Bandwidth efficient channel estimation method for airborne hyperspectral data transmission in sparse doubly selective communication channels

Vahid Vahidi; Ebrahim Saberinia; Emma E. Regentova

doi:10.1117/1.JRS.11.046026

28 December 2017 Bandwidth efficient channel estimation method for airborne hyperspectral data transmission in sparse doubly selective communication channels

Vahid Vahidi, Ebrahim Saberinia, Emma E. Regentova

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 046026 (December 2017). https://doi.org/10.1117/1.JRS.11.046026

Abstract

A channel estimation (CE) method based on compressed sensing (CS) is proposed to estimate the sparse and doubly selective (DS) channel for hyperspectral image transmission from unmanned aircraft vehicles to ground stations. The proposed method contains three steps: (1) the priori estimate of the channel by orthogonal matching pursuit (OMP), (2) calculation of the linear minimum mean square error (LMMSE) estimate of the received pilots given the estimated channel, and (3) estimate of the complex amplitudes and Doppler shifts of the channel using the enhanced received pilot data applying a second round of a CS algorithm. The proposed method is named DS-LMMSE-OMP, and its performance is evaluated by simulating transmission of AVIRIS hyperspectral data via the communication channel and assessing their fidelity for the automated analysis after demodulation. The performance of the DS-LMMSE-OMP approach is compared with that of two other state-of-the-art CE methods. The simulation results exhibit up to 8-dB figure of merit in the bit error rate and 50% improvement in the hyperspectral image classification accuracy.

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Vahid Vahidi, Ebrahim Saberinia, and Emma E. Regentova "Bandwidth efficient channel estimation method for airborne hyperspectral data transmission in sparse doubly selective communication channels," Journal of Applied Remote Sensing 11(4), 046026 (28 December 2017). https://doi.org/10.1117/1.JRS.11.046026

Received: 27 July 2017; Accepted: 29 November 2017; Published: 28 December 2017

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