基于迭代收缩的蜂窝网络区间双曲定位

Biao Zhou;Xuan Su;Min Pang;Le Yang
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摘要

使用通常采用的代数方法获得的到达时间差(TDOA)定位结果缺乏不确定性信息。在这封信中,我们建议将区间计算纳入基于 TDOA 的双曲线定位中,并采用迭代收缩策略生成区间定位结果,以保证包含真实解。在新开发的算法中,区间 TDOA 测量被视为区间双曲线,并使用二分法将其划分为不重叠的矩形集。确定这些矩形的交集后,通过迭代收缩过程更新目标位置区间,缩小位置区间直至收敛。通过仿真来评估所提出的区间双曲定位算法的准确性、不确定性和有效性。结果表明,新算法可以在高水平高斯噪声下达到 Cramér-Rao 下限,并以接近 1 的概率产生包围真实目标位置的定位区间。
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Interval Hyperbolic Localization Based on Iterative Contraction for Cellular Networks
Time difference of arrival (TDOA) positioning results obtained using commonly adopted algebraic methods lack uncertainty information. In this letter, we propose to incorporate interval computation into TDOA-based hyperbolic localization and employ an iterative contraction strategy to generate interval positioning results that guarantee to enclose the true solution. With the newly developed algorithm, interval TDOA measurements are considered as interval hyperbolas and partitioned into non-overlapping sets of rectangles using the dichotomy method. The intersection of these rectangles is determined and applied to update the target location interval through an iterative contraction process to shrink the location interval until convergence. Simulations are conducted to evaluate the accuracy, uncertainty and validity of the proposed interval hyperbolic localization algorithm. It is shown that the new algorithm can attain the Cramér-Rao lower bound under high level Gaussian noise and produce, with a probability close to one, positioning intervals enclosing the true target location.
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2024 Index IEEE Networking Letters Vol. 6 Table of Contents IEEE Networking Letters Publication Information IEEE Networking Letters Society Information Editorial SI on Advances in AI for 6G Networks
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