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震后特殊环境下压埋人员精确定位算法

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针对目前对震后压埋人员定位精度较低、探测设备成本高且易受环境影响等不足,提出适用于压埋环境特性的压埋人员手机WiFi定位方法,通过衰减因子模型对WiFi探针获取的RSSI数据进行距离解算,结合简化压埋环境内部信号传输方式,采用高斯-卡尔曼滤波对获取的RSSI数据进行处理,通过模型测定的距离,利用改进附有参数的加权最小二乘平差方法,结合粒子群优化算法,最终得到压埋人员手机平面坐标位置.研究结果表明,该方法具有较高精度,在10 mx10 m范围内其平面坐标定位误差在0.3 m左右,可为震后压埋人员应急救援提供辅助决策.
Algorithm of Accurate Location of Buried Personnel in Special Environment after Earthquake
In view of the current shortcomings of the positioning accuracy of the buried people after the earthquake,the high cost of the detection equipment and the vulnerability to environmental impact,in this paper we propose a WiFi Positioning Method to search for the buried people's mobile phones,which is suitable for the characteristics of the signals from buried environment.The RSSI data obtained by the WiFi probe is calculated by the attenuation factor model.Combined with the simplified internal signal transmission mode of the buried environment,the obtained RSSI data is processed by Gauss Kalman filter.Through the distance measured by the model,using the improved weighted least square adjustment method with parameters,combined with particle swarm optimization al-gorithm,it is capab le to obtain the plane coordinate position of the buried personnel's mobile phone.The experimental results show that this method has high accuracy,with the average error of plane coordinate positioning about 0.3 m within the range of 10 m x 10 m,which can provide auxiliary decision-making for emergency rescue of buried personnel after earthquake.

Buried environmentAttenuation factor modelPositioning of embedded personnelGaussian Kalman filterParticle swarm optimization algorithm

成鹏、肖东升

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西南石油大学,土木工程与测绘学院,成都 610500

西南石油大学,测绘遥感地理信息防灾应急研究中心,成都 610500

压埋环境 衰减因子模型 压埋人员定位 高斯-卡尔曼滤波 粒子群优化算法

国家自然科学基金西南石油大学测绘遥感地信与防灾应急青年科技创新团队四川省区域创新合作项目

517742502019CXTD07

2024

震灾防御技术
中国地震台网中心

震灾防御技术

CSTPCD北大核心
影响因子:0.704
ISSN:1673-5722
年,卷(期):2024.19(1)
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