自适应遗传算法抑制复杂短时延Galileo BOC(1,1)多径

摘要

针对传统GPS多径抑制算法无法在复杂短时延多径情况下准确估计出Galileo BOC(1,1)多径信号,提出一种隐含多径数量参数的多径模型,并构造自适应遗传算法对实际多径信号进行估计.当实际多径参数变化不大时,引用上次估计的多径参数,直到多径模型发生较大变化时,再启动遗传算法进行多径估计.城市峡谷中多径信号频繁消失和出现,而文算法中隐含对消失多径信号的估计,当消失的多径信号再次出现时,可以快速获得多径估计模型.本算法对于BOC(1,1)多径信号估计更加准确,有效地减小复杂短时延多径环境下每个通道的伪距测量误差,从而提高了Galileo接收机的定位精度,并且在一定程度上减小了计算量.%Complex close-in Galileo BOC(1,1) multipath signals can not be estimated by traditional GPS multipath mitigation algorithms, and therefore a model containing multipath number parameter and using genetic algorithm to estimate the multipath signals was provided. When the actual multipath signal changes little, the last estimated multipath parameters is used, until the multipath model changes largely, then the genetic algorithm is used to estimate the multipath parameter. In urban valley, the multipath signals disappear and appear frequently, and the algorithm estimates all the multipath signals even when it disappears temporarily. Thus when the multipath signals appear again, the multipath parameters could be obtained quickly. The algorithm estimates the multipath signals accurately and reduces the pseudo-range measurement errors, and thereby improves positioning accuracy, and to some extent, reduces the computational complexity.