Hybrid non-line-of-sight localization algorithm based on IMM-KF
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NETL
NSTL
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为了降低视距(Line of Sight,LOS)和非视距(Non-Line of Sight,NLOS)混合场景下无线定位的误差,提出一种基于交互式多模型-卡尔曼滤波(Interactive Multiple Model-Kalman Filter,IMM-KF)的残差选择NLOS定位算法.构建适用于LOS的多边定位模型和适用于NLOS的残差选择3边定位模型,通过似然概率加权估计两个模型的融合位置.结合卡尔曼滤波进行误差估计确定残差,得到最优的位置估计值,从而降低计算复杂度和由计算不准确导致的模型失配问题.仿真结果表明,所提算法在混合场景中NLOS噪声服从高斯分布、指数分布及均匀分布下,定位精度优于其他对比算法,能有效降低NLOS误差.
To reduce the error in wireless positioning in mixed line of sight(LOS)and non-line of sight(NLOS)scenarios,a residual selection NLOS positioning algorithm based on the interactive multiple model-Kalman filter(IMM-KF)is proposed.This approach constructs a multi-lateration positioning model suitable for LOS and a residual selection trilateration positioning model suitable for NLOS.The fusion position of these two models is estimated through likelihood probability weighting,and the residuals are determined by the Kalman filtering for error estimation.The optimal position estimate is obtained,reducing both computational complexity and model mismatch issues caused by inaccurate calculations.Simulation results demonstrate that the proposed algo-rithm effectively reduces the NLOS errors under noises with Gaussian distribution,exponential dis-tribution,and uniform distribution,and achieves higher positioning accuracy compared to the other algorithm.
wireless sensor networknon-line-of-sightline of sightinteractive multi-modelKal-man filterresidual selection
NETL
NSTL
万方数据
