The present thesis aims to make an in-depth study of Radar pulse compression, Neural Networks and Phase coded pulse compression codes. Pulse compression is a method which combines the high energy of a longer pulse width with the high resolution of a narrow pulse width. The major aspects that are considered for a pulse compression technique are signal to sidelobe ratio (SSR) performance, noise performance and Doppler shift performance. Matched filtering of biphase coded radar signals create unwanted sidelobes which may mask important information. The adaptive filtering techniques like Least Mean Square (LMS), Recursive Least Squares (RLS), and modified RLS algorithms are used for pulse radar detection and the results are compared. udIn this thesis, a novel approach for pulse compression using Recurrent Neural Network (RNN) is proposed. The 13-bit and 35-bit barker codes are used as signal codes to RNN and results are compared with Multilayer Perceptron (MLP) network. RNN yields better signal-to-sidelobe ratio (SSR), error convergence speed, noise performance, range resolution ability and Doppler shift performance than neural network (NN) and some traditional algorithms like auto correlation function(ACF) algorithm. But the SSR obtained from RNN is less for most of the applications. Hence a Radial Basis Function (RBF) neural network is implemented which yields better convergence speed, higher SSRs in adverse situations of noise and better robustness in Doppler shift tolerance than MLP and ACF algorithm. There is a scope of further improvement in performance in terms of SSR, error convergence speed, and Doppler shift. A novel approach using Recurrent RBF is proposed for pulse radar detection, and the results are compared with RBF, MLP and ACF. Biphase codes, namely barker codes are used as inputs to all these neural networks. The disadvantages of biphase codes include high sidelobes and poor Doppler tolerance.udThe Golay complementary codes have zero sidelobes but they are poor Doppler tolerant as that of biphase codes. The polyphase codes have low sidelobes and are more Doppler tolerant than biphase codes. The polyphase codes namely Frank, P1, P2, P3, P4 codes are described in detail and autocorrelation outputs, phase values and their Doppler properties are discussed and compared. The sidelobe reduction techniques such as single Two Sample Sliding Window Adder (TSSWA) and double TSSWA after the autocorrelator output are discussed and their performances for P4 code are presented and compared. Weighting techniques can also be applied to substantially reduce the range time sidelobes. The weighting functions such as Kaiser-Bessel amplitude weighting function and classical amplitude weighting functions (i.e. Hamming window) are described and are applied to the receiver waveform of 100 element P4 code and the autocorrelation outputs, Peak Sidelobe Level (PSL), Integrated Sidelobe Level (ISL) values are compared with that of rectangular window. The effects of weighting on the Doppler performance of the P4 code are presented and compared.ud
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机译:本文旨在对雷达脉冲压缩,神经网络和相位编码脉冲压缩编码进行深入研究。脉冲压缩是一种将较长脉冲宽度的高能量与窄脉冲宽度的高分辨率相结合的方法。脉冲压缩技术要考虑的主要方面是信号旁瓣比(SSR)性能,噪声性能和多普勒频移性能。双相编码雷达信号的匹配滤波会产生不想要的旁瓣,从而可能掩盖重要信息。自适应滤波技术,例如最小均方(LMS),递归最小二乘(RLS)和改进的RLS算法用于脉冲雷达检测,并对结果进行比较。 ud本文提出了一种使用递归神经网络(RNN)进行脉冲压缩的新方法。 13位和35位巴克码用作RNN的信号码,并将结果与多层感知器(MLP)网络进行比较。与神经网络(NN)和一些传统算法(例如自相关函数(ACF)算法)相比,RNN产生更好的信噪比(SSR),误差收敛速度,噪声性能,距离分辨能力和多普勒频移性能。但是对于大多数应用而言,从RNN获得的SSR较少。因此,与MLP和ACF算法相比,实现了径向基函数(RBF)神经网络,该神经网络具有更快的收敛速度,更高的SSR(在不利的噪声情况下)和更好的多普勒频移容忍性。在SSR,错误收敛速度和多普勒频移方面,性能还有进一步改善的范围。提出了一种基于递归RBF的脉冲雷达检测新方法,并将其结果与RBF,MLP和ACF进行了比较。双相码,即巴克码被用作所有这些神经网络的输入。双相码的缺点包括旁瓣高和多普勒容差低。 udGolay互补码的旁瓣为零,但与双相码一样,多普勒容错性差。多相编码具有较低的旁瓣,比双相编码具有更高的多普勒容忍度。详细描述了多相代码,即Frank,P1,P2,P3,P4代码,并讨论并比较了自相关输出,相位值及其多普勒特性。讨论了旁瓣减少技术,例如在自动相关器输出之后的单两个样本滑动窗口加法器(TSSWA)和双TSSWA,并介绍和比较了它们在P4码上的性能。加权技术也可以被应用以实质上减小范围时间旁瓣。描述了诸如Kaiser-Bessel幅度加权函数和经典幅度加权函数(即Hamming窗口)之类的加权函数,并将其应用于100元素P4码的接收机波形以及自相关输出,峰值旁瓣电平(PSL),集成旁瓣电平(ISL)值与矩形窗口的值进行比较。呈现并比较了加权对P4码的多普勒性能的影响。 ud
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