基于速度差補(bǔ)償?shù)碾p頻連續(xù)波雷達(dá)室內(nèi)人體定位方法
doi: 10.11999/JEIT160861
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2.
(武漢理工大學(xué)信息工程學(xué)院 武漢 430070) ②(長沙學(xué)院數(shù)學(xué)與計算機(jī)科學(xué)系 長沙 410022)
基金項目:
國家自然科學(xué)基金(61373042, 61502361)
Indoor Human Localization Method of Dual Frequency Continuous Wave Radar with Velocity Deviation Compensation
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2.
(School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)
Funds:
The National Natural Science Foundation of China (61373042, 61502361)
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摘要: 目前,用于室內(nèi)定位的雷達(dá)技術(shù)為了達(dá)到高精度而使用高帶寬的調(diào)頻連續(xù)波,這樣不僅對設(shè)備要求高且雜波干擾大。為降低帶寬開銷,該文使用雙頻連續(xù)波比相測距機(jī)制來實現(xiàn)室內(nèi)人體定位。在該方法中復(fù)雜的室內(nèi)環(huán)境和人體變化的移動速度會造成頻譜擴(kuò)展,導(dǎo)致有效信號信噪比降低,能量發(fā)散甚至出現(xiàn)峰值誤判,直接降低測速和定位的精度。由此,提出應(yīng)用于室內(nèi)環(huán)境中的基于雙頻連續(xù)波比相測距的局部速度差補(bǔ)償算法,以校準(zhǔn)頻域信號,獲取高精度的速度與距離信息。實驗結(jié)果表明在設(shè)備功率提供的測距范圍內(nèi)固定位置均方根誤差在9~14 cm內(nèi),與已有調(diào)頻連續(xù)波有同等級的測距精度,最終實現(xiàn)了低帶寬下的高精度人體定位。同時該系統(tǒng)的算法復(fù)雜度較低,能更好地適用于人體軌跡的實時跟蹤。
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關(guān)鍵詞:
- 雙頻連續(xù)波雷達(dá) /
- 速度差補(bǔ)償 /
- 定位跟蹤
Abstract: The radar technology used in indoor localization prefers wide bandwidth frequency modulated continuous wave for high accuracy, yet this way needs specific device and suffers from clutters. In order to reduce the bandwidth overhead, the indoor human localization is implemented based on dual frequency continuous wave phase ratio. However, the receive signal spectrum spreads due to the complex indoor environment and the changing moving speed. The spectrum spread will leads to SNR reduction, energy divergence and wrong peak value, which decrease velocity measuring and localization accuracy. To improve the location accuracy, the frequency domain signal is calibrated with the proposed partial velocity deviation compensation algorithm in the dual frequency phase ratio localization. The experiment results show that the root mean square error of the distance measuring is as high as 9 ~ 14 cm in low bandwidth, which is parallel to the state of art. Moreover, the indoor localization and tracking can work in real time by using the proposed low complexity algorithm. -
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