Rubem Vasconcelos Pacelli , Rodrigo de Lima Florindo , Felix Antreich , Antônio Macilio Pareira de Lucena
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引用次数: 0
Abstract
Audio frequency-shift keying (AFSK) is a widely adopted modulation scheme for CubeSat systems due to its favorable bandwidth efficiency and implementation simplicity. However, coherent detection usually is avoided because synchronization impairments, caused by intense line-of-sight (LOS) dynamics inherent in low Earth orbit (LEO), may significantly degrade the bit error rate (BER). This paper presents a new all-digital coherent AFSK demodulator based on a Kalman filter (KF) for carrier phase and timing delay synchronization and the Viterbi algorithm for bit detection. The Viterbi algorithm is employed for maximum likelihood sequence detection, and the detected bit statistics are fed back to the KF to estimate phase shift, Doppler frequency shift, and Doppler drift induced by the LOS dynamics. Original mathematical analyses are derived to provide a theoretical foundation for the proposed demodulator's operation, specifically addressing its synchronization accuracy in dynamic LEO environments. The proposed demodulator is evaluated considering an additive white Gaussian noise channel with real CubeSat orbits. The performance results obtained through computer simulations demonstrate that the proposed model can withstand such scenarios with a gain of 5 dB in terms of BER compared to the conventional noncoherent AFSK demodulator. The KF performance is assessed using a moving root-mean-square error (MRMSE) statistic and the trace of its state error covariance matrix estimate.
期刊介绍:
Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.
The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:
• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,