脉压雷达脉内特征分析与处理技术研究
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摘要
随着脉冲压缩雷达的产生与发展,雷达系统通过脉冲压缩技术的应用不仅提高了雷达距离分辨率,同时也降低了其被截获概率,对现代反辐射导弹(ARM)导引头提出了新的挑战。因此,ARM必须在进行脉压雷达的匹配截获过程中为其提供必需的脉内调制特征信息,脉压雷达脉内调制特征分析与处理技术也就成为了ARM亟待解决的关键问题。本文针对该课题进行了深入研究,主要内容包括常见调制形式脉压雷达的脉内调制方式的识别、脉内调制参数的估计技术解决方案。
提出了脉压雷达信号脉内特征分析与处理系统框图,定义了二维谱特征波形相似度、全参数二维谱特征波形相似度质心等概念,构造了调制特征变量,在最小距离判决准则下提出了基于二维谱特征波形相似度的脉压雷达脉内调制方式识别方法。仿真分析证明,对于脉压雷达信号参数范围内不同信噪比、不同调制参数的情况,该方法可以在常规、调频、调相雷达的“调制类别识别结果”中获得接近100%的识别准确率,识别性能明显优于传统的瞬时自相关和瞬时频率等调制识别方法,为下文有针对性、高效率地估计脉压雷达脉内调制参数奠定了必要基础。同时也指出了该调制识别方法进一步完善、推广的方向。
提出了双延时HAF的LFM参数估计方法,改善了传统HAF的自身局限性,解决了传统HAF其参数无模糊估计范围与估计精度间的矛盾,扩大了无模糊估计范围;在此基础上提出了基于双延时HAF+局部ML的LFM参数估计方法,利用双延时HAF对局部ML及其搜索范围、搜索步径的定义,不仅发挥了ML最优参数估计的优势,而且较传统ML全域搜索而言,大大降低了算法运算量。旨在进一步改善算法实时性,利用双延时HAF的解调制与局部ML的极值搜索高精度估计思想,提出了基于频谱方差准则改进HAF的LFM参数估计方法,通过频谱方差极值搜索下的LFM调制斜率估计,将局部ML中的LFM参数二维联合搜索估计解耦、降维成两次一维估计,大幅度地提高了算法实时性,并且可以得到优于双延时HAF的参数估计精度。同时还提出了将双延时HAF推广至三阶次NLFM参数估计的应用方法,分析了该方法在多次相关运算下信噪比的损失,并为其提出了进一步的研究方向。
以载频估计为核心,采用逐级简化思路提出了基于相位编码雷达基带信号的PSK参数估计结构:首先,提出了基于改进小波脊线的PSK载频估计,以及基于改进MAT的PSK载频估计偏差修正思想,实现了对传统小波脊线的改进,获得了优于传统小波脊线、相位差分法的载频估计精度,以及优于传统相位差分法的抗噪性能;其次,通过ARM微波前端同步视频脉冲与去载波后PSK基带信号的应用,降低了载频对PSK脉内相位突变检测的影响,提出了基于二尺度CWT检测的PSK码元速率估计方法;最后,通过脉内子码间相位编码差的估计,提出了基于瞬时自相关极性判断的PSK编码序列识别。仿真分析证明,SNR≥3dB后该结构可实现100%准确率的BPSK码元速率估计,QPSK码元速率估计在利用编码序列识别结果修正后也可实现100%准确率;BPSK、QPSK编码序列识别则分别在SNR≥3dB,5dB后可实现100%的准确率。
Along with the emergence and development of the pulse compression radar, the radar using of pulse compression technology can not only improves the range resolution, but also reduces the probability of interception. As a result, it puts forward the new challenge for the Modern ARM Seeker, which needs the ARM provide necessary intra-pulse modulation feature informatios for the matched filter and interception. Therefore, the intra-pulse modulation feature analysis and processing technology for Pulse Compression Radar signal becomes the critical problem to be solved urgently. The subject is researched deeply and extensively in this dissertation, including the key technology solution aiming at intra-pulse modulation mode recognition, and intra-pulse modulation parameters estimation.
The intra-pulse modulation feature analysis and processing system for Pulse-Compression Radar signal is proposed. The Two-Dimensional Spectrum Features's Wave Similarity, its All Parameters Centroid and so on are defined, the modulation feature variable is constructed then. Using the minimum distance principle, the intra-pulse modulation mode recognition method base on Two-Dimensional Spectrum Features's Wave Similarity is proposed. The simulation and anlalysis verifies that, in the SNR and modulation parameters range, this method can achieve nearly 100% accuracy of "modulation category recognition result" among normal, frequency modulation and phase modulation radar category, which is obvious superior to the traditional instantaneous auto-correlation and instantaneous Frequency. It can choose efficient means for the modulation parameters estimation. Then, the improved direction is also discussed.
The dual-delay HAF for LFM signal parameters estimation is presented. It can resolve the HAF's contradiction between parameters' estimation precision and unambiguity range, enlarge the unambiguity range. With this improvement, the dual-delay HAF+local ML for LFM signal parameters estimation is proposed. Through the definition of searching range and step for local ML, the dual-delay HAF+local ML not only plays the ML's optimal parameter estimation advantage, but also reduces the computational complexity greatly. In order to improve the real-time performance further, the improved HAF based on Spectrum's Maximum Variance Principle is put forward, combining the dual-delay's demodulation with the local ML's extremum searching idea. The improved HAF can decouple the local ML's two-dimensional joint estimation into twice one-dimensional, and get higher estimation precision than dual-delay HAF. In addition, the generalization of the dual-delay HAF applied to the third-order NLFM parameters estimation and its signal-to-noise ratio loss under multiple correlation operation are also introduced.
The PSK parameters' estimation structure based on the baseband signal is proposed, which takes the carrier-frequency estimation as core, using the gradually simplified idea. Firstly, the M-Wavelet-ridge for PSK carrier-frequency estimation and the M-MAT for correcting its deviation are presented. They can acquire not only higher carrier-frequency estimation precision than the traditional and phase differences, but also superior anti-noise performance to phase differences. Using the video synchronous pulse from ARM's microwave front-end and the PSK baseband signal, the structure can reduces the carrier-frequency's influence to the intra-pulse phase mutation detection, and puts forward the PSK symbol rate estimation method based on the two-Scale CWT's detection. Finally, the estimation structure also produces the PSK phase coding sequences estimation method, through estimation of phase codes' difference betweent the intra-pulse subcodes. The simulation and anlalysis verifies that, this strcture can achieve 100% accuracy of BPSK symbol rate estimation when SNR≥3dB, the QPSK symbol rate estimation accuracy can achieve 100% through the correction of phase coding sequences estimation when SNR≥3dB , the BPSK and QPSK phase coding sequences' estimation accuracy can also achieve 100% when SNR≥3dB ,5dB respectively.
提出了脉压雷达信号脉内特征分析与处理系统框图,定义了二维谱特征波形相似度、全参数二维谱特征波形相似度质心等概念,构造了调制特征变量,在最小距离判决准则下提出了基于二维谱特征波形相似度的脉压雷达脉内调制方式识别方法。仿真分析证明,对于脉压雷达信号参数范围内不同信噪比、不同调制参数的情况,该方法可以在常规、调频、调相雷达的“调制类别识别结果”中获得接近100%的识别准确率,识别性能明显优于传统的瞬时自相关和瞬时频率等调制识别方法,为下文有针对性、高效率地估计脉压雷达脉内调制参数奠定了必要基础。同时也指出了该调制识别方法进一步完善、推广的方向。
提出了双延时HAF的LFM参数估计方法,改善了传统HAF的自身局限性,解决了传统HAF其参数无模糊估计范围与估计精度间的矛盾,扩大了无模糊估计范围;在此基础上提出了基于双延时HAF+局部ML的LFM参数估计方法,利用双延时HAF对局部ML及其搜索范围、搜索步径的定义,不仅发挥了ML最优参数估计的优势,而且较传统ML全域搜索而言,大大降低了算法运算量。旨在进一步改善算法实时性,利用双延时HAF的解调制与局部ML的极值搜索高精度估计思想,提出了基于频谱方差准则改进HAF的LFM参数估计方法,通过频谱方差极值搜索下的LFM调制斜率估计,将局部ML中的LFM参数二维联合搜索估计解耦、降维成两次一维估计,大幅度地提高了算法实时性,并且可以得到优于双延时HAF的参数估计精度。同时还提出了将双延时HAF推广至三阶次NLFM参数估计的应用方法,分析了该方法在多次相关运算下信噪比的损失,并为其提出了进一步的研究方向。
以载频估计为核心,采用逐级简化思路提出了基于相位编码雷达基带信号的PSK参数估计结构:首先,提出了基于改进小波脊线的PSK载频估计,以及基于改进MAT的PSK载频估计偏差修正思想,实现了对传统小波脊线的改进,获得了优于传统小波脊线、相位差分法的载频估计精度,以及优于传统相位差分法的抗噪性能;其次,通过ARM微波前端同步视频脉冲与去载波后PSK基带信号的应用,降低了载频对PSK脉内相位突变检测的影响,提出了基于二尺度CWT检测的PSK码元速率估计方法;最后,通过脉内子码间相位编码差的估计,提出了基于瞬时自相关极性判断的PSK编码序列识别。仿真分析证明,SNR≥3dB后该结构可实现100%准确率的BPSK码元速率估计,QPSK码元速率估计在利用编码序列识别结果修正后也可实现100%准确率;BPSK、QPSK编码序列识别则分别在SNR≥3dB,5dB后可实现100%的准确率。
Along with the emergence and development of the pulse compression radar, the radar using of pulse compression technology can not only improves the range resolution, but also reduces the probability of interception. As a result, it puts forward the new challenge for the Modern ARM Seeker, which needs the ARM provide necessary intra-pulse modulation feature informatios for the matched filter and interception. Therefore, the intra-pulse modulation feature analysis and processing technology for Pulse Compression Radar signal becomes the critical problem to be solved urgently. The subject is researched deeply and extensively in this dissertation, including the key technology solution aiming at intra-pulse modulation mode recognition, and intra-pulse modulation parameters estimation.
The intra-pulse modulation feature analysis and processing system for Pulse-Compression Radar signal is proposed. The Two-Dimensional Spectrum Features's Wave Similarity, its All Parameters Centroid and so on are defined, the modulation feature variable is constructed then. Using the minimum distance principle, the intra-pulse modulation mode recognition method base on Two-Dimensional Spectrum Features's Wave Similarity is proposed. The simulation and anlalysis verifies that, in the SNR and modulation parameters range, this method can achieve nearly 100% accuracy of "modulation category recognition result" among normal, frequency modulation and phase modulation radar category, which is obvious superior to the traditional instantaneous auto-correlation and instantaneous Frequency. It can choose efficient means for the modulation parameters estimation. Then, the improved direction is also discussed.
The dual-delay HAF for LFM signal parameters estimation is presented. It can resolve the HAF's contradiction between parameters' estimation precision and unambiguity range, enlarge the unambiguity range. With this improvement, the dual-delay HAF+local ML for LFM signal parameters estimation is proposed. Through the definition of searching range and step for local ML, the dual-delay HAF+local ML not only plays the ML's optimal parameter estimation advantage, but also reduces the computational complexity greatly. In order to improve the real-time performance further, the improved HAF based on Spectrum's Maximum Variance Principle is put forward, combining the dual-delay's demodulation with the local ML's extremum searching idea. The improved HAF can decouple the local ML's two-dimensional joint estimation into twice one-dimensional, and get higher estimation precision than dual-delay HAF. In addition, the generalization of the dual-delay HAF applied to the third-order NLFM parameters estimation and its signal-to-noise ratio loss under multiple correlation operation are also introduced.
The PSK parameters' estimation structure based on the baseband signal is proposed, which takes the carrier-frequency estimation as core, using the gradually simplified idea. Firstly, the M-Wavelet-ridge for PSK carrier-frequency estimation and the M-MAT for correcting its deviation are presented. They can acquire not only higher carrier-frequency estimation precision than the traditional and phase differences, but also superior anti-noise performance to phase differences. Using the video synchronous pulse from ARM's microwave front-end and the PSK baseband signal, the structure can reduces the carrier-frequency's influence to the intra-pulse phase mutation detection, and puts forward the PSK symbol rate estimation method based on the two-Scale CWT's detection. Finally, the estimation structure also produces the PSK phase coding sequences estimation method, through estimation of phase codes' difference betweent the intra-pulse subcodes. The simulation and anlalysis verifies that, this strcture can achieve 100% accuracy of BPSK symbol rate estimation when SNR≥3dB, the QPSK symbol rate estimation accuracy can achieve 100% through the correction of phase coding sequences estimation when SNR≥3dB , the BPSK and QPSK phase coding sequences' estimation accuracy can also achieve 100% when SNR≥3dB ,5dB respectively.
引文
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