1. 金陵科技学院,江苏,南京,211169
2. 南京信息职业技术学院电子信息学院,江苏,南京,210023
3. 南京林业大学信息科学技术学院,江苏,南京,210037
4. 金陵科技学院,江苏,南京,211169
5. 南京信息职业技术学院电子信息学院,江苏,南京,210023
6. 南京林业大学信息科学技术学院,江苏,南京,210037
网络出版:2019-09-25,
纸质出版:2019
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胡国兵, 吴珊珊, 杨忠, 等. LFM/BPSK混合调制信号盲处理结果可信性评估:一种简化的似然比算法[J]. 电子学报, 2019,47(9):1891-1897.
HU Guo-bing, WU Shan-shan, YANG Zhong, et al. Credibility Evaluation for Blind Processing Results of LFM/BPSK Hybird Modulation Signals:A Simple Likelihood Ratio Based Approach[J]. Acta Electronica Sinica, 2019, 47(9): 1891-1897.
胡国兵, 吴珊珊, 杨忠, 等. LFM/BPSK混合调制信号盲处理结果可信性评估:一种简化的似然比算法[J]. 电子学报, 2019,47(9):1891-1897. DOI: 10.3969/j.issn.0372-2112.2019.09.011.
HU Guo-bing, WU Shan-shan, YANG Zhong, et al. Credibility Evaluation for Blind Processing Results of LFM/BPSK Hybird Modulation Signals:A Simple Likelihood Ratio Based Approach[J]. Acta Electronica Sinica, 2019, 47(9): 1891-1897. DOI: 10.3969/j.issn.0372-2112.2019.09.011.
针对以相关谱最大值作为统计量对线性调频/二相编码(LFM/BPSK,Linear Frequency Modulation/Binary Phase Shift Keying)混合调制信号盲处理结果进行可信性检验时,存在概率密度函数复杂,难以得到似然比检验闭合表达式的问题,提出了一种基于极值分布理论(EVT,Extreme Value Theory)的简化处理算法.利用相关谱最大值的极限分布替代其精确分布,基于纽曼-皮尔逊(NP,Neyman-Pearson)准则得到简化的似然比检验,给出了相应判决式及其判决门限的解析表达式.文中给出了不同假设下相关谱最大值的极限分布形式.计算机仿真结果表明:本算法与已有的恒虚警方法相当,但优于基于分组极值模型及超阈值模型的两种分布拟合检验法,且具有较低的计算复杂度.
In order to verify the confidence of the blind analysis result of the LFM/BPSK (Linear Frequency Modulation/Binary Phase Shift Keying) hybrid modulation signals by using the maximum value of the correlation spectrum
a simple likelihood ratio algorithm based on extreme value theory is formulated in this paper
which can solve the problem caused by the complexity of the probability of the maximum statistic of the correlation spectrum when creating the likelihood ratio test. By replacing the asymptotic distribution of the exact distribution of the maximum statistic and using the NP criterion
the decision statistic and its corresponding threshold is derived. The asymptotic distribution of the maximum statistic under the two hypotheses are provided. The simulations show that the performance of the proposed algorithm is similar to the constant false alarm based algorithm
and is superior to the two goodness of fit test based algorithms that are formulated by the group extreme value and the peak over threshold models respectively.
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