混合噪声下移动受限水声传感器网络自定位算法

现有的水声传感器网络定位算法需要信标节点辅助定位,测距噪声服从高斯分布,定位成本高,精度低,对此,提出一种混合测距噪声下基于最大后验概率的自定位算法. 首先对受限移动节点的移动模式进行建模以获取节点位置的先验信息,测量节点间距离并基于加性和乘性混合噪声构建似然函数,在贝叶斯框架下将节点位置的先验与似然信息进行融合,通过最大化后验概率得到定位目标函数;然后利用BFGS拟牛顿法对目标函数进行优化求解. 仿真结果表明,相比同类定位方法,所提方法无需信标节点,定位精度高,收敛速度快,且对测距噪声的变化具有鲁棒性.

Self-Localization Algorithm for Drifting-Restricted Underwater Acoustic Sensor Networks under Mixed Noise

Existing localization algorithms for underwater acoustic sensor networks need the presence of beacon nodes and assume that measurement noises follow Gaussian distributions, resulting in high cost and low accuracy. To address these problems, a self-localization algorithm based on maximum a posteriori is proposed for drifting-restricted underwater acoustic sensor networks under mixed measurement noises. We analyze nodes' mobility patterns to obtain the prior knowledge for localization, and characterize distance measurements under the assumption of additive and multiplicative noises as the likelihood information for localization. Under the Bayesian framework, the priori and likelihood information are fused to derive localization objective function by maximum a posteriori probability. Then Broyden, Fletcher, Goldfarb and Shanno quasi-Newton method is resorted to solve the objective function. The simulation results show that compared with similar localization methods, the proposed method does not need the presence of beacon nodes, and it has the advantages of high localization accuracy, fast convergence speed, and being robust to changes in measurement noises.