本文为印度Rourkela国立技术学院（作者：Ajit Kumar Sahoo）的博士论文，共160页。
在雷达系统中采用了脉冲压缩技术，综合利用了长脉冲的远距离检测能力和短脉冲的高距离分辨能力。在该技术中，使用了一种长持续时间的脉冲，它在发射前进行相位或频率调制，接收到的信号通过滤波器将能量积累成短脉冲。通常，脉冲压缩采用匹配滤波器来实现高信噪比。然而，匹配滤波器的输出，即调制信号的自相关函数（ACF）与距离旁瓣以及主瓣相关。这些旁瓣是来自脉冲压缩滤波器的不需要的输出，当一个较弱目标靠近一个较强的目标时，该较弱的目标可能会被较强目标所掩盖。因此，这些旁瓣会影响雷达探测系统的性能。为了提高雷达探测系统的性能，本论文对利用计算智能技术减小距离旁瓣进行了研究。
在相位编码信号中，长脉冲被分成若干子脉冲，每个子脉冲都被赋予一个相位值。相位分配应使相位编码信号的ACF达到较低的旁瓣。本文提出了一种多目标进化算法，通过对二相码的相位值进行分配，以实现低旁瓣。基本上，对于特定长度的码失配滤波器，优于传统的匹配滤波器，能够获得更好的峰值旁瓣比（PSR）。为了在各种噪声、多普勒频移和多目标环境下获得较好的PSR值，提出了一种基于递归神经网络（RNN）和递归径向基函数（RRBF）结构的失配滤波器。
与二相码相比，多相和线性调频（LFM）码产生的旁瓣更低。各种加权函数被用来进一步抑制多相和线性调频码的旁瓣。本文采用卷积窗作为加权函数，在不同多普勒频移条件下获得较高的PSR值。在高距离分辨率雷达中，采用的发射信号为宽带信号，传统窄带硬件系统可能不支持较高的瞬时带宽。因此，在发射端将宽带信号分割成若干窄带信号，然后在接收端进行相干解调并重组宽带信号，以获得宽带信号的处理效果。然而，这种窄带脉冲串的ACF存在栅瓣，从而降低了脉冲串的距离分辨能力。本文提出了一种进化计算算法，通过优化选择步进频率线性调频脉冲串的参数，实现了减小栅瓣、降低峰值旁瓣和缩小主瓣宽度的目的。
Pulse compression techniques are used in radar systems to avail the benefits of large range detection capability of long duration pulse and high range resolution capability of short duration pulse. In these techniques a long duration pulse is used which is either phase or frequency modulated before transmission and the received signal is passed through a filter to accumulate the energy into a short pulse. Usually, a matched filter is used for pulse compression to achieve high signal-to-noise ratio (SNR). However, the matched filter output i.e. autocorrelation function (ACF) of a modulated signal is associated with range sidelobes along with the mainlobe. These sidelobes are unwanted outputs from the pulse compression filter and may mask a weaker target which is present nearer to a stronger target. Hence, these sidelobes affect the performance of the radar detection system. In this thesis, few investigations have been made to reduce the range sidelobes using computational intelligence techniques so as to improve the performance of radar detection system. In phase coded signals a long pulse is divided into a number of sub pulses each of which is assigned with a phase value. The phase assignment should be such that the ACF of the phase coded signal attain lower sidelobes. A multiobjective evolutionary approach is proposed to assign the phase values in the biphase code so as to achieve low sidelobes. Basically, for a particular length of code mismatch filter is preferred over matched filter to get better peak to sidelobe ratio (PSR). Recurrent neural network (RNN) and recurrent radial basis function (RRBF) structures are proposed as mismatch filters to achieve better PSR values under various noise conditions, Doppler shift and multiple target environment. Polyphase and linear frequency modulated (LFM) codes yield lower sidelobes compared to biphase codes. Various weighing functions are used to further suppress the sidelobes of polyphase and LFM codes. In this thesis, convolutional windows are used as weighing function to achieve high PSR magnitude at different Doppler shift conditions. In high range resolution radar wide bandwidth signals are used for transmission. The conventional narrowband hardware may not support the instantaneous wide bandwidth. Therefore, the wide bandwidth signal is split into several narrowband signals which are transmitted and recombined coherently at the receiver to get the effect of the wideband signal. However, the ACF of such narrow band pulse train suffers from grating lobes and hence reduce the range resolution capability of the pulse train. In this work, evolutionary computation algorithms are proposed to optimally choose the parameters of stepped frequency LFM pulse train to achieve reduced grating lobes, low peak sidelobe and narrow mainlobe width.
1 引言
1.1 脉冲压缩
1.2 匹配滤波器
1.3 雷达信号
1.4 本文研究背景与研究范围
1.5 研究动机
1.6 本文研究目标
1.7 结论
2 基于多目标遗传算法的脉冲压缩码生成
2.1 引言
2.2 衡量标准与问题建模
2.3 所使用的算法技术
2.4 产生脉冲压缩码
2.5 仿真结果
2.6 结论
3 用于旁瓣抑制的新型高效人工神经网络失配滤波器的研制与性能评价
3.1 引言
3.2 问题描述
3.3 所使用的算法技术
3.4 仿真结果
3.5 结论
4 卷积窗对线性调频码和多相码的有效旁瓣抑制
4.1 引言
4.2 LFM与多相码
4.3 问题描述
4.4 用于旁瓣抑制的加窗方法
4.5 仿真结果
4.6 结论
5 基于进化计算技术的步进频率脉冲串的有效设计
5.1 引言
5.2 LFM脉冲串
5.3 问题描述
5.4 所使用的算法技术
5.5 LFM脉冲串参数的确定
5.6 仿真结果
5.7 结论
6 结论与未来工作展望
6.1 结论
6.2 未来工作展望
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