多套光学经纬仪联合跟踪的容错最小距离平方和定位

Joint multiple optical theodolite tracking based outlier-tolerant least distance sum localization

针对现有算法在多套经纬仪测角数据解算目标位置时计算复杂,精度受限,对野值数据缺乏容错能力等问题,提出了一种基于最小距离和的融合容错定位方法。在采用最短距离和融合不同设备数据的同时,通过算法的容错设计,在测量数据含野值时依然能够正常解算出目标位置,确保定位结果的可靠性。仿真结果表明,该定位方法对测量数据具有良好的容错能力,当测量数据正常时定位结果达到距离和最小;当测量数据含孤立型野值或长度不超过3个的斑点型野值时,无需事前修复即可确保定位结果依然可靠。本文建立的最小距离和容错定位算法,既能够充分利用有效的测量数据信息,又可以在不进行野值检验的情况下进行定位计算,确保定位结果不失真,对动态目标的实时可靠定位具有重要的应用价值。

Aiming at the problems inherent to existing targeting algorithms,such as complicated calculations,poor accuracies,and lack of fault tolerances for outliers when solving for the target position using multiple sets of theodolite angle measurement data,a new type of fusion fault-tolerant positioning method based on the shortest distance sum was proposed.By using the sum of the shortest distance method to fuse data from different devices,the fault-tolerant algorithm designed in this study could still obtain the target position,which was mostly unaffected by outliers in the measured data,thereby ensuring the reliability of the positioning results.Simulation results show that the proposed method exhibits a good fault tolerance for the measured data,i.e.,when the measurement data are normal,the location result reaches the minimum sum of distances,and when the measurement data contain isolated outliers or speckle outliers with a length of less than 3,it can ensure that the positioning results are still reliable and mostly unaffected by outliers without prior repair.The fault-tolerant location algorithm based on the sum of the minimum distances not only makes full use of the effective measurement data information,but also carries out positioning calculations without an outlier detection,ensuring that the positioning results are not distorted.This has important engineering value for real-time and reliable positioning of dynamic targets.