Underwater Acoustic Sensor Networks: Target Size Detection and Performance Analysis
Underwater Acoustic Sensor Networks: Target Size Detection and Performance Analysis
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DOI：
10.1109/icc.2008.593
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
Q. Liang;Xiuzhen Cheng
中科院分区：
文献类型：
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作者：
Q. Liang;Xiuzhen Cheng
提出了一种基于水声传感器网络的水下目标尺寸最大似然估计算法。理论分析表明，我们的水下传感器网络可以大大降低目标尺寸估计的方差。我们证明了我们的ML估计是无偏的，参数估计的方差符合Cramer-Rao下界。仿真结果进一步验证了这些理论结果。
In this paper, we propose a maximum-likelihood (ML) estimation algorithm for underwater target size detection using underwater acoustic sensor networks. Theoretical analysis demonstrates that our underwater sensor network can tremendously reduce the variance of target size estimation. We show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer-Rao lower bound. Simulations further validate these theoretical results.