基于改进Elman神经网络的麻雀搜索室内定位方法

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI:10.1109/CCISP55629.2022.9974419

Tiansheng Shen, Wenying Chen, Binbin Zhu, Yan Wang, Yongliang Zhou, Xiuping Wang

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引用次数: 0

摘要

目前，室内定位算法得到了广泛的关注和研究。由于复杂的室内环境中存在多径效应，难以实现高精度定位。本文提出了一种基于Cat混沌映射的改进麻雀搜索算法(ISSA)的BLE(Bluetooth Low Energy)室内定位算法。首先用BDOR(Bilateral Direction Outlier Removal)算法去除异常数据，然后建立采集到的BLE数据数据库。然后用改进的卡尔曼滤波对剩余数据进行滤波。研究了ISSA-Elman模型来预测待测点的横坐标和纵坐标。实验结果表明，该算法在室内定位中具有较好的精度。最小位置误差接近2cm。

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An Indoor positioning method based on Improved Elman neural network using sparrow search

N owadays, indoor positioning algorithms have attracted comprehensive attention and research. Due to multipath effect in the complex indoor environment, it is difficult to position with high precision. In this paper a BLE(Bluetooth Low Energy) indoor positioning algorithm based on ISSA(Improved Sparrow Search Algorithm) with Cat chaotic mapping is proposed. Firstly the abnormal data are removed by BDOR(Bilateral Direction Outlier Removal) algorithm before establishing the database of the collected BLE data. Then the rest of data are filtered by improved Kalman filter. The ISSA-Elman models are studied to predict the horizontal and vertical coordinates of the point to be tested. Results of experiments reveal that the proposed algorithm performs precisely in indoor positioning. The minimal position error is nearly 2cm.

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来源期刊

2022 7th International Conference on Communication, Image and Signal Processing (CCISP)

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