西安交通大学机械制造系统工程国家重点实验室,西安,710049
网络首发:2015-08-10,
纸质出版:2015
移动端阅览
成玮, 张周锁, 何正嘉. 采用信息理论准则的信号源数估计方法及性能对比[J]. 西安交通大学学报, 2015,49(8):38-44.
Information Criterion-Based Source Number Estimation Methods with Comparison[J]. 2015, 49(8): 38-44.
成玮, 张周锁, 何正嘉. 采用信息理论准则的信号源数估计方法及性能对比[J]. 西安交通大学学报, 2015,49(8):38-44. DOI: 10.7652/xjtuxb201508007.
Information Criterion-Based Source Number Estimation Methods with Comparison[J]. 2015, 49(8): 38-44. DOI: 10.7652/xjtuxb201508007.
为了从机械系统观测混合信号中有效评估信号源的数目
以及解决数据点较大时贝叶斯信息准则(BIC)难以计算的问题
在剖析了3种信源数目估计准则(赤池信息准则(AIC)、最小描述长度(MDL)以及贝叶斯信息准则(BIC))的原理和算法的基础上
提出了基于对数函数修正的改进贝叶斯准则(IBIC)。该准则利用对数运算将BIC目标函数中的多参数指数运算转换为乘积运算
在不降低计算精度的条件下
显著改善了BIC准则的计算效率和工程应用性能。仿真实验分析表明:AIC与MDL具有近似的源数估计性能
对非线性调制成分非常敏感; 从能量角度分析
提出的新准则容忍非线性调制成分(非线性调制信号能量占观测信号总能量)能量比为5.15%
较AIC(0.07%)与MDL(0.08%)具有更好的鲁棒性能。壳体结构试验台声源数目估计实验表明
3种方法均可有效评估声源数目。本研究对于模态阶数选择、系统复杂度分析以及基于机械系统信号源分离的状态监测与故障诊断具有学术意义和工程应用价值。
To effectively evaluate the source number of mechanical systems from the measured mixed signals
and solve the calculating difficulty of BIC for large data points
three information criterion-based source number estimation methods
Akaike information criterion(AIC)
minimum description length(MDL)
and Bayesian information criterion(BIC)
are comparatively studied and an improved BIC
named IBIC
is proposed following an exponential function modification
which transforms the multi-parameter exponential calculating to multiplications. Without decreasing the accuracy
IBIC obviously improves the calculating efficiency and engineering application performances. The numerical case study results show that AIC and MDL obtain the similar performances on source number estimation
and they are both very sensitive to the nonlinear modulation effects. In respect to signal energy ratios
the proposed method has a robustness tolerance on nonlinear modulation effects for 5.15%
which is higher than that of AIC(0.07%)and MDL(0.08%). The results of source number estimation for acoustical signals of a test bed with shell structures show that all the three methods are effective for the given acoustical signals. This work benefits model order selection
complexity analysis of a system
and applications of source separation to mechanical systems for the condition monitoring and fault diagnosis purposes.
WIESEL A, HERO A O. Decomposable principal component analysis [J]. IEEE Transactions on Signal Processing, 2009, 57(11): 4369-4377.
COMON P, JUTTEN C. Handbook of blind source separation [M]. Waltham, MA, USA: Academic Press, 2010.
CHENG W, ZHANG Z S, LEE S, et al. Investigations of denoising source separation technique and its application to source separation and identification of mechanical vibration signals [J]. Journal of Vibration and Control, 2014, 20(14): 2100-2117.
HYVARINEN A. Fast and robust fixed-point algorithm for independent component analysis [J]. IEEE Transactions on Neural Networks, 1999, 10(3): 626-634.
HYVARINEN A, OJA E. Independent component analysis: algorithms and applications [J]. Neural Networks, 2000, 13(4/5): 411-430.
CHENG W, ZHANG Z, LEE S, et al. Source contribution evaluation of mechanical vibration signals via enhanced independent component analysis [J]. Journal of Manufacturing Science and Engineering, 2012, 134(2): 160-165.
HU J S, YANG C H. Estimation of sound source number and directions under a multisource reverberant environment [J/OL]. EURASIP Journal on Advanced in Signal Processing, 2010: 870756 [2014-11-20]. http:∥asp.eurasipjournals.com/content/2010/1/870756.
CHENG W, LEE S, ZHANG Z S, et al. Independent component analysis based source number estimation and its comparison for mechanical systems [J]. Journal of Sound and Vibration, 2012, 331(23): 5153-5167.
HAN KY, NEHORAI A. Improved source number detection and direction estimation with nested arrays and ULAS using JACKKNIFING [J]. IEEE Transactions on Signal Processing. 2013, 61(23): 6118-6128.
WILLIANMS D B. Counting the degrees of freedom when using AIC and MDL to detect signals [J]. IEEE Transactions on Signal Processing, 1994, 42(11): 3282-3284.
HUANG L, SO H C. Source enumeration via MDL criterion based on linear shrinkage estimation of noise subspace covariance matrix [J]. IEEE Transactions on Signal Processing, 2013, 61(19): 4806-4821.
DING Q, KAY S. Inconsistency of the MDL: on the performance of model order selection criteria with increasing signal-to-noise ratio [J]. IEEE Transactions on Signal Processing, 2011, 59(5): 1959-1969.
马建仓, 牛奕龙, 陈海洋. 盲信号处理 [M]. 北京: 国防工业出版社, 2006: 35-65.
MINKA T P. Automatic choice of dimensionality for PCA [C]∥14th Annual Neural Information Processing Systems Conference. Cambridge, MA, USA: MIT Press, 2001: 598-604.
徐光华,张锋,谢俊,等.稳态视觉诱发电位的脑机接口范式及其信号处理方法研.2015,49(6):1-7.[doi:10.7652/xjtuxb201506001]
熊涛,江桦,崔鹏辉,等.应用基扩展模型的混合信号单通道盲分离算法.2015,49(6):60-66.[doi:10.7652/xjtuxb201506 010]
刘进,李赞,高锐.低信噪比下采用广义随机共振的能量检测算法.2015,49(6):27-32.[doi:10.7652/xjtuxb201506005]
刘进,李赞,高锐.低信噪比下采用广义随机共振的能量检测算法.2015,49(6):27-32.[doi:10.7652/xjtuxb201506005]
郝雯洁,齐春.一种鲁棒的稀疏信号重构算法.2015,49(4):98-103.[doi:10.7652/xjtuxb201504016]
孙锦华,韩会梅.低信噪比下时频联合的载波同步算法.2015,49(2):62-68.[doi:10.7652/xjtuxb201502011]
唐成凯,廉保旺,张玲玲.卫星通信系统双向中继转发自干扰消除算法.2015,49(2):74-79.[doi:10.7652/xjtuxb201502 013]
王静,黄建国,侯云山.采用峰值平均功率比的低信噪比水下多目标检测方法.2012,46(2):124-129.[doi:10.7652/xjtuxb201202021]
蔡改改,陈雪峰,陈保家,等.利用设备响应状态信息的运行可靠性评估.2012,46(1):108-113.[doi:10.7652/xjtuxb 201201020]
国强,王常虹,李峥.支持向量聚类联合类型熵识别的雷达信号分选方法.2010,44(8):63-67.[doi:10.7652/xjtuxb201008 013]
鲁慧民,冯博琴,李旭.面向多源知识融合的扩展主题图相似性算法.2010,44(2):20-24.[doi:10.7652/xjtuxb201002005]
种稚萌,朱世华,吕刚明.分布式Alamouti空时码的信道容量分析.2007,41(8):969-973.[doi:10.7652/xjtuxb200708 020]
0
浏览量
4
下载量
5
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621