507 results about "Signal subspace" patented technology

The invention discloses a method for estimating target arrival angle of a multiple input multiple output (MIMO) radar, which mainly solves the problem of large signal processing capacity in the target positioning process of MIMI radar. The method comprises the following steps of: 1) obtaining a virtual array of echo of each receiving antenna by a matched filter bank; 2) constructing a data conversion matrix and a dimension reduction array according to position of transmitting and receiving array; (3) reducing dimension of the virtual array data by the dimension reduction array to obtain an effective array after dimension reduction; 4) constructing two sub-arrays by rotational variance of effective array, and deriving covariance matrix of data; 5) decomposing eigenvalue of covariance matrix to obtain signal sub-spaces corresponding to two sub-arrays; 6) deriving rotational invariant relationship matrix by least square method to obtain arrival angle of target. The dimension reduction matrix form constructed by the method has versatility; the computation quantity is reduced by the dimension reduction of data and the ESPRIT (estimating signal parameter via rotational invariance techniques) algorithm; the computation speed of MIMO radar is increased; and the real-time signal processing of the MIMO radar is made easier.

The invention discloses a multi-sound-source locating method based on a spherical microphone array, and the method comprises the following steps of firstly conducting spherical harmonics decomposition for high-order sound fields collected by a spherical microphone array, and building a noise-contained sound source signal model received by a spherical harmonics domain array; then expressing a covariance matrix of data received by the array; classifying the covariance matrix according to a subspace decomposition method to obtain two mutually-orthogonal signal subspace and noise subspace; utilizing the orthogonality of the signal subspace and the noise subspace to define a guide vector of the signal subspace, and extracting one characteristic vector of the noise subspace to build a space azimuth spectrum; and finally searching a spectrum peak position of an azimuth spectrum function, and determining a space azimuth of a sound source. The method utilizes a three-dimensional space rotary symmetric structure of the spherical microphone array to adequately sample the sound field, so that the operation quantity is remarkably reduced through the high-resolution spectrum estimation and dimensional-reduced noise subspace, the sound source azimuth is accurately estimated, and the method can be widely applied to the fields such as a voice signal processing field.

The invention provides a method for estimating parameters of a space stretching electromagnetic vector sensor array. The method comprises the steps of receiving K unrelated incoming signals through a receiving array, and constructing guide vectors of the incoming signals corresponding to the array; expressing the guide vectors of the incoming signals as a product of a spatial domain function array and a polarizational domain function vector; computing a covariance matrix of the received data; analyzing features of the covariance matrix of the received data to obtain signal subspace and noise subspace; constructing a multi-signal classified MUSIC spatial-polarizational domain combination zero spectrum function, and maximizing the spatial-polarizational domain combination zero spectrum function; performing MUSIC dimension reduction process to separate a spatial domain spectrum and a polarizational domain spectrum by means of the self-conjugate moment Rayleigh-Ritz entropy theorem, performing traversal search within value ranges of variables and estimating signal parameters. By means of the method, four-dimensional MUSIC search is transformed into two-dimensional spatial domain search and two-dimensional polarizational domain search, and therefore, calculated quantity is decreased.

The invention discloses a characteristic space-based backward and forward adaptive wave beam forming method, and relates to the technical field of medical ultrasonic imaging. The method comprises the following steps of: performing focusing delay processing and backward and forward smoothing on a plurality of paths of sampled signals of a received array to obtain a sample covariance matrix estimate; performing diagonal loading on the sample covariance matrix estimate and then combining with a direction vector to calculate an adaptive wave beam forming weight; performing characteristic decomposition on the backward and forward covariance matrix estimate after the diagonal loading to construct a signal subspace; projecting the adaptive wave beam forming weight into the signal subspace to obtain a new adaptive wave beam forming weight; and finally, performing weighted summation on a plurality of paths of data subjected to the backward and forward smoothing by the new adaptive wave beam forming weight so as to obtain a path of adaptive wave beam signal. By using the method, the problems of improving the image resolution and contrast, being sensitive to the direction error and the like existing in the conventional adaptive wave beam forming algorithm are solved, and the overall quality of the ultrasonic imaging is comprehensively improved.

The invention provides a multi-target positioning method of a bistatic multi-input multi-output radar, comprising the following steps of: (1) transmitting mutually orthogonal phase-coded signals by M transmitting array elements, and receiving the phase-coded signals by N receiving array elements, wherein the distances of the M transmitting array elements and the N receiving array elements are all of half wavelengths; (2) carrying out matched filtering on the received phase-coded signals by a matched filter of a receiver of each receiving array element; (3) carrying out multistage Wiener filtering on a matched signal data covariance matrix space, and carrying out forward recursion to obtain a signal subspace; (4) carrying out high-resolution DOA (Direction of Arrival) estimation by using an ESPRIT algorithm, wherein a pairing algorithm is used for carrying out the automatic pairing on two-dimensional parameters; and (5) realizing multi-target positioning according to cross points at two angles so as to obtain the positions of space targets. The multi-target positioning method provided by the invention has the advantages of low computation complexity, high computation speed, high estimation accuracy and can be used for positioning the sea-surface or low-altitude targets during tracking and guidance.

The invention discloses a single-snapshot data-based coherent signal DOA estimating method and relates to the technical field of array antenna systems or methods capable of indicating different signal directions. The method comprises rearranging the single-snapshot received data of an array to obtain two pseudo-covariance matrixes, and then through two pseudo-covariance matrixes, expanding the covariance matrix of a subspace restructuring algorithm; performing singular value decomposition on the new pseudo-covariance matrixes to obtain a signal subspace and a noise subspace, and performing DOA estimation on incoming coherent wave signals through a MUSIC spectrum estimating method. The single-snapshot data-based coherent signal DOA estimating method can eliminate coherence among source signals under single-snapshot conditions and meanwhile further improve the DOA estimating precision. The single-snapshot data-based coherent signal DOA estimating method is mainly applied to rapid estimation of incoming directions of coherent signals under the single-snapshot conditions.

The invention provides a broadband signal direction of arrival (DOA) estimation method based on threshold detection. The method comprises the following steps: by means of energy threshold detection, abandoning subbands the energy of which is smaller than the threshold, and carrying out narrow-band spatial spectrum treatment on subbands the energy of which is larger than the threshold; and then solving DOA angles by adopting a spectrum weighing method. By means of the method, accurate estimation results can be obtained for incoherent signals; and in terms of coherent signals, the estimation results obtained by the method is taken as estimated angles for a Coherent Signal-Subspace Method (CSM), and then accurate DOA information is solved by utilizing a focus transformation method. Compared with the traditional method, the method has the advantages that the theory is simple, the estimation is accurate, the computation amount is less, and the hardware cost can be greatly lowered in practical application.

The invention discloses a bidimensional wave arrival direction estimation method based on a non-concentric electromagnetic vector array radar. The bidimensional wave arrival direction estimation method mainly solves the problems of serious cross coupling and low estimated accuracy of a polarization array radar in the process of target positioning and tracking. The method includes a first step of building receiving data of a sparse uniform non-concentric electromagnetic vector sensor array, a second step of carrying out eigenvalue decomposition to the received data to obtain a signal subspace, a third step of obtaining dim direction cosine precision estimation according to signal subspace tectonic rotation invariant relationship equation, and through utilization of the least square algorithm, a fourth step of obtaining non-dim-direction cosine rough estimation through utilization of the signal subspace, and a fifth step of combining the dim direction cosine precision estimation and the non-dim-direction cosine rough estimation, the bidimensional wave arrival direction estimation can be obtained. The bidimensional wave arrival direction estimation method effectively expands array physical apertures on the condition of not adding array elements and complexity of hardware, enables the bidimensional wave arrival direction estimation accuracy to be greatly improved, and can be applied to accurate positioning and tracking of the radar to targets.

The invention discloses an adaptive high-precision phase estimation method for an interferometric SAR, comprising the following steps of: structuring optimum weight vectors in combination with a Wiener filter theory, performing an eigen decomposition on an optimum covariance matrix composed of the optimum weight vectors to obtain a signal subspace and a noise subspace, adequately utilizing a corresponding pixel pair and the coherent information of the neighboring pixels thereof to structure a space spectrum function according to the orthogonality of the signal subspace and the noise subspace in a MUSIC (multiple signal classification) algorithm, and precisely estimating the interferometric phase between the corresponding pixels via a spectral peak searching. The optimum weight is obtained by only a Wiener filter without the need to determine a registration error and the direction thereof, thereby solving the problem of large computational burden in the traditional InSAR (interferometric synthetic aperture radar) interferometric phase estimation. The adaptive high-precision phase estimation method for the interferometric SAR disclosed by the invention is adaptive to the field of accurate surface parameter inversion of InSAR complex scene and the like.

The invention relates to a mechanical vibration fault characteristic time domain blind extraction method, and belongs to the technical field of mechanical equipment status monitor and fault diagnosis. The mechanical vibration fault characteristic time domain blind extraction method includes: firstly, expanding a vibration observation signal into a high dimension signal subspace; then, obtaining a low dimension signal; afterwards, performing FastICA independent component analysis, calculating normalization kurtosis of all independent components, figuring out a component signal corresponding to the minimum normalization kurtosis, and using an orthogonal matching pursuit algorithm to reconstitute periodic signals; subsequently, removing the reconstituted periodic signal from each independent component, and then using an improved KL distance algorithm to calculate a distance matrix among the independent components after the periodic signals are removed from the independent components, and performing dynamic particle swarm clustering so as to obtain an estimation signal; finally, analyzing an envelope demodulation spectrum of the estimation signal, and performing fault diagnosis. The mechanical vibration fault characteristic time domain blind extraction method is suitable for processing a long convolution data problem, can effectively reduce influences from periodic ingredients on a blind separation result, and simultaneously can solve blind separation result order uncertainty problems, and finally achieves bearing fault characteristic extraction.

The invention discloses an array antenna based multiple local discharge point positioning method for a local discharge detection device. The array antenna based multiple local discharge point positioning method comprises the steps of establishing a position equation related to local discharge points according to time difference of local discharge signals received by detection antennae and calculating direction angles of the local discharge points; enabling a master control computer to control rotation of cloud platforms of the detection antennae according to the input direction angles till the detection antennae receives the local discharge signals at the same moment; controlling shooting of a video camera, accordingly obtaining images of the local discharge signals and achieving positioning of the local discharge signals. The array antenna based multiple local discharge point positioning method for the local discharge detection device adopts a canonical correlation technology to estimate the number of local discharge sources; adopting a multiple signal classifying algorithm and utilizing covariance matrixes of the received data to separate out local discharge spaces and noise sub-spaces, utilizing direction vectors of the local discharge signals and orthogonality of the noise sub-spaces to form a space angle spectrum, wherein the spectrum peak positions shows the orientations of target signals.

The invention discloses a passive time reversion reverberation inhibition method based on forward predication, comprising: first, processing submarine reverberation and target echo via modeling and multiplying window operations, obtaining a signal sub space corresponding to the target distance, using forward predication to evaluate a submarine reverberation sub space corresponding to the target distance, deriving a group of optimal weighted vectors w of SRA, using the optimal weighted vectors w to process weighted sum on the echo signal x(r, omega) received by SRA, thereby restraining the reverberation component of the echo signal and improving the target echo signal component of the echo signal most, to further improve the echo signal mixed ratio. The invention can effectively restrain reverberation, keep or enhance target echo, to effectively improve echo signal mixed ratio, improve the target detection property of active sonar and improve practicality.

The invention discloses an ESPRIT algorithm based short-range clutter suppression method for airborne radar and mainly aims at solving the problem that the clutter suppression performance of the STAP processing for non-side looking arrays of the airborne radar is reduced due to the short-range clutter. The method comprises the steps of (1), calculating a pitch covariance array by using pitch snapshot vectors corresponding to different direction array elements and different pulses of non-side looking clutter data; (2), performing characteristic decomposition on the pitch covariance array and separating characteristic vector arrays corresponding to signal subspaces; (3), solving guide vectors corresponding to the short-range and long-range clutter of the characteristic vector arrays by using the least square method; (4), restraining pitch self0adaptive weights through the short-range and long-range guide vectors and performing pitch filtering processing; (5), performing STAP processing on the clutter data after filtering processing. According to the short-range clutter suppression method, short-range clutter can be suppressed effectively, the space-time self-adaptive processing performance is improved, and the method is applicable to suppression of the non-side looking array short-range clutter for the airborne radar.

The invention discloses a microphone array postfiltering sound enhancement method based on multi-models and hearing characteristic, aiming at two important factors influencing the postfiltering sound enhancement performance of a microphone array, i.e. accurate estimation for signal parameters and suitable compromise between increasing noise reduction performance and reducing voice distortion. Thescheme of the invention comprises the following steps of carrying out time domain alignment on signals collected by the microphone array, and carrying out short-time Fourier transform and characteristic value analysis based of power spectrum; determining the dimensionality of a signal subspace through the existence probability of target voice signal in maximation noise-carried voice signals; self-adaptively selecting a distribution model of a noise power spectrum in the noise-carried voice signals; estimating noise power spectrum by utilizing a conditional probability; estimating an auditory masking threshold value based on the signal subspace; and estimating a postfilter by combining Lagrange multipliers according to the auditory sensing characteristics.

The invention discloses a partial sparse L array and a two-dimensional DOA estimation method thereof. Two subarrays for the L array, the array element interval of the first subarray is half of a wavelength, and the array element interval of the second subarray is n times of the wavelength; and an auxiliary array element distanced half of the wavelength away from a reference array element is arranged on the second subarray. During DOA estimation processing, the subarrays are respectively placed on an x axis and a z axis, and by use of a feature that a cross-correlation matrix is not affected by noise, cross correlation of receiving data is solved, and a signal subspace is extracted; by use of translation invariability of a ULA and the signal subspace, a rotation matrix of a z-axis array flow type matrix is solved, possible pitch angle estimation values are obtained by performing feature value decomposition on the rotation matrix, and then defuzzificaiton processing is performed on estimation results based on the auxiliary array element; and then based on this, a source waveform and an x-axis array flow type matrix are estimated, and corresponding azimuths are solved. The partial sparse L array and the two-dimensional DOA estimation method thereof are applied to radar, sonar and the like, and have the advantages of low realization cost, low computation quantity and high direction finding precision.

The invention discloses a low-order recurrence least mean square error estimation for an MIMO-OPDM channel, which relates to the wireless transmission technical field. After a pilot frequency is used to insert in to obtain a recurrence least square estimation of a time-varying channel fading, the channel fading is decomposed into a signal subspace and a noise subspace by adopting a subspace tracking method of being capable of tracking singular values and singular vectors under the non-stationary complicated noise, then an order-reduction is made according to the quantity of main singular values to obtain an auto-correlation matrix of the channel fading, and a least mean square error estimation with higher precision is obtained through the recurrence. The invention has the characteristics of having computation complexity of decreasing algorithm, higher estimation accuracy as well as good robustness and applicability, and being capable of providing channel estimation and self-adaptive equalization proposals of systems such as third generation (3G) cell mobile communication, beyond third generation (B3G) cell mobile communication, fourth generation (4G) cell mobile communication and digital TVs, wireless local area networks (WLAN), wireless wide area networks (WWAN) and so on, with an important theoretical evidence and a concrete realization method and so on.

The invention discloses a MIMO (Multiple-input Multiple-output) radar angle estimation algorithm based on tensor subspace and rotation invariant. Through building a three-order tensor model for receiving data, a higher-order covariance tensor model for tensor data is further built, and the internal correlation structure of array signals is thoroughly exploited; HOSVD (Higher Order Singular Value Decomposition) is then carried out on the tensor data, new signal subspace is built, and high-precision noise subspace is thus acquired; and finally, by using the rotation invariant of array data, a DOD (Direction of Departure) and a DOA (Direction of Arrival) which are paired are acquired through a constrained optimization method and a lagrangian multiplier method, and further pairing calculation is not needed. According to the MIMO radar angle estimation algorithm of the invention, by using the internal correlation structure of the received signals, the angle estimation precision is higher, the target DOD and the target DOA which are more accurate can be acquired, a more reasonable reference is provided for further processing on a detected target, spectral peak search is not needed, and the computation complexity is low.

The invention provides an array element defect MIMO radar angle estimation method based on signal subspace reconstruction, which comprises the following steps: step 1, performing characteristic decomposition on a covariance matrix of an array element defect bistatic MIMO radar to obtain a signal subspace matrix, so as to reduce the dimension of the data matrix to be recovered, every M rows of dataform each signal subspace block matrix from top to bottom in the signal subspace matrix, and N signal subspace block matrices are formed in total, wherein M and N are the number of transmitting arrayelements and the number of receiving array elements respectively; step 2, according to the correlation among the signal subspace block matrixes, forming a low-rank block Hankel matrix from all the signal subspace block matrixes, and reconstructing the low-rank block Hankel matrix through an indefinite augmented Lagrangian multiplier method to recover the missing data in the block Hankel matrix, so as to obtain a complete signal subspace matrix; step 3, estimating the target angle by using an ESPRIT algorithm according to the complete signal subspace matrix.

The invention provides a low-complexity receiving and transmitting angle joint estimation method for non-circular signal double-base MIMO radar. Firstly, observation data are extended according to the non-circular characteristic of received signals of the MIMO radar, and a virtual array element of the MIMO radar is amplified by one time. Secondarily, the observation data are divided into two orthogonal portions according to the front-to-back recursion formula of multi-stage Wiener filtering, the two orthogonal portions serve as desired signals of a next stage of multi-stage Wiener filtering and observation data vectors respectively, and then a new signal subspace and a new noise space are formed through pre-filtering matrixes. Finally, the target transmission angle DOD is estimated according to the extended ESPRIT, the target reception angle DOA is estimated according to the estimated value of the DOD and the non-circular ROOTMUSIC technology, and two-dimension parameters of the DOD and two-dimension parameters of the DOA are automatically paired to obtain the target transmission angles, relative to the transmission end, of many targets and target reception angles, relative to the reception end, of the targets. According to the method, the virtual aperture of the MIMO radar is increased according to the non-circular signal characteristic, maximization of the recognized targets is achieved, the algorithm complexity is low, and additional pairing processes are not needed.

The invention discloses a method, namely an SLS-ESPRIT (structure least square-estimating signal parameters via rotational invariance techniques) algorithm, for joint estimating angle of departure (DOD) to angle of arrival (DOA) of a target in a MIMO (multiple input multiple output) radar system on the basis of an SLS method. Estimation accuracy of signal subspace is improved by solving rotational invariance equation by the aid of the SLS and iterating estimation error of minimized signal subspace, and angle estimation performance is enhanced. In addition, effectiveness of the estimation method is testified through simulation results.

The invention relates to a dynamic tracking method of the direction of arrival of coherent signals under an impulsive noise environment. The dynamic tracking method comprises the steps that signal sampling data are acquired; classifying unification preprocessing is conducted on a received signal data vector; a uniform linear array is divided into a plurality of subarrays; a received data vector undergoing spatial forward backward smoothing is substituted into a PASTA algorithm to obtain signal subspace; a MUSIC algorithm is used for processing the data of the signal subspace, and the direction of arrival of object signals is acquired through spectral peak searching; a t is made to be equal to t+1, the angle of the direction of arrival of the signals with corresponding the beat number is obtained until the maximum beat number is reached, and DOA dynamic tracking of coherent signal sources under the impulsive noise environment is achieved. According to the method, a projection subspace approaching tracking algorithm is used, the signal subspace of incident signals can be tracked in real time, then a MUSIC method is used for conducting precise detection on the direction of arrival of the signals, the robustness of the method is high, and the tracking effect is good.