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针对啁啾（Chirp）信号参数估计的高复杂度和在低信噪比（SNR）下保持高精度等问题,文章提出了一种改进的Chirp信号参数估计算法。该算法根据当信号能量和周期长度一定时Chirp信号频谱幅度的平方与调频率成反比的特性,不断地产生已知Chirp信号的共轭与未知Chirp信号相乘,找到使相乘后频谱幅度平方最大的已知Chirp信号的调频率。为了做到参数估计的高精度,用修正的幅度插值（M-Rife）算法估计中心频率和修正相乘后信号频谱的幅值。将该算法与短时傅里叶变换（STFT）结合,首先用STFT估计出调频率的大致范围和信号周期,然后用二分法代替等步长使改进的Chirp信号参数估计算法在这个大致范围内搜索,极大地降低了计算复杂度。实验结果表明,在SNR≥-10 dB时,该算法估计调频率归一化均方误差（NMSE）要优于分数阶傅里叶变换（FRFT）约1～2 dB,接近克拉美罗下界（CRLB）。

针对分数阶傅里叶变换（FRFT）对啁啾（Chirp）信号进行盲检测时存在运算量大的问题,文章提出一种针对Chirp信号参数估计的盲检测方法。首先利用改变窗长相位差法对Chirp信号进行粗估计,并利用最小二乘法进行拟合得到Chirp信号调频斜率粗估计值,然后通过FRFT对Chirp信号进行精细估计,并利用二分法对FRFT的阶次进行搜索,进一步降低算法的运算量。仿真结果表明,利用改变窗长相位差法和FRFT联合检测Chirp信号的方法,在低信噪比下具有较高精度的估计值,且其归一化均方误差接近克拉美罗下界,并且与FRFT穷举检测相比,极大地减少了运算量,具有很好的工程应用意义。

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Geoscientica Sinica is the property of Acta Geoscientica Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Pesticide Science / Nongyaoxue Xuebao is the property of Chinese Journal of Pesticide Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mutually exclusive products (such as liquids, hazardous chemicals, etc.) cannot be mixed into the same container. Logistics companies usually use multi-compartment trucks to deliver mutually exclusive products to customers. The loading and vehicle routing strategies are the key issues to determine the distribution efficiency and distribution costs. Considering the constraints of loading and unloading sequence and transportation time of mutually exclusive products, a joint optimization model of loading and distribution is constructed to minimize the distribution cost. This paper designs an improved genetic algorithm for solving the model, using the queen evolution and the edge reconstruction crossover operations based on probability to lift the ability of finding the optimal solution. Then we construct the testing examples based on the vehicle routing problem benchmark provided by Augerat to verify the running time and solving efficient of genetic algorithm. The simulation results show that, the solutions obtained by improved genetic algorithm are better than those of classical genetic algorithm, for small-scale examples, the improved genetic algorithm can obtains global optimal solution; for the medium-sized or large size examples with no more than 101 customers, the approximate optimal solution can be obtained in 130 seconds using genetic algorithm. The innovation of this paper lies in the establishment of a mathematical model for a new expended vehicle routing problem and the design of a fast and effective algorithm for solving the model. The mathematical model and algorithm of this paper provide a theoretical basis and algorithmic support for logistics companies to draw up distribution schedule of mutually exclusive products. [ABSTRACT FROM AUTHOR]