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针对信号稀疏度在大多数情况下时变且未知的问题,提出了一种实时信号稀疏度预测及最优采样速率确定机制.利用离散时间马尔科夫链对信号稀疏度进行建模,分析信号稀疏度各状态之间变化的规律,根据当前状态预测下一个采样周期内信号的稀疏度状态及概率.此外,基于预测结果,综合考虑采样过程中的能量消耗和信号重构的精确度,以最大化预期收益为目的,提出一种控制机制来确定最优采样速率.该机制能够达到能量消耗和精确度之间的折中.仿真证明,所提出的基于离散时间马尔科夫链的动态控制机制与现有控制机制相比在采样性能方面具有较大的优势.
Aiming at the problem that the signal sparsity is time-varying and unknown in most cases, a real-time signal sparseness prediction and the optimal sampling rate determination mechanism are proposed.The signal sparsity is modeled using discrete-time Markov chain to analyze the signal According to the prediction results, considering the energy consumption in the sampling process and the accuracy of signal reconstruction, we use the current state to predict the signal sparsity state and probability in the next sampling period This paper proposes a control mechanism to determine the optimal sampling rate, which can achieve a trade-off between energy consumption and accuracy.The simulation results show that the proposed dynamic control based on discrete-time Markov chain Compared with the existing control mechanism, the mechanism has great advantages in sampling performance.