逼近采样率极限的自适应追踪采样
Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling
摘要：
在各种监测应用中经常采集测量样本。为了降低感测成本，期望在使用较少样本的同时实现更好的感测质量。压缩感知（CS）技术发现它的作用时，被采样的信号满足一定的稀疏性要求。在本文中，我们调查的可能性和基本技术，可以进一步减少涉及传统CS理论的样本数量，利用基于学习的非均匀自适应采样。基于一个典型的信号检测应用程序，我们说明和评估我们的算法，个人追逐和质心追逐，不同的分布特征的信号的性能。我们提出的基于学习的自适应采样方案补充了CS领域的现有努力，不依赖于任何特定的信号重建技术。仿真结果表明，与传统的稀疏采样方法相比，该算法在保证信号重构精度的前提下，允许$46\%$使用更少的采样数，并在相同噪声条件下实现了高达$57\%$更小的信号重构误差.
英文摘要：
Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow $46\%$ less number of samples for accurate signal reconstruction and achieve up to $57\%$ smaller signal reconstruction error under the same noise condition.
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