摘要: |
受海况、浸水、结冰、对星等因素影响,飞行员海上遇险后报位可靠性较低,工作时长较短。为此,提出一种自适应功率退避方法,在对报位影响因素分析基础上,利用深度神经网络,通过对经纬度、高度、俯仰角、方向、加速度、速度、浸水、信号、电量等多维度数据进行学习,拟合出覆冰、浪涌、液面等信号特征与环境特征联想模型,自适应控制信号发射时机,实现功耗的有效控制。试验表明,该方法将通信成功率及工作时长分别由36.3%、6.0 h提升至73.3%、8.6 h。 |
关键词: 海上遇险 功率退避 卫星导航 “北斗”救生终端 神经网络 环境预测 |
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An Adaptive Power Avoidance Algorithm for Beidou Life-saving Terminal |
YANG Hang,CHENG Chunhua,LI Honglie,FANG Fang |
(1.Naval Aviation University Qingdao Campus,Qingdao 266041,China;1.Naval Air University Qingdao Campus,Qingdao 266041,China;2.Naval Qingdao Special Service Convalescent Center,Qingdao 266000,China) |
Abstract: |
Affected by sea conditions,water immersion,icing,satellite pointing and so on,the life-saving communication positioning terminal carried by the pilot in distress is less reliable and has less endurance.To solve above problems,an adaptive power avoidance method is proposed.According to the analysis of the influencing factors of the distress location report,the deep neural network is used to learn the multi-dimensional data such as longitude,latitude,height,pitch angle,direction,acceleration,speed,water immersion,signal,battery,etc.,fit the model of signal characteristics and environmental characteristics such as icing,surge,liquid level,etc.,adaptively control the timing of signal transmission,and achieve the effective control of power consumption.Experiments show that the communication success rate is increased from 36.3% to 73.3%,and working time increased from 6.0 hours to 8.6 hours. |
Key words: maritime distress power avoidance satellite navigation Beidou life-saving terminal deep neural network environmental prediction |