The present invention discloses an estimation method of a quasi-stationary broadband array signal direction of arrival (DOA) based on block sparse Bayesian learning. An intra-frame correlation and aninterframe independence of a quasi-stationary broadband signal frequency spectrum are employed to set a corresponding a block sparse prior distribution model for signals, and a block sparse Bayesian model is employed to perform estimation of sparse signals, so that an estimation result with higher precision is obtained. Array receiving signals are subjected to appropriate framing processing, eachframe of the signal is subjected to Fourier transform, and each block sparse Bayesian model for each signal is established in a frequency domain; under an assumption of each frame of the signal is independent, information of all the frames is combined to establish a total Bayesian model, and hyper-parameter vectors are employed to control a sparsity of all the frames of signals to be reconstructed; and finally, the expectation maximization algorithm (EM) is employed to obtain an iterative update formula of the hyper-parameter vectors. The estimation method of a quasi-stationary broadband arraysignal direction of arrival based on block sparse Bayesian learning fully utilizes a short-time stability feature of quasi-stationary broadband array signals to establish a block sparse model, and therefore a higher DOA estimation precision can be obtained.

Estimation method of quasi-stationary broadband array signal direction of arrival based on block sparse Bayesian learning

The invention discloses a SAR tomographic building height method based on cyclic spectrum estimation. The method comprises steps that an SAR data acquisition step, N-scene SAR data is acquired; a main image selection step, N-scene single view complex data images are acquired through pre-processing the acquired N-scene SAR data at an earlier stage, and a one-scene image is selected as a public main image according to a combined correlation function of a vertical baseline and Doppler centroid frequency difference; a data pre-processing step, the acquired N-scene SAR data is processed to be in a data mode required for SAR tomography; a cyclic spectrum height-direction position estimation step, the position of a building in the height direction is acquired through utilizing cyclic spectrum estimation. The method is advantaged in that signals can be divided into interested signals and noise through utilizing cyclic stationary characteristics of the signals, influence of the noise can be excellently inhibited, and the better result can be acquired even under the low signal to noise ratio condition, and higher precision is acquired compared with a routine spectrum estimation method.

SAR tomographic building height method based on cyclic spectrum estimation

The invention discloses a method for estimating the number of signals in broadband spectrum sensing based on a DPMM, and belongs to the technical field of broadband spectrum sensing. According to theinvention, the problem of low accuracy of estimating the number of signals in the received signal by using the existing method is solved. By selecting the cyclic spectrum of the signal as a characteristic, the cyclic spectrum of the signal is recovered, so that the anti-noise performance of the broadband spectrum sensing method is improved; the extracted cyclic spectrum is modelded into a Gaussianmixture model for signal and noise classification, signal elements are reserved after de-noising, the reserved signals are clustered, and the number of the signals in the received signals is estimated according to the calculated maximum probability. Under the condition of -4dB of the signal to noise ratio, the accuracy of signal number estimation can be improved by about 30% by adopting the method disclosed by the invention, and the anti-noise performance of the method disclosed by the invention is better. The method can be applied to the technical field of broadband spectrum sensing.

Method for estimating number of signals in broadband spectrum sensing based on DPMM

Wind Turbine Fault Diagnosis Based on Cyclostationary Feature Detection from Compressive Samples

The invention discloses a distorted towed array line spectrum feature enhancement method and system The method comprises the following steps: 1, acquiring an observation array signal; 2, roughly estimating a target orientation based on ideal beam forming; 3, detecting L frequencies with the maximum power of the target signal, and calculating a relative delay inequality vector gamma l of the strong line spectrum array; 4, fitting the L strong line spectrum array relative time delay difference vectors gamma l into L curves; 5, classifying the L curves by using a Frechet distance-based hierarchical clustering algorithm to obtain a classification cluster set; 6, selecting a class with most line spectrums in a classification result, and obtaining array relative delay inequality vector estimation gamma' for eliminating the influence of strong interference; 7, obtaining a target tracking beam based on the estimated time delay, and obtaining fidelity-enhanced line spectrum features through Fourier transform According to the method, the influence of a strong interference line spectrum on time delay estimation is eliminated through fitting-hierarchical clustering, a target radiation noisetracking beam is obtained based on the estimated time delay, and line spectrum feature enhancement based on a distorted towed array is realized in a multi-target strong interference underwater acoustic environment

Distorted towed array line spectrum feature enhancement method and system

Cyclic spectrum is a basic tool to study the cyclostationary of signal. It has the advantages of high resolution, anti-interference ability and low channel environment sensitivity. These properties are beneficial to process digital modulated signals. There are two time-smoothed algorithms: the FFT Accumulation Method (FAM) and the Strip Spectral Correlation Algorithm (SSCA). SSCA is suitable for fast estimation and engineering realizable, while FAM has shortcoming of complex computation. But both methods need high sampling rate that requires high performance analog-to-digital converter. To improve the efficiency of cyclic spectrum estimation, we propose CS-SSCA algorithm in the framework of Compressive Sensing (CS). The cyclic spectrum can be recovered via compressed samples by SSCA. The theoretical analysis and simulation results prove that the improved algorithm displays better performance than CS-FAM under the same conditions.

Cyclic Spectrum Estimation Under Compressive Sensing by the Strip Spectral Correlation Algorithm

The present invention provides a target orientation tracking method based on the Bayesian method, belonging to the array signal processing field. The problems are solved that the DOA (direction of arrival) tracking method based on spatial category is poor in precision and known conditions required by the particle filter DOA tracking method are too many in the condition of a low signal to noise ratio. Through adoption of the Taylor expansion formula, the target orientation tracking method based on the Bayesian method allows the DOA tracking in an angle slow varying mode to be modeled to a dynamic model, converts the angle tracking to the parameter estimation problem in a probability model based on the Bayesian theory, and performs correction of the information of the previous moment through adoption of the EM algorithm according to the signal arrival angle, the signal power and the estimation value of the noise power at the previous moment so as to realize the tracking of the signal arrival angle. The target orientation tracking method based on Bayesian method is mainly used for tracking of the target orientation.

Target orientation tracking method based on Bayesian method

The invention relates to a signal estimation method in a non-reconstruction framework, belonging to the field of cognitive radio parameter identification and estimation In order to solve the problems of slow reconstruction speed and poor accuracy in using an existing reconstruction algorithm to restore a signal, the method comprises a step of establishing an association between a sample signal cyclic spectrum vector Sx(c) and a sample signal cyclic autocorrelation vector rx, a step of establishing an association between a sampling signal compression measurement value autocorrelation vector rz and the sample signal cyclic autocorrelation vector rx, a step of establishing the relation between the sampling signal compression measurement value autocorrelation vector rz and the sample signal cyclic spectrum vector Sx(c), a step of deleting the redundant elements in the sample signal cyclic spectrum vector Sx(c), and obtaining a simplified sample signal cyclic spectrum vector Sxs(c), a step of reconstructing the simplified sample signal cyclic spectrum vector Sxs(c) by using the sampling signal compression measurement value autocorrelation vector rz and an orthogonal matching tracking algorithm based on block sparse, and obtaining an original signal cyclic spectrum, and a step of extracting the parameter information of the original signal according to the original signal cyclic spectrum, and a step of extracting the parameter information of the original signal according to the original signal cyclic spectrum The method is mainly used for extracting the signal parameter information

Signal estimation method in non-reconstruction framework

Modulation recognition of sparse multiband signals is a key technology for intelligent signal processing. However, the existing method suffers from high sampling rate and poor anti-noise performance. Therefore, compressed sensing and cyclic spectral density are combined to cope with the shortcomings of existing methods followed by the multi-layer perceptron (MLP) to recognize modulation mode of signal. Some simulations are carried out, and the simulation results show that the proposed algorithm is correct and effective.

CS-Based Modulation Recognition of Sparse Multiband Signals Exploiting Cyclic Spectral Density and MLP

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Papers

Cyclic Spectrum Estimation Under Compressive Sensing by the Strip Spectral Correlation Algorithm

Target orientation tracking method based on Bayesian method

Signal estimation method in non-reconstruction framework

CS-Based Modulation Recognition of Sparse Multiband Signals Exploiting Cyclic Spectral Density and MLP