SUMMARYRepresenting the channel varying rate and the mobile speed of a mobile terminal directly, Doppler shift is an important parameter in vehicular mobile communications and therefore is widely used in mobile target detection and adaptive applications. Hence, this paper puts forward an accurate Doppler shift estimator in mobile communications with high vehicle speeds, which can also be treated as a vehicular speed estimator due to the well-known relation between the Doppler shift and the mobile speed. Specifically, the proposed estimator is based on the channel level crossing rate, and an iterative process is presented to achieve signalto-noise ratio (SNR) insensitive estimates in accordance with the level crossing rate estimation error analysis. Moreover, we prove the convergency of the iterative Doppler shift estimator in theory. Computer simulations conducted under a wide range of noise corruption clearly show that the proposed estimator substantially outperforms several existing estimators in terms of accuracy and achieves a good SNR-insensitive performance in a wide range of velocities and SNRs.
SUMMARYWe put forward a novel frequency offset estimator for burst orthogonal frequency division multiplexing (OFDM) systems under double selective fading channels. With the help of pilot bits and channel estimates in frequency domain, we calculate the delay power spectrum (DPS) of multi-path channels and estimate the integer carrier frequency offset (CFO), in which the FFT-realized (Fast Fourier Transform) algorithm is computation effective. Additionally, the pseudo noise (PN) sequence is proposed to act as a pilot signal to extend the estimation range. Yet the algorithm uses pilots instead of preambles, resulting in higher spectrum efficiency than conventional methods. Simulations show high accuracy and robustness in a wide range of terminal velocities (time-selectivity) and signal-to-noise ratios (SNRs).
The theory of compressive sensing (CS) enables the reconstruction of a sparse signal from highly compressed data. However, in many applications, we are ultimately interested in information retrieval rather than signal reconstruction. In this paper, we study the problem of multi-objects classification in compressive sensing systems. Theoretical error bounds are derived based on the analysis of classical compressive classification. The optimal projection matrix design problem is studied and an algorithm is derived to solve the corresponding problem. Application in the identification of license plate numbers is considered and simulation results show that the projection measurement obtained using the proposed algorithm significantly improve the classification performance in terms of classification error rate.
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