Angular distributions of reflected and refracted relativistic electron beams crossing a thin planar target at a small angle to its surface

Серов, А. В.; Mamonov, I. A.; Koltsov, A. V.

doi:10.1134/s1063776115100143

Cited by 4 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bishop et al [35] proposed a new algorithm based on geometry constrained least squares (GCLS), which minimizes the angle observation error under the constraint of geometric positional relationship between the target and angle observer. This method is equivalent to TBML under Gaussian observation noise because the geometric constraints do not provide any additional information to the position estimate.…”

Section: Introductionmentioning

confidence: 99%

Lateral Cross Localization Algorithm Using Orientation Angle for Improved Target Estimation in Near-Field Environments

Xu¹,

Yan²

2016

Information

View full text Add to dashboard Cite

Abstract:Passive positioning systems with a small aperture array exhibit poor accuracy of target estimation under strong interference in near-field environments. To improve this accuracy, we propose a novel cross localization algorithm for direction-finding using the orientation angle. Improved geometric and numerical target-positioning models are constructed after analyzing the mechanism of the conventional positioning algorithm. The target prediction equation is then derived using the constructed models, and the equation for nonlinear estimation is linearized using the Taylor series. An unbiased estimation of the target is obtained by optimizing the control of the iteration process, thus achieving an accurate positioning of the target. The performance of the proposed algorithm was evaluated in terms of its effectiveness and positioning accuracy under varying signal-to-noise conditions and orientation angle-measurement errors. Simulation results show that the proposed algorithm is capable of positioning the target effectively, and offers better positioning accuracy than traditional algorithms under the conditions of large orientation angle measurement errors or high-level background noise.

show abstract

Section: Introductionmentioning

confidence: 99%