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首页> 外文期刊>International Journal of Distributed Sensor Networks >Hybrid Fingerprinting-EKF Based Tracking Schemes for Indoor Passive Localization
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Hybrid Fingerprinting-EKF Based Tracking Schemes for Indoor Passive Localization

机译：基于混合指纹-EKF的室内被动定位跟踪方案

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This paper investigates a combination of fingerprinting (FP) and extended Kalman filter (EKF) based tracking aiming to tackle conventional problems related to implementation of either tracking or fingerprinting separately. One of the common drawbacks of FP belongs to large data size and consequent large search space. By taking advantage of latest position estimate got from EKF, a virtual surveillance area (VSA) is defined around the estimate. The dimension of this defined surveillance area is much smaller than the size of indoor environment. Consequently, there will be a possibility for FP to be applied in larger areas maintaining the possibility of adding necessary grid points in order to achieve a desired localization performance. Additionally, in order to improve accuracy of ranging, we investigate the impact of a priori knowledge related to the clusters impulse responses and other features; the applied so calledsoftranging algorithm for time of arrival (TOA) estimation is modified in order to take advantage of this a priori information and to make its decision variables more accurate. Simulation results show a promising performance improvement via using the proposed hybrid tracking technique and applying a priori information to soft ranging. The tradeoff is along a reasonable increased implementation complexity.
机译：本文研究了基于指纹的（FP）和基于扩展卡尔曼滤波器（EKF）的组合跟踪，旨在解决与分别实现跟踪或指纹相关的常规问题。 FP的常见缺点之一是数据量大，因此搜索空间大。利用从EKF获得的最新位置估计，在估计周围定义了一个虚拟监视区域（VSA）。定义的监视区域的尺寸远小于室内环境的尺寸。因此，将有可能将FP应用在较大的区域中，从而保持增加必要的网格点以实现所需定位性能的可能性。另外，为了提高测距的准确性，我们调查了与群集脉冲响应和其他特征相关的先验知识的影响;修改了应用的所谓的到达时间（TOA）估计软调整算法，以利用此先验信息并使决策变量更准确。仿真结果表明，通过使用提出的混合跟踪技术并将先验信息应用于软测距，可以改善性能。折衷是沿着合理增加的实现复杂性进行的。

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