compression method base on monobit compression perception

The invention discloses a generation method of a sensing matrix for signal compressive sensing (CS) The generation method comprises the following steps: firstly, selecting a determinated sensing matrix or generating a random sensing matrix; then performing left multiplication on a specific reversible matrix or right multiplication on a specific reversible diagonal matrix or full permutation of lines/rows of the specific reversible diagonal matrix to obtain a matrix product which is the required novel sensing matrix Based on conventional sensing matrixes, the structures or elements are adjusted to generate the sensing matrix with certain regularity, and meanwhile, the CS attribute is unchanged Particularly for the determinated sensing matrix, the invention provides a very convenient generation or extension new way The sensing matrix with certain regularity on structure or element facilitates improvement of a CS measurement system in the aspects of compressive sampling frequency, signal reconstruction speed, implementation cost and the like

Generation method of sensing matrix for signal compressive sensing

The invention provides a Lamb wave compression sensing method based on an adaptive observation matrix. A sensor array composed of multiple sensors is adhered on the surface of a plate, and the Lamb wave compression sensing method based on the adaptive observation matrix comprises the following steps: a, collecting an original signal of Lamb wave through the sensor array; b, reordering transformation coefficients through discrete cosine transform, and acquiring a sparse coefficient of the original signal; c, constructing the adaptive observation matrix through a number of non-zero values and positions thereof in the sparse coefficient, carrying out projection transformation on the signal to acquire an observation value; and d, constructing a reconstruction algorithm based on the observation value, and acquiring a construction signal. The Lamb wave compression sensing method based on the adaptive observation matrix provided by the invention has the beneficial effects that only a data collection device having a low sampling rate is adopted, so that the requirements on the device is low, and the method is simple and feasible.

Lamb wave compression sensing method based on adaptive observation matrix

The invention provides a method for Bayes compressed sensing signal recovery based on a self-adaptive measurement matrix and relates to the field of the information and communication technology The method aims at solving the problem that an existing compressed sensing signal recovery method is low in accuracy Based on the design of the self-adaptive measurement matrix in compressed sensing and combined with the Bayes compressed sensing algorithm, a design scheme of the compressed sensing method is obtained The method is characterized in that the designed measurement matrix can be generated in a self-adaptive mode according to different signals, the purposes of determinacy and storage of the matrix are both achieved, and combined with the Bayes compressed sensing recovery algorithm of a relevant vector machine, the priority of a layered structure is introduced The design scheme passes simulation verification, it is confirmed that the good signal recovery effect can be obtained, and the error range of recovered signals can be evaluated The method is suitable for wireless signal transmission occasions in the information and communication technology

Method for Bayes compressed sensing signal recovery based on self-adaptive measurement matrix

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Method for Bayes compressed sensing signal recovery based on self-adaptive measurement matrix