Passive Location Based on Weighted Least Squares and Genetic Algorithm

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    For the passive location of radiation sources for motion communications in complex environments, the closed-form solution method is sensitive to measurement noise in time-frequency difference models and has a large root-mean-square error of location. To improve the location performance under large observation errors, this study proposes a recursive hybrid TDOA/FDOA location method, which is based on weighted least squares and the genetic algorithm. Firstly, massive time-frequency difference data are observed at known stations, and error models are built. On this basis of the models, multiple sets of time-frequency difference sequences are processed. Secondly, the initial value of the target position is solved by weighted least squares. Given the initial value, the improved genetic algorithm is used to solve and correct the position coordinates through multiple groups of time-frequency difference sequences iteratively and recursively. Finally, position estimation and the frequency difference model are used to estimate the target velocity. The simulations show that the proposed location algorithm has a lower root-mean-square error than the classical two-step weighted least squares method and can maintain high accuracy under large observation errors. Moreover, compared with other hybrid location algorithms, the proposed algorithm boasts a fast convergence speed and can effectively reduce the amount of computation.

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  • Received:December 01,2022
  • Revised:January 06,2023
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  • Online: April 23,2023
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