46results about "Diversity direction finding" patented technology

The invention belongs to the crossing field of information and ocean science and technology, and discloses a DOA estimation method for a UUV broadside parallel co-prime array. According to the method, firstly, a virtual extension array is generated by utilizing an array auto-covariance and cross-covariance matrix, then, Toeplitz completion is carried out on'holes' in the virtual array through trace norm regularization and nuclear norm regularization constraints, a complete covariance structure is recovered, and finally, an extension matrix is constructed, and rotation invariance of a signal subspace of the extension matrix is utilized, so that the covariance structure of the virtual array is obtained. The pitch angle and the azimuth angle of the target are jointly solved, automatic angle pairing is achieved, and the effectiveness and the reliability of the method are verified through simulation results. According to the method, aiming at the application challenges that the UUV broadside array space is limited and the underwater environment is complex, all virtual array elements of the broadside parallel co-prime array are fully utilized, the array freedom degree and estimation precision are remarkably improved, the target detection capacity of the UUV is effectively enhanced, and the method has high practical engineering application value.

The invention belongs to the technical field of crossing of information and ocean science and technology, and discloses a UUV broadside parallel array covariance weighted fusion DOA estimation method. The method comprises the following steps: firstly, calculating auto-covariance and cross-covariance matrixes of two sub-arrays, carrying out weighted fusion on covariance matrixes of different sources through differential common-array expansion of a virtual aperture, then reconstructing a complete covariance matrix by respectively adopting a convex optimization method of trace regularization and nuclear norm regularization according to the characteristics of the fused matrix, and finally, carrying out weighted fusion on the covariance matrixes of different sources. The pitch angle and the azimuth angle of the target are jointly estimated by performing subspace decomposition on the constructed extension matrix, and the effectiveness of the method is verified by a simulation result. According to the method, the precision of parameter estimation is improved through data fusion, the degree of freedom is remarkably improved under a small number of physical array elements, high-precision azimuth estimation of the UUV on multiple targets in the underwater environment is achieved, the target detection capacity of the UUV is enhanced, and the method has high engineering application value.

The application discloses a typhoon direction estimation method and system based on a single vector hydrophone and a terminal, and relates to the field of typhoon direction estimation.The method comprises the following steps: strictly checking sound pressure and three-directional vibration velocity data collected by the single vector hydrophone to ensure data quality; then, pre-processing the data, including filtering to remove noise interference; then, normalizing the filtered data; then, calculating sound intensity in each direction within a certain azimuth and pitch search range, constructing a sound intensity matrix, and finding the azimuth and pitch corresponding to the maximum sound intensity as the real reference value; dividing the processed data into a training set and a test set according to a certain proportion; constructing a multilayer perception machine model; and through experiments of different learning rates and small batch sizes, optimizing the model, selecting the parameters with the minimum loss to train the best model; finally, using the trained model to predict the test set to obtain the estimated value of the typhoon azimuth and pitch angle.The application effectively improves the accuracy of typhoon direction estimation.

The invention discloses a sparse Bayesian out-of-grid orientation estimation method under non-Gaussian noise, which comprises the following steps of: constructing a hierarchical noise prior model based on Student-t distribution, a double-layer sparse signal prior model based on a binary indication variable and the like, and inferring approximate posteriori distribution of iteratively updating parameters by using Bayesian variation; the noise precision weight is adaptively adjusted according to the reconstructed residual energy so as to suppress impulse noise, and random pseudo peaks are effectively removed through a binary indication variable judgment mechanism. And finally, adaptively correcting the grid offset by using the residual gradient information subjected to anti-noise weighting so as to eliminate the off-grid effect. Compared with the traditional method, the method provided by the invention not only can carry out DOA estimation under non-Gaussian noise, but also has higher DOA estimation precision. The method disclosed by the invention still has relatively high performance under the conditions of few snapshots and low signal-to-noise ratio, so that the method has relatively high application value in actual engineering.

This invention belongs to the field of information and marine technology, and discloses a method for estimating the DOA (Direction of Aperture) of a UUV (Ultra-UV) hull-side array using conjugate enhancement and virtual extension. This method performs conjugate enhancement processing on the received signal from a parallel coprime array, calculates the augmented statistical matrix including autocorrelation, conjugate autocorrelation, cross-correlation, and conjugate cross-correlation, and constructs an extended correlation matrix through weighted combination. A virtual uniform linear array is constructed using the differential co-array characteristics of the coprime array. A redundant integration matrix is ​​designed to remove redundancy from the covariance matrix, obtaining the equivalent signal of the virtual array. Then, the covariance matrix of the virtual array with a Toeplitz structure is reconstructed. Finally, a dual rotation-invariant subspace algorithm is used to achieve high-precision, automatic pairing estimation of the underwater target's azimuth and pitch angles. This invention extends the array aperture, improves the estimation robustness in low signal-to-noise ratio environments, and eliminates the need for spectral peak search, providing technical support for UUV underwater target positioning.