9 results about "Spatial estimation" patented technology

A three-dimensional space estimation system includes a relative depth acquisition unit that acquires relative depth of a two-dimensional image, a three-dimensional skeleton information acquisition unit that acquires three-dimensional coordinates of reference points set on a human body in the two-dimensional image, a two-dimensional skeleton information acquisition unit that acquires two-dimensional coordinates of the reference points in the two-dimensional image, a parameter calculation unit that calculates parameters of a conversion formula by substituting the three-dimensional and two-dimensional coordinates of the reference points into the conversion formula, a linear ratio acquisition unit that acquires a linear ratio on the basis of an absolute distance and a relative distance between specific points on the human body, and a three-dimensional space model generation unit that generates a three-dimensional space model by arranging pixel values of the two-dimensional image at corresponding positions in a three-dimensional space using the parameters, the linear ratio, and the relative depth of each pixel.

A radio-frequency (RF) receiver includes: at least n antennas, where n is an integer greater than two; m processing channels configured to receive and process n RF signals from the at least n antennas

A local anomaly detection model for monitoring a local entity set of a network system is generated by applying a transformation function to a global anomaly detection model for the network system, without re-training the global anomaly detection model for the local entity set. The global anomaly detection model, which may be generated via unsupervised learning methods, includes global vector(s) of metrics and a global healthy vector space. The transformation function is estimated and applied to the global anomaly detection model to generate the local anomaly detection model, which includes local vector(s) of metrics pertaining to the local entity set and a local healthy vector space. Responsive to a determination that the local vector(s) of metrics comprises one or more anomalous data points outside of the local healthy vector space, an alert regarding the one or more anomalous data points can be generated and output.

The invention discloses a dictionary adaptive processing method and device based on feature subspace guidance, and the method comprises the steps: selecting space-time snapshot data from an adjacent distance unit of a to-be-detected distance unit, carrying out the clutter subspace estimation of a constructed sample set, and obtaining a clutter subspace; according to a self-adaptive dictionary matrix generated by the clutter subspace, reconstructing to obtain a clutter covariance matrix; obtaining a clutter and noise covariance matrix for calculating the weight of the STAP filter according to the clutter covariance matrix; and obtaining an STAP filter weight according to the clutter plus noise covariance matrix, and filtering the space-time snapshot data of the distance unit to be detected according to the STAP filter weight to obtain filtered data. The method has stronger clutter structure characterization capability, fundamentally solves the problem of grid mismatch, overcomes the defects of overlarge calculation amount and high correlation of dictionary atoms, and improves robustness while significantly reducing sparse recovery calculation complexity.

A set of geometric shapes to be applied by a machine learning model to objects identified in image data is defined. A learning rate of the machine learning model is updated in response to external events. The machine learning model is used to estimate spatial parameters for each of the objects identified in the image data. The spatial parameters are estimated by fitting the objects to the set of geometric shapes. Updates to the spatial parameters are temporally integrated. A spatial estimate of the objects identified in the image data is generated.

A radio-frequency (RF) receiver includes: at least n antennas, where n is an integer greater than two; m processing channels configured to receive and process n RF signals from the at least n antennas, where m is an integer greater than one and less than n; a controller configured to cause a first processing channel of the m processing channels to receive, at different corresponding times, a plurality of RF signals of the n RF signals; an indexing module configured to receive outputs from the m processing channels, and generate one or more representations of the n RF signals based on the outputs; and a spatial estimation module configured to receive the one or more representations, execute a machine learning model based on the one or more representations, and determine, based on an output of the machine learning model, a spatial estimate for an emitter of the n RF signals.

The invention provides a convolution beam space DOA estimation method based on hole filling, so as to solve challenges faced by a traditional convolution beam space method. According to the algorithm, differential common array holes are filled through low-rank matrix recovery, a solving framework is constructed through an alternating direction multiplier method, and a multi-phase component averaging strategy based on a sliding window is designed for the extraction process. Simulation results show that the method solves the problem that the precision of a traditional convolution beam space method is reduced, and only negligible extra time overhead is introduced. Through comparison, the performance of the provided method is better than that of a traditional convolution beam space method and a method based on atom norm minimization and CVX toolbox solving, and the advantages are crucial for realizing real-time and high-precision DOA estimation in a large-scale extended co-prime array scene.

This invention proposes a fusion subspace DOA estimation method based on sparse Bayesian learning. The implementation steps are as follows: first, an overcomplete steering vector dictionary matrix is ​​constructed, and a sparse representation model is built. Then, the signal is reconstructed using the sparse Bayesian learning algorithm. After obtaining the reconstructed signal, a signal covariance matrix is ​​constructed. Finally, eigenvalue decomposition is performed on the covariance matrix to construct a spatial spectral function, and the peak value of this spectral function is searched across the entire angular range. This invention integrates the SBL algorithm and the MUSIC algorithm. The MUSIC algorithm is used to perform subspace decomposition on the reconstructed covariance matrix, extracting the noise subspace and utilizing its orthogonality with the steering vector, thereby improving the angular resolution of the SBL algorithm. This makes this invention applicable to achieving high-resolution DOA estimation under conditions of few or even single snapshots.

The invention discloses a subspace estimation adaptive matrix sensing method for compressed sensing imaging. A Gaussian random linear operator A is used as a stimulation mode to be applied to a compressed sensing imaging system so as to carry out p1-time measurement on a scene to be measured, and a corresponding observed quantity y1 is collected. And obtaining first matrix estimation of an unknown matrix X of the scene to be measured through a matrix low-rank recovery algorithm by using y1 and A. And designing a second measurement operator AH based on specific structure information estimated by the first matrix. AH is used as a stimulation mode to be applied to the compressed sensing imaging system for measurement, new observed quantity y2 is collected, and the unknown matrix X is recovered by combining the first matrix estimation, the coefficient matrix estimation and the column space base. According to the method, the total number of times of measurement of the unknown matrix representing the image can be reduced, and the method has great value in invisible light wave band imaging, machine learning and big data, especially in scenes extremely sensitive to measurement cost.