An algorithm of global maximum search for evaluating position estimation of electromagnetic emitting source by a passive radar system.

Zhukov, D. M.; Vavilova, Zhanna A.; Shevgunov, Timofey; Guschina, Oksana; Efimov, Evgeniy

doi:10.30898/1684-1719.2018.12.8

Cited by 2 publications

(1 citation statement)

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“…The results of the conducted analysis of the frequency of abnormal errors and relative time costs in the course of calculations by the proposed algorithm of search with the adaptive interval for the narrow-base subsystems and combined passive system, as well as wide-base passive system [8], show a significant saving of computing costs (from 3 to 20 times, depending on the type of passive system) for the search for the extremum in comparison with the universal method of searching the global extremum, such as "particle swarm" [9], [10]. The proposed algorithm provides a zero frequency of abnormal errors, while the universal algorithm is characterized by the frequency of abnormal errors around 0.01.…”

Section: Discussionmentioning

confidence: 99%

Heuristic Global Optimization Algorithm for Estimating a Radio Source Location by a Passive Radar System

Shevgunov¹,

Vavilova²,

Guschina³

et al. 2020

TEM Journal

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The methods for generating estimator for radio source location based on digital processing of signals received at various points in space are one of the main areas of multi-position radar systems’ research. Nowadays the above-mentioned methods that can provide the highest accuracy among the others are subject of interest. So, the mean square error usually serves as a measure of accuracy, which allows formulating a convenient, for mathematical transformations, quality criteria and synthesizing the algorithms. The traditional estimation algorithms have a multi-stage character, and they are based on the formation of optimal estimators for time and phase delays of the signals and their subsequent conversion to the source coordinates. The research has the modern approaches of the development of new positioning algorithms to guarantee the achievement of the minimum mean square error and do not create excessive computing load.

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Section: Discussionmentioning

confidence: 99%