基于岭最小截平方的传感器稳健配准方法

传感器配准是多传感器数据融合系统获得性能优势的关键前提.受随机噪声、系统误差、虚警、漏报等因素的干扰，传感器配准常常工作在非理想关联环境中，依赖于理想关联假设的传统配准方法性能衰退严重.另一方面，传统传感器配准方法对目标分布场景敏感，当目标密集分布时，配准问题呈现病态性，估计结果数值不稳定.本文重点研究非理想关联及场景病态性共存时的传感器稳健配准问题，提出了系统误差的岭最小截平方（Ridge Least Trimmed Squares，RLTS）估计方法.该方法结合了岭回归（Ridge Regression，RR）与最小截平方（Least Trimmed Squares，LTS）估计的优点，能够有效应对错误关联及病态性的不良影响.仿真实验证实了所提方法的稳健性能.

Robust Sensor Registration Based on Ridge Least Trimmed Squares

Sensor registration is the key precondition of the performance advantages of the multisensor data fusion system.In the presence of random errors,sensor biases,false alarms and missed detections,sensor registration usually works in a nonideal association envrionment.Traditional registration approches relying on the ideal association condition degrade seriously.On the other hand,traditional registration methods are sensitive to the target distribution.When targets are densely distributed,the registration problem is ill-conditioned and the estimation encounters the numerical instability phenomena.Focusing on sensor registration in the context of nonideal association and ill-condition,this paper presents the robust registration approach based on ridge least trimmed squares (RLTS).The proposed approach combines the advantages of the ridge regression (RR) and the least trimmed squares (LTS) estimation.The RLTS can deal with nonideal association and ill-condition simultaneously.Simulation results verify the robust performance of the RLTS method.