多段正弦信号频谱融合法(简称"原融合算法")是提高低信噪比条件下正弦信号频率估计精度的一条有效途径,具有重要研究意义和应用价值。为满足雷达、声纳、电子对抗等实时性要求较高的频率估计应用需求,提出多段正弦信号快速频...多段正弦信号频谱融合法(简称"原融合算法")是提高低信噪比条件下正弦信号频率估计精度的一条有效途径,具有重要研究意义和应用价值。为满足雷达、声纳、电子对抗等实时性要求较高的频率估计应用需求,提出多段正弦信号快速频谱融合算法。该方法通过设计离散时间傅里叶变换(Discrete Time Fourier Transform,DTFT)快速算法、降维处理加权融合频谱矩阵和1/3主瓣相关性分析处理等措施来降低算法计算量,提高实时性。重点对上述三项措施的原理进行了阐述与分析。计算量对比和仿真实验表明,多段正弦信号快速频谱融合算法在精度损失极小的前提下,能够大幅降低计算量;在信噪比极低的情况下(SNR≤-13 dB),其性能略优于原融合算法。展开更多
This paper presents an analysis on and experimental comparison of several typical fast algorithms for discrete wavelet transform (DWT) and their implementation in image compression, particularly the Mallat algorithm, ...This paper presents an analysis on and experimental comparison of several typical fast algorithms for discrete wavelet transform (DWT) and their implementation in image compression, particularly the Mallat algorithm, FFT-based algorithm, Short- length based algorithm and Lifting algorithm. The principles, structures and computational complexity of these algorithms are explored in details respectively. The results of the experiments for comparison are consistent to those simulated by MATLAB. It is found that there are limitations in the implementation of DWT. Some algorithms are workable only for special wavelet transform, lacking in generality. Above all, the speed of wavelet transform, as the governing element to the speed of image processing, is in fact the retarding factor for real-time image processing.展开更多
基金国家重点基础研究发展规划( 973)( the National Grand Fundamental Research 973 Program of China under Grant No.2004CB318000)国家自然科学基金( the National Natural Science Foundation of China under Grant No.60133020, No.10671002, No.10771002)+1 种基金 浙江大学CAD&CG国家重点实验室开放课题( No.A0503) 澳门科技发展基金( No.045/2006/A)
文摘多段正弦信号频谱融合法(简称"原融合算法")是提高低信噪比条件下正弦信号频率估计精度的一条有效途径,具有重要研究意义和应用价值。为满足雷达、声纳、电子对抗等实时性要求较高的频率估计应用需求,提出多段正弦信号快速频谱融合算法。该方法通过设计离散时间傅里叶变换(Discrete Time Fourier Transform,DTFT)快速算法、降维处理加权融合频谱矩阵和1/3主瓣相关性分析处理等措施来降低算法计算量,提高实时性。重点对上述三项措施的原理进行了阐述与分析。计算量对比和仿真实验表明,多段正弦信号快速频谱融合算法在精度损失极小的前提下,能够大幅降低计算量;在信噪比极低的情况下(SNR≤-13 dB),其性能略优于原融合算法。
基金the Natural Science Foundation of China (No.60472037).
文摘This paper presents an analysis on and experimental comparison of several typical fast algorithms for discrete wavelet transform (DWT) and their implementation in image compression, particularly the Mallat algorithm, FFT-based algorithm, Short- length based algorithm and Lifting algorithm. The principles, structures and computational complexity of these algorithms are explored in details respectively. The results of the experiments for comparison are consistent to those simulated by MATLAB. It is found that there are limitations in the implementation of DWT. Some algorithms are workable only for special wavelet transform, lacking in generality. Above all, the speed of wavelet transform, as the governing element to the speed of image processing, is in fact the retarding factor for real-time image processing.
