Estimation Algorithm of Incident Sources' Stokes Parameters and 2‐D DOAs Based on Reduced Mutual Coupling Vector Sensor

Abstract

To estimate Stokes parameters and two-dimensional (2-D) direction-of-arrival (DOA) of incident sources, a computational efficient algorithm is proposed based on reduced mutual coupling vector sensor. The biquaternion-based output model of reduced mutual coupling vector sensor is presented. The contribution in this paper is mainly on the estimation of Stokes parameters. By exploiting the information in both the covariance matrix and the pseudo-covariance matrix of the biquaternion-based sensor's output, the Stokes parameters and 2-D DOAs of partially polarized source can be extracted in closed form. Comparing with the Jone's formulation, the Stokes' formulation can characterize both completely polarized waves and partially polarized waves. Thus, the proposed algorithm is also applicable to completely polarized source. Because the proposed algorithm presents in closed form and does not require the search or iterative process, it is computationally more efficient. Simulations are used to verify the performance of the proposed algorithm.

Improved Drosophila Visual Neural Network Application in Vehicle Target Tracking and Collision Warning

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“…In a binary graph, different connected regions of the same moving target can be merged using the distance between the bounding rectangles of the moving target area and the distance threshold. The distance between the bounding rectangles is calculated using formula (11).…”

“…, and the distance threshold is set as  . The distance between adjacent target areas is calculated using formula (11), If the distance between two adjacent target areas is less than or equal to  , the two target areas will be merged and the center point coordinates of the merged target area will be recalculated. If it is greater than  , adjacent target areas will be re selected to determine whether to merge until all adjacent target areas have a distance greater than  .…”