5 results about "Kernel adaptive filter" patented technology

This invention discloses an adaptive filtering method based on the multi-kernel Nystrom method, belonging to the field of kernel adaptive filtering. The multi-kernel Nystrom method combines multiple kernel functions, which can include not only Gaussian kernels but also other different kernel functions, mapping the original data to multiple different independent feature spaces. Compared with the traditional single-kernel Nystrom method, the multi-kernel Nystrom method has better filtering accuracy and becomes less sensitive to the choice of kernel parameters. Compared with other multi-kernel adaptive filtering methods, it can significantly reduce computational complexity, time, and memory consumption. Subsequently, this invention introduces the proposed multi-kernel Nystrom method into kernel adaptive filters for the first time. Experiments show that this algorithm can achieve better filtering accuracy than current state-of-the-art kernel adaptive filtering algorithms with relatively low computational complexity.

The invention discloses a near-field millimeter wave super-resolution imaging method based on nuclear adaptive filtering, and relates to the field of super-resolution imaging. The method is based on a kernel adaptive filtering operator, through a lightweight learning method, it is ensured that estimation characteristics conforming to original data to the maximum extent are met, and a high-resolution image is efficiently predicted and reconstructed. Aiming at a subsequent image processing process of a two-dimensional near-field millimeter wave imaging system, under the condition of shortening data sampling time, a high-resolution image is reconstructed from low-resolution sampling data, and redundant hardware, a large number of image data sets and a dictionary learning process are not needed. Based on the method provided by the invention, the optimal visual effect is achieved in data sets with different shape features, and detail information in an amplified view is clearest. And compared with a typical super-resolution algorithm, the effect is better. Compared with a typical super-resolution algorithm, the method provided by the invention has the highest numerical value similarity and higher structural similarity.

This invention discloses a near-field millimeter-wave super-resolution imaging method based on kernel adaptive filtering, belonging to the field of super-resolution imaging. This method, based on a kernel adaptive filtering operator, uses a lightweight learning method to ensure maximum accuracy in predicting and reconstructing high-resolution images by accurately matching the estimated features of the original data. This invention addresses the subsequent image processing of two-dimensional near-field millimeter-wave imaging systems, reconstructing high-resolution images from low-resolution sampled data while shortening data sampling time, without requiring additional hardware, large image datasets, or dictionary learning processes. The proposed method exhibits optimal visual effects across datasets with varying shape features, providing the clearest detail in magnified views. It outperforms typical super-resolution algorithms. The proposed method demonstrates the highest numerical similarity and high structural similarity compared to typical super-resolution algorithms.

This invention discloses an adaptive data-driven fractional derivative positioning optimization method, belonging to the field of radio positioning technology, comprising the following steps: S1. Receive signal strength indicators at actual locations using sensors within a target area to generate an RSSI dataset; S2. Generate and train a fractional derivative kernel adaptive filter based on the RSSI dataset; S3. Input the actual location and RSSI dataset into the trained fractional derivative kernel adaptive filter to obtain the target location. Compared to traditional classical kernel adaptive filtering methods, this invention utilizes a weight update strategy constructed using fractional derivatives, resulting in higher and more robust filtering accuracy. This invention is the first to incorporate fractional calculus combined with q-Laplacian kernel technology into kernel adaptive filters to construct a positioning system. Experiments have shown that this algorithm achieves better positioning accuracy than the most advanced positioning algorithms currently available.

The invention discloses an indoor positioning method for adaptive data processing, and relates to the field of data processing. Compared with a traditional classical kernel adaptive filtering method, the method adopts a weight updating strategy, and has higher and more stable filtering precision. According to the invention, the q-series fitting is introduced into the kernel adaptive filter for the first time to construct the positioning system; experiments show that compared with the current most advanced positioning algorithm, the algorithm provided by the invention can obtain better positioning precision. The kernel adaptive filter provided by the invention and the classical kernel adaptive filtering algorithm are applied; compared with an indoor positioning algorithm based on machine learning, the classical indoor positioning method has the highest filtering precision and higher robustness. Compared with a classic kernel adaptive filtering algorithm, a classic indoor positioning method and an indoor positioning algorithm based on machine learning, the high-precision positioning adaptive data processing method has remarkable advantages in the aspect of positioning prediction accuracy.