WSN下基于二进制鲸鱼优化压缩感知重构的多目标定位
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合肥工业大学电气与自动化工程学院 合肥 230000

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TP391 ;TN98

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国家自然科学基金(51577046),国家自然科学基金重点项目(51637004),国家重点研发计划“重大科学仪器设备开发”项目(2016YFF0102200)。


Multi-target localization based on binary whale optimization compressive sensing reconstruction under WSN
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    摘要:

    为提高基于压缩感知的无线传感器网络的定位精度和抗噪性,提出一种无线传感器网络下二进制鲸鱼优化算法压缩感知重构的多目标定位算法。首先将连续的鲸鱼优化算法离散在二进制空间中,并保留鲸鱼捕食策略的特性,再将二进制鲸鱼优化算法用于压缩感知信号重构,最终实现了无线传感器网络下的多目标定位。实验结果对比表明,相比于传统的压缩感知重构算法,该算法在目标数为8,信噪比为5dB时,平均定位误差控制在1.25m以内,具有良好的抗噪性,且计数性能和定位性能优于贪婪匹配追踪算法、传统的L1范数求解算法。

    Abstract:

    In order to improve the localization accuracy and anti-noise performance of wireless sensor network based on compressive sensing, a multi-target localization algorithm based on compressive sensing reconstruction of binary whale optimization algorithm in wireless sensor network is proposed. Firstly, the continuous whale optimization algorithm was discretized in the binary space, and the characteristics of whale preying strategy were retained. Then, the binary whale optimization algorithm was used to reconstruct the compressed sensing signal, and finally the multi-target positioning under the wireless sensor network was realized. The comparison of experimental results shows that compared with the traditional compressive sensing reconstruction algorithm, when the target number is 8 and the SNR is 5dB, the average positioning error of this algorithm is controlled within 1.25m and has good anti-noise performance. In addition, the counting performance and positioning performance are better than the greedy matching tracking algorithm and the traditional L1 norm solving algorithm.

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  • 收稿日期:2019-04-26
  • 最后修改日期:2019-08-20
  • 录用日期:2019-08-21
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