定位是无线传感器网络技术和应用的重要基础.基于接收信号强度(received signal strength,简称RSS)的定位方法是实际应用中比较重要的定位方法.考虑到实际应用中不同地点RSS 测量信号的方差有所不同这一特点,运用最大概率似然理论,建立了更加符合实际的基于RSS 测量的概率定位模型.对于模型中目标表达式高度非线性不好求解的特点,运用进化计算理论设计出符合传感器通信特征的定位算法(location in probability maximum withevolutionary algorithm,简称PMEA)求解概率可能性最大的位置坐标点,并用随机过程在数学上证明了算法的收敛性.最后,通过对实际公开数据集的实验,证实所提出的概率模型和PMEA 算法确实能够提高RSS 测距定位的精度.

Location is a crucial part of wireless sensor networks technologies and applications. RSS-based (based on received signal strength) location estimations play an important role in practice. Considering the characteristic that the variance of RSS varies in different estimation points, a practical RSS-based probabilistic model is tailored and established according to the probability-based maximum likelihood in this paper. Next, taking the highly nonlinear characteristic of the object function in this probabilistic model, a location approach using the probability maximum with evolutionary algorithm (PMEA), which corresponds more to the characteristic of communication of the sensors, is proposed to find out the maximum likelihood point. The convergence is proved by the stochastic process. The results of the proposed algorithm, when implemented in a public dataset, show that this proposed probabilistic model and PMEA outperform existing solutions in terms of RSS-based location estimation accuracy.

国家自然科学基金(60873099, 61272119, 61203372); 国家教育部博士点基金(20090203110005)