Universal Localization Algorithm Based on Beetle Antennae Search in Indoor Environment
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摘要: 在复杂的室内环境中,测得的接收信号强度(RSS)值会出现不同程度的波动,导致无法准确地刻画出无线信号传播模型。为了解决这个问题,在基于Wi-Fi测距定位模型下,该文提出一种普适的粗粒度定位方法。该方法通过对测量到的RSS值进行拟合,以此获取信号的传播模型;在此基础上计算出未知节点与接入点(AP)的距离,再利用天牛须算法实现未知节点定位,通过仿真验证此传播模型的性能以及该优化算法的有效性。Abstract: In complex indoor environment, the measured Received Signal Strength (RSS) values will fluctuate in different degrees, which lead to inaccurate characterization of wireless signal propagation model. To solve this problem, a universal coarse grained localization method is proposed based on the Wi-Fi ranging location model. This method gets the signal propagation model by fitting the measured RSS value. On this basis, the distance between the unknown node and the Access Point (AP) is calculated, then the location of the unknown node is realized by the beetle antennae search algorithm. The performance of the propagation model and the effectiveness of the optimization algorithm are verified by simulation.
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表 1 本文算法与经典算法对比
真实位置 测量位置 误差(m) 收敛速度 BAS-DSPM (0, 0) (0.7123, 1.4437) 1.6098 迭代27次趋于平稳 PSO-DSPM (0, 0) (1.3654, –1.8031) 2.2617 迭代108次趋于平稳 PSO-经典测距模型 (0, 0) (–2.0462, 3.4623) 4.0217 迭代98次趋于平稳 -
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