基于改进的CLAMBDA算法的低成本车载定姿研究

Low-Cost Vehicle Attitude Determination Based on Improved CLAMBDA Algorithm

在消费级低成本车载姿态解算中,由于接收机性能较差,采用最小二乘模糊度降相关平差(least square ambiguity decorrelation adjustment,LAMBDA)方法进行整周模糊度搜索时成功率较低。针对这一问题,提出了利用基线长度已知的条件,使用CLAMBDA(constrained LAMBDA)算法进行解算,并添加了检核条件改进算法提高性能。实测数据处理结果表明,该方法能提高整周模糊度固定成功率和正确率,当动态实验和观测条件不好时效果尤为突出。

In low-cost consumer-grade vehicle attitude solution,due to the poor receiver performance,the search success rate is low when using the LAMBDA method for ambiguity.In view of this problem,this paper takes advantage of the conditions where the baseline length is known,conducts the solution with the improved CLAMBDA algorithm,and adds a check condition to improve the algorithm performance.The measured data processing results show that this method can improve the fixed performance of the ambiguity,especially when the dynamic experiment and observation conditions are not good.