40 results about "Spatial estimation" patented technology

The invention discloses an improved subspace sea clutter suppression method based on local correlation. Suppression of sea clutter can improve the signal to clutter ratio of targets and is conductive to improving the vessel target detection performance of high frequency surface wave radar (HFSWR). The original subspace method leads to identical covariance matrix of all distance unit structures during sea clutter suppression, so that a poor suppression effect caused by inaccurate clutter space estimation is brought about, which is reflected on the distance Doppler spectrum that the suppression effects of sea clutter in different distances are similar. According to the invention, the related coefficient between a distance unit to be detected and adjacent distance units is calculated, the reference unit of the covariance matrix is determined and formed in a self-adaptive manner, the formed covariance matrix is subjected to singular value decomposition (SVD) to obtain subspaces of the sea clutter, and finally, suppression is realized. The method can simultaneously suppress first-order and high-order sea clutter, improves the signal to clutter ratio of the targets and has a relatively ideal suppression effect on all distance units.

Video data compression performance is improved through the use of multiple processors operating in parallel. The parallel processors perform motion or spatial estimation, where portions of a video frame are found to be similar to portions in reference frames. Because this estimation operation can be very time consuming, the use of multiple processors can reduce the overall time required, or they can enable higher-performing algorithms that might otherwise require a prohibitively long processing time. The motion or spatial estimation results are applied to reconstructed versions of the video frame data to enable high levels of video data compression.

A system and a method for spatially ordered estimation and visualization of multi-lead electrocardiographic ST deviations induced by myocardial ischemia, in which system a plurality of ECG signals are recorded from a ECG source, which signals are stored by a processor in a memory, which processor processes the signals to obtain ST deviation, which processor performs measurement of ST deviation from each lead where the processor performs a multi-dimensional estimation of an vector representing of the spatial direction and magnitude of the underlying cardiac injury-current giving rise to the measured ST deviations, which processor hereby estimates the spatial location and severity of myocardial ischemia.

The invention relates to an arrival direction self-correction method for a sensor array. According to the method, a noise subspace estimated value is determined; an array phase error of the sensor array is initialized, and an initial value of the signal arrival direction is estimated through utilizing an MUSIC algorithm; based on the calculation value of the signal arrival direction, a Hermitian positive definite matrix is calculated, and the Hermitian positive definite matrix satisfies a first equation; an array element phase error is made to satisfy the first limit condition, and the array phase error is converted to satisfy a second equation; the first equation and the second equation are solved through utilizing a Lagrangian multiplier method to acquire a vector estimated value of the array phase error; a correction value of the signal arrival direction is acquired through utilizing the MUSIC algorithm or a high resolution subspace algorithm; and iteration of the previous steps is repeatedly carried out till iteration stop conditions are satisfied. The method is advantaged in that dependence on special structure characteristics of an array output covariance matrix is avoided, excellent performance is realized under the condition of limited sampling data, and influence of the sensor array error on the arrival direction can be effectively reduced.

Described herein is a system and a method of spatial field estimation from input data from a domain of interest. The method comprises defining a spatial mesh of positions over the domain of interest (802) and defining a smoothness information model (804) which is defined with respect to the spatial mesh to form an information matrix Y1 and vector y1. The method further comprises defining an information representation of the input data, the information representation comprising an information matrix Y_obs and vector y, both defined relative to the spatial mesh. The method further comprises through an additive function fusing (806) the smoothness information model with the information representation of the input data to form an information matrix Y and vector y. The method then comprises, in a computational system, solving for x in Yx=y (808), wherein x represents the spatial field estimation.

The invention provides a spatial microphone positioning method based on a sound source array and relates to the field of indoor positioning. According to the method, a device composed of a sound source distribution unit, a reference microphone, a to-be-positioned microphone and a microphone signal processing unit is used, three sound signals different in frequency band are selected and used as sound production signals of three sound sources to achieve spatial estimation of the microphone position, and the three sound signals different in frequency band are received by the to-be-positioned microphone and the reference microphone; by calculating the distance between the to-be-positioned microphone and the sound sources, the spatial position of the to-be-positioned microphone is calculated; through a mode of sodar time difference and geometric volume solution, spatial microphone positioning based on the sound source array is achieved. The defects in the prior art are overcome that the operation is cumbersome, the efficiency is low, installation is inconvenient, only offline calculation can be conducted, and online estimation of the microphone position and position estimation of the spatially distributed microphones cannot be achieved.

The present invention belongs to the field of the array signal processing technology, and provides a multiple signal classification method based on a Sigmoid covariance matrix. The method has high inhibition capability for the pulse noise obeying the non-Gaussian distribution, and is able to realize the multiple signal classification in the condition of the pulse noise and estimate the wave reaching direction of each signal. The method comprises: 1) estimating the parameters of a Sigmoid function according to the median including the signal amplitude of noise; 2) estimating the Sigmoid covariance matrix outputted by the array through adaption of the Sigmoid function and the output vector of an uniform linear array; 3) performing characteristic constant decomposition of the Sigmoid covariance matrix and obtaining the estimation of the noise subspace; and 4) employing the noise subspace estimation for the space spectrum of the multiple signal classification, and estimating the wave reaching direction angle through adaption of the estimated value of the space spectrum. The multiple signal classification method based on a Sigmoid covariance matrix has good algorithm performance, and has a good application prospect in the real engineering application.

The invention discloses a transfer and semi-supervised learning-based spatial estimation method for an air quality index of a non-city region. The method comprises the following steps of (1) based onterrain distribution and air quality monitoring station deployment situations, looking for an auxiliary region around a target region, and constructing an auxiliary sample set; (2) based on a transferlearning technology, and in combination with a tagged sample set of the target region and the auxiliary sample set, training multiple regression models; and (3) based on a semi-supervised learning technology, and by utilizing an untagged sample set of the target region, enhancing and fusing the multiple regression models to obtain a final air quality index spatial estimation model. The method isbased on the transfer and semi-supervised learning technologies, and effectively utilizes tagged data generated by air quality monitoring stations of the non-city region of the auxiliary region and untagged data of the non-city region of the target region, thereby solving the problem that the target region lacks of the air quality monitoring stations deployed in the non-city region.

A method for detecting a loop closure is described. A device accesses pose information and a three-dimensional map of feature points generated by a visual inertia system of the device. The device splits the pose information into a translational part and a rotational part. The device limits the translational part to two-dimensional coordinates and estimates two-dimensional information of the limited translational part based on an accumulator voting space. The device determines an updated pose of the device based on the estimated two-dimensional information, the rotational part, and the three-dimensional map. The pose information is updated with the updated pose.

The invention provides a conical surface conformal array blind polarization direction-of-arrival estimation method. The method comprises the following steps of 1, performing of data modeling on a conical surface conformal array antenna snapshot; and 2, a real-valued blind polarization direction-of-arrival estimation method. According to the new algorithm, the subspace estimation process is subjected to real-valued processing, the problem of mirror image blur caused by covariance matrix splitting is solved through subarray segmentation design, the Cramer-Rao bound of parameter estimation is deduced, and the algorithm complexity is reduced on the premise that DOA estimation precision is guaranteed.

The invention discloses a rapid MUSIC spectrum decomposition method, device and equipment for a large-scale antenna, and the method comprises the steps: receiving a signal X, and estimating a high-dimensional autocorrelation matrix R according to the signal X; performing skeleton extraction on the high-dimensional autocorrelation matrix R to obtain a low-dimensional representation matrix C; calculating to obtain a low-rank matrix Y and obtaining a low-dimensional approximate decomposition CY of the high-dimensional autocorrelation matrix R; obtaining an SVD approximate decomposition (shown inthe description) of the high-dimensional autocorrelation matrix R is obtained by conducting SVD decomposition on the low-dimensional approximate decomposition CY; constructing a signal space K by means of the SVD approximate decomposition (shown in the description) of the high-dimensional autocorrelation matrix R, estimating a spatial spectrum P (theta) by means of the signal space K, and conducting target signal detection and estimation according to the spatial spectrum P (theta). According to the invention, while estimating the MUSIC spatial spectrum accurately, the calculation complexity ofSVD is reduced from cubic growth to square and even linear growth rate, high-precision and low-complexity MUSIC spatial spectrum estimation is realized, matrix norm minimization is used as an optimization criterion, and the approximate error precision of a high-dimensional autocorrelation matrix is ensured.

The invention discloses an estimation method for soil respiration on basis of remote sensing data. According to the estimation method, spatial estimation accuracy of the soil respiration on large-area scale can be improved and the uncertainty of a soil respiration space is reduced. The estimation method comprises the following steps: constructing a regional soil respiration analysis data set; dividing biomes; constructing a soil respiration estimation model driven by multi-source remote sensing data; verifying and selecting an optimal soil respiration estimation model, so as to obtain regional-scale spatial-temporal distribution of the soil respiration by using spatial remote sensing data as driving data.

The invention provides a physiological state prediction method, which comprises the following steps that: collecting n samples, wherein each sample in the n samples comprises N parts of physiological signal data; projecting the N parts of physiological signal data included in each sample to a space of a B-spline function, and estimating a track of each sample; estimating a slope variance function based on the estimated trajectory of each of the n samples; estimating a characteristic function based on the estimated oblique variance function, and obtaining an estimated value of a principal component corresponding to each sample based on the estimated characteristic function; and based on the estimated value of the principal component corresponding to each sample, utilizing a preset prediction model to predict the physiological state corresponding to each sample. The invention further provides a computer device and a storage medium for implementing the physiological state prediction method. The method can improve the prediction accuracy while simplifying the calculation burden.

The invention discloses a self-powered detector three-dimensional measuring point arrangement optimization method based on a Kriging model, and the method comprises the steps: considering the influence of a self-powered detector measuring point position set in a nuclear reactor monitoring system on a spatial estimation value of a non-measuring point position, and calculating a spatial covariance between specific positions of the monitoring system; calculating a weight function, a penalty factor and a Kriging variance of a non-measuring point position according to the spatial covariance; on the basis of a simulated annealing process, searching a minimum value which takes a Kriging variance average value of a non-measuring-point position set as an objective function, and optimizing an arrangement scheme of the self-powered detector according to the minimum value; the measuring point arrangement optimization method can effectively reduce uncertainty of estimated parameters, is high in universality, is suitable for various nuclear reactor monitoring systems, can be used for evaluating a current self-powered detector measuring point arrangement scheme and recommending measuring point optimization design, and provides a new method for reactor core design of the self-powered detector.

The invention relates to a monostatic FDA-MIMO radar distance angle joint estimation method based on real value order reduction, and belongs to the technical field of FDA-MIMO radar research. On the basis of a real value order reduction method, a complex value receiving model is converted into a real value receiving model by utilizing forward and reverse average and unitary transformation technologies, original complex operation is converted into real value operation, the calculation complexity is reduced compared with other methods, then a covariance matrix of a real value receiving signal is calculated according to a maximum likelihood estimation criterion, and the real value receiving signal is obtained. And carrying out eigenvalue decomposition on the real-value covariance matrix by adopting EVD, selecting eigenvectors corresponding to smaller eigenvalues to form a real-value noise subspace, and estimating the angle and the distance by utilizing the real-value noise subspace. According to the angle and distance estimation method, the two-dimensional search cost function of the angle and the distance is decomposed into two one-dimensional searches by using the property of the Kronecker product, and the calculation amount is greatly reduced on the premise of ensuring the search precision.