宋喜佳,杨亮,吕燚.基于压缩感知和迭代优化策略的LED参数的快速估计[J].光电子激光,2015,26(6):1062~1066 |
基于压缩感知和迭代优化策略的LED参数的快速估计 |
Fast estimating LED parameters based on compress sensing and iterative optimizing scheme |
投稿时间:2014-09-18 |
DOI: |
中文关键词: 压缩感知(CS) 参数估计 脉冲宽度调制(PWM) 迭代优化 |
英文关键词:compress sensing (CS) parameter estimation pulse-width modulation (PWM) iter ative optimization |
基金项目:广东省高校优秀青年创新人才培养计划(2013LYM0104)、中山市科技计划(2013A3FC0289)和电子科技大学中山学院博士启动基金(413YKQ03)资助项目 , 杨亮, 吕燚 (电子科技大学 中山学院,广东 中山 528402) |
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中文摘要: |
为快速有效地估计LED智能照明中的未知参数, 首先,将解析表达式中由频率偏移-相位构成的二维平面空间在 水平和垂直两个方向上离散化,构成网状格点空间,并根据测量得到的数据在格点空间具有 稀疏性的特 点建立稀疏模型;然后,在稀疏模型的基础上采用最新的压缩感知(CS)技术,对脉冲宽度调 制PWM信号进行伪 随机线性测量;并利用基追踪(BP)算法重建未知参数;最后,采用迭代细分网格策略对稀 疏模型 进行优化,减少估计误差。实验结果表明,在理想情况下,本文方法仅使用相当于奈奎斯特 采样定理所要 求的0.23%的采样值,就可以快速准确地重建未知参数;经过3次迭代 优 化后,算法的均方误差(RMSE)一般不大于 10-4;算法具有良好的抗噪性能,在信噪比(S NR)大于-160dB时表现出良好的鲁棒性。 |
英文摘要: |
Pulse-width modulation (PWM) technolo gy is mainly used to drive LED in intelligent illumination fields,thus the LED emitting signal is approximately rectangular pulse array at normal state ,and unknown parameters (amplitude,frequency offset and phase) are usually contained in the analytical expression. How to estimate these unknown parameters rapidly and effectively is the main res earch contents of this paper.To achieve this purpose,the frequency offset-phase space is firstly discretized into ret iculated grids space along the horizontal and vertical directions,and a sparse model is constructed based on the characteristics that the measured data are sparse in the grids space.Moreover,the latest compressed se nsing technology is involved, and a small number of data are acquired by pseudo random linear measurement.The n,the unknown parameters are rapidly reconstructed by means of basic pursuit (BP) algorithm.Finally,an it erative refinement of grids scheme is also introduced to optimize the sparse model so as to suppress the estimatio n error effectively.Experimental results indicate that the method presented in this paper can fast reconstruct the unknown parameters using only 0.23% of samples,which is needed in the traditional Nyquist sampling theorem i n an ideal situation.After 3times of iterative optimizations,the root mean squared error (RMSE) of our metho d is generally no greater than 10-4.In addition,this algorithm has good anti-noise performance,and it is robust when signal-to-noi se ratio (SNR) is higher than -160dB. |
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