On the Particle-Assisted Stochastic Search Mechanism in Wireless Cooperative Localization
On the Particle-Assisted Stochastic Search Mechanism in Wireless Cooperative Localization
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DOI：
10.1109/twc.2016.2545665
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发表时间：
2016-03
影响因子：
10.4
通讯作者：
Bingpeng Zhou;Qingchun Chen
中科院分区：
文献类型：
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作者：
Bingpeng Zhou;Qingchun Chen
无线协同定位在位置感知服务中起着关键作用。然而，由于非线性测量函数和/或非高斯系统干扰，其目标函数（如后验概率函数）通常是非凸的。此外，由于不可避免的参考节点定位误差，关联目标函数通常是难以处理的，这进一步增加了协同定位的复杂性。本文提出了一种新的粒子辅助随机搜索（PASS）算法来实现协同定位。该方法在给定非凸目标函数的情况下，通过搜索粒子、检测粒子和建议粒子的辅助，在概率上找到全局最优解。此外，PASS算法还利用其建议粒子来利用协同定位中参考节点位置的不确定性。给出了相关的crmer - rao下界（CRLB）、定位误差传播、计算复杂度和收敛性来评估基于pass的协同定位。最后，对基于接收信号强度的定位进行了仿真，验证了所提PASS方法的有效性。
The wireless cooperative localization plays a key role in location-aware service. However, its objective function, e.g., the posteriori probability function, is commonly nonconvex due to nonlinear measurement function and/or non-Gaussian system disturbance. Moreover, due to the unavoidable reference node location error, the associated objective function is commonly intractable, which further complicates the cooperative localization. In this paper, a novel particle-assisted stochastic search (PASS) algorithm is proposed to realize the cooperative localization. Given a nonconvex objective function, the proposed PASS method can find out the global optimum in probability, assisted with its search particles, detection particles, and proposal particles. In addition, the PASS algorithm can harness the reference node location uncertainties in cooperative localization, by employing its proposal particles. The associated Cramer-Rao lower bound (CRLB), localization error propagation, computational complexity, and convergence properties are also presented to assess the proposed PASS-based cooperative localization. Finally, received signal strength-based localization is simulated to validate the effectiveness of the proposed PASS approach.