Abstract:

In a complex communication environment,it is difficult for the global navigation satellite system (GNSS) to provide users with stable and accurate location information.An enhanced adaptive genetic location (EAGL) algorithm is proposed to solve the problem of positioning bias caused by the uncertainty of measured data.In the proposed algorithm,a localization model based on time difference of arrival is established to reflect the relationship between the location of target source and the signal environment.The possible solutions satisfying the objective function are encoded in real number,and the fitness function is established,which is used to calculate the fitness value of each individual.The selection operation is performed on the population,and the improved adaptive crossover and mutation operation are used to improve the genotype quality of the population and avoid falling into the dilemma of local optimal solution.The genotype of the individual with the highest fitness value is obtained by iteration to get the exact coordinates of a target source.The simulated results show that the positioning accuracy of the proposed algorithm is higher than those of the simple genetic algorithm (SGA) and Chan-Taylor algorithm.With the gradual increase in the error of the measured value,the error fluctuation of EAGL algorithm under different error conditions is the smallest.As a result,EAGL algorithm is stable and capable of achieving the high-precision positioning.

Key words: communication localization, genetic algorithm, adaptive, crossover, variation