Continuous Wave Signal Detection Based on Weight Vector under Colored Gaussian Noise
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摘要: 针对高斯色噪声背景下信号检测需要进行预白化处理的问题, 提出了一种基于权向量的信号检测方法。首先基于权向量构造检验统计量, 分析其检测性能, 在此基础上推导了N-P准则下的最优权向量, 并证明了其与广义匹配滤波的等价性。通过构造合适的权向量, 证明在大数据记录下检测连续波信号时无须进行预白处理, 可以明显提高检测效率。仿真结果表明了该检测方法的有效性。Abstract: Signal detection under colored Gaussian noise needs pre-white processing. In view of this, a signal detection method based on a weight vector was proposed. Firstly, the test statistics were constructed based on the weight vector, and its detection performance was analyzed. On this basis, the optimal weight vector under the N-P criterion was derived, and its equivalence with the generalized matched filter was proved. By constructing a suitable weight vector, it was proved that there was no need for prewhitening when continuous wave signals were detected in big data records, which significantly improved the detection efficiency. The simulation results verify the effectiveness of the detection method.
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Key words:
- colored Gaussian noise /
- weight vector /
- signal detection /
- continuous wave signal /
- prewhitening
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图 1 N-
$ {{\boldsymbol{\eta}} _{\boldsymbol{d}}} $ 曲线Figure 1. Curve of N-
$ {{\boldsymbol{\eta}} _{\boldsymbol{d}}} $ -
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