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Joint Range Estimation Using Single Carrier Burst Signals for Networked UAVs.

Pan, X., Yan, C. and Zhang, J., 2021. Joint Range Estimation Using Single Carrier Burst Signals for Networked UAVs. IEEE Access, 9, 42533-42542.

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DOI: 10.1109/ACCESS.2021.3066415


The localization accuracy demand is ever growing in UAV communication networks. We propose a joint coarse and fine range estimation method using single carrier burst signals with two samples per symbol for UAV networks. The coarse estimation of our joint estimation method exploits multiple preamble symbols for flexible single-carrier frequency-domain equalization (SC-FDE) frame structures to calculate correlation metrics, which are insensitive to frequency offset due to the differential correlation operation. Then, we propose a fine range estimation method using only two samples per symbol with expectation relying on shaping or matched filter. Furthermore, we derive the performance bounds for the ranging system using both raised cosine (RC) and better than raised-cosine (BTRC) pulses. Finally, extensive simulations are conducted to validate the proposed method in terms of estimate bias and variance for different modulations, shaping filters, and fading channels. Our simulation results show that, the root mean square errors of proposed ranging method can reach the order of centimeter at medium-to-high signal-to-noise ratio (SNR) region, whereas the case using BTRC filter is capable of enhancing the ranging performance at low SNRs.

Item Type:Article
Additional Information:Funding Agency: 10.13039/501100012129-Youth Outstanding Talent Support Program of China Aerospace Science and Technology Corporation; Key Project of the Key Research Development Program, Ministry of Science and Technology, China; 10.13039/501100001809-National Natural Science Foundation of China; Innovative Talent of Colleges and University of Henan Province;
Uncontrolled Keywords:UAV networks; range estimation; SC-FDE; oversampled signals; two samples per symbol; fading channel; burst signals
Group:Faculty of Science & Technology
ID Code:35275
Deposited By: Symplectic RT2
Deposited On:15 Mar 2021 10:55
Last Modified:14 Mar 2022 14:26


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