水下认知网络中的哺乳动物定位测速算法

Localization and speed measurement algorithm targeting marine mammals for underwater cognitive acoustic networks

针对水下认知声学网络(UCAN)中的环境感知问题,提出一种针对海洋哺乳动物的被动定位(PLM)算法及相应的基于多普勒效应的测速算法SMD。PLM算法基于海洋哺乳动物发声信号声源级范围,结合接收信号强度,运用检索筛选的方法推算发声位置。SMD在PLM定位的基础上,利用接收生物信号的多普勒效应对其运动测速。实验结果表明,PLM与SMD均能达到较高的精度,其中PLM算法的平均定位误差随海豚游速的增加而增加,其平均值约为10 m,定位成功率可达到90%。PLM和SMD结合,可较准确地估计海洋哺乳动物的运动区域。

In view of the problem of environmental sensing in Underwater Cognitive Acoustic Networks （UCAN）, a Passive Localization algorithm targeting Marine Mammals （PLM） and Speed Measurement algorithm based on Doppler effect （SMD） were proposed. PLM uses the method of retrieval and screening with received signal power to localize marine mammals based on the source level range of their signals. SMD calculates speed using Doppler effect of the received signals on the basis of PLM localization. The experimental results show that PLM and SMD can achieve high accuracy. The average error of PLM increases with the increase of dolpine＇s speed, and its mean value is about 10 m. Success rate of localization using PLM can be 90%. The combination of PLM and SMD can help to estimate the movement area of marine mammals accurately.