2014Unpublished venueRequires access

One intelligent algorithm for estimation of TDOA and FDOA

Zhi yu Lu, Jian hui Wang, Da ming Wang, Yue Wang

Open publisher page 1 citations

Abstract

The calculation is large to estimate the TDOA and FDOA with cross ambiguity function. Existing algorithms which are based on the ergodic theory have poor real-time performance. To solve this problem, the genetic algorithm is proposed with improvements based on the characteristics of cross ambiguity function. With the self-adapting mutation probability by following the convergence extent of the population and multiple population initializations, the diversity of the population is effectively improved to prevent the algorithm into a local optimum. The simulation results show that the computational efficiency of the improved algorithm, compared with the existing algorithms, is greatly improved, and the TDOA/FDOA estimation results can quickly be obtained.

About this research paper

What this paper is about

The calculation is large to estimate the TDOA and FDOA with cross ambiguity function. Existing algorithms which are based on the ergodic theory have poor real-time performance. To solve this problem, the genetic algorithm is proposed with improvements based on the characteristics of cross ambiguity function. With the self-adapting mutation probability by following the convergence extent of the population and multiple population initializations, the diversity of the population is effectively improved to prevent the algorithm into a local optimum. The simulation results show that the computational efficiency of the improved algorithm, compared with the existing algorithms, is greatly improved, and the TDOA/FDOA estimation results can quickly be obtained.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The calculation is large to estimate the TDOA and FDOA with cross ambiguity function. Existing algorithms which are based on the ergodic theory have poor real-time performance. To solve this problem, the genetic algorithm is proposed with improvements based on the characteristics of cross ambiguity function. With the self-adapting mutation probability by following the convergence extent of the population and multiple population initializations, the diversity of the population is effectively improved to prevent the algorithm into a local optimum. The simulation results show that the computational efficiency of the improved algorithm, compared with the existing algorithms, is greatly improved, and the TDOA/FDOA estimation results can quickly be obtained.

Key concepts: FDOA, Multilateration, Computer science, Algorithm, Ambiguity, Convergence (economics), Population, Mathematical optimization

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