脉压雷达信号的识别和估计算法研究及其实现
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摘要
脉冲压缩雷达是一种低截获概率(LPI)雷达,对现代反辐射导弹(ARM)导引头和其他被动侦察接收机提出了新的挑战。论文具体以反辐射导弹导引头中脉冲压缩雷达信号的脉内特征分析与处理技术为主要研究内容,以脉压雷达信号中常见的信号形式,主要包括二相编码(BPSK)和四相编码(QPSK)信号、线性调频(LFM)和非线性调频(NLFM)雷达信号为主要研究对象,研究了脉压雷达信号的脉内调制方式识别,以及根据调制方式识别的结果实现有针对性的脉压雷达信号进一步处理,包括相位编码信号的参数估计,调频信号的检测和参数估计等,最后对脉压雷达信号处理的硬件实现进行了探索,为ARM导引头中脉压雷达信号的分选截获、匹配跟踪等提供必要的参数。
论文首先给出了脉压雷达信号处理的系统框图,本文的研究内容和所做的工作均是围绕着这样的系统结构进行的。针对脉压雷达信号的脉内调制方式识别,首先分析了脉压雷达信号的脉内调制特征,提出了一种从粗到细的调制类型识别方法,它首先根据信号的频谱带宽特征将信号粗分为相位编码信号和调频信号两类,然后使用类内细分的方法实现了细分类,该方法计算简单,方便有效,具有很高的工程应用价值；然后在统计模式识别方面,提出了一种基于自适应相像系数的脉压雷达信号全分类方法,通过构造联合特征分布和建立判决准则,实现了脉压雷达信号的调制类型识别。这些方法都为后续根据调制类型识别结果选取有针对性、高效率的调制参数估计方法提供了必要的前提。
在相位编码信号的参数估计方面,运用小波理论,首先针对含载频的信号,提出一种双尺度的小波变换法,利用粗细定位相结合的思想实现了对二相编码信号奇异点的精确定位,该方法尺度不易选取且低信噪比性能不佳；进一步针对去载频的相位编码信号,提出了一种基于乘积性多尺度小波变换的MPSK信号码速率估计算法,可以在较低信噪比提取突变点,采用FFT算法来估计码速率,使低信噪比性能进一步提高,且尺度的选取变得简便和有章可循。最后提出了一种基于时域累加瞬时自相关的PSK信号参数估计算法,通过累加后的时域波形便可提取突变点,对变换的结果做FFT,同样可以估计信号的码速率,该算法简单实用,同时也具有很优越的估计性能,在后续的硬件平台中得到了应用。
针对多项式相位信号(PPS)的检测问题,首先提出了一种基于重排小波-Radon变换的多分量LFM信号检测算法,该方法有效提高了时频分布图的聚集性,同时也起到了抑制噪声干扰的作用,辨识效果明显提高。针对WVD变换在检测多分量LFM信号和NLFM信号时的不足,讨论了基于PWVD和LWVD的NLFM信号检测算法,提出了一种基于乘积性谱图-WVD(PSWVD)变换的多分量PPS信号的检测方法,该方法综合了谱图和WVD变换的优势,在去除WVD变换交叉项的同时保持了良好的频率聚集性和优良的低信噪比性能。针对PPS的参数估计,重点讨论了基于三次相位函数(CPF)的PPS信号参数估计算法,提出了一种基于加权平均三次相位函数的多分量LFM信号参数估计算法及其快速实现算法。推导了三次相位函数的FFT快速算法,进一步采用了舍入最近采样点的方法改进算法,使其可以应用于实际的离散采样系统。
最后对脉压雷达信号处理的硬件实现做了一些探索。使用四片300MHz的浮点ADSP-TS101S芯片搭建了雷达信号硬件处理平台,基于频谱特征实现了信号类型的识别,基于时域累加瞬时自相关法实现了BPSK信号的参数估计算法,基于CPF法实现了LFM和NLFM雷达信号参数估计算法。并且成功进行了宽带数字信道化接收机与参数识别分机的联合测试。
Pulse compression radar with low probability of intercept brings a new challenge to the modern ARM seeker and other passive surveillance receivers. In present study, the intra-pulse modulation feature analysis and processing of pulse compression radar signals in ARM seeker were carried out, taking BPSK and QPSK signals, LFM and NLFM signals as main objects. And the recognition of modulation types and their corresponding further processing were studied, including the parameter estimation for PSK signals, detection and parameter estimation for frequency modulation signals. Additionally, some hardware experiments were done for the processing of pulse compression radar signals, which supplied necessary parameters for sorting, intercepting, matching and tracking of pulse compression radar signals in ARM seeker.
This study was shown around the block diagram of processing system of pulse compression radar signals. For the recognition of modulation types, the intra-pulse modulation feature of signals was analyzed, and a recognition method from the rough to the detailed was proposed. It firstly classified signals roughly into PSK signals and FM signals according to the feature of spectrum band, and then got the detailed classification in each group. The method is easy for computation and valid for performance, which is very valuable in engineering application. Besides, as to statistical pattern recognition, a full recognition algorithm based on adaptive resemblance coefficient was proposed, which realized the modulation type recognition by constructing joint feature distribution and setting up a decision rule. These methods provided the necessary prerequisite for later choosing corresponding and efficient algorithms of parameter estimation.
On the parameter estimation of PSK signals, using wavelet theory, a double-scale wavelet transform was proposed for signals with carrier frequency which could locate singularities precisely by combining rough location and accurate location. However, this method is difficult to choose scales and has poor performance of lower SNR. Thus, an estimation algorithm of symbol rate for MPSK signals without carrier frequency was put forward based on product multi-scale wavelet transform, which could extract singularities and estimate symbol rate by FFT transform in lower SNR, and it was easier and rule-based to choose scales. Finally, a parameter estimation method based on instantaneous autocorrelation with time-domain addition was proposed, which could extract singularities through the cumulative time-domain waveform and could also estimate symbol rate by FFT transform. It is easy and practical, and has good estimation performance, which is applied in the following hardware platform.
On the detection of PPS, a method of wavelet reassignment and Radon transform was proposed to detect multi-component LFM signals, which improves the aggregation of time-frequency distribution effectively, inhibits noise and enhances the detection performance obviously. Due to the disadvantage of WVD transform in the detection of multi-component LFM signals and NLFM signals, the detection methods of PWVD and LWVD transform were discussed, and a detection method called Product Spectrogram-WVD(PSWVD) transform for multi-component PPS was proposed. This new detection method integrated the superiors of spectrogram and WVD, which removed cross-terms of WVD and held good frequency aggregation and anti-noise performance. On the parameter estimation of PPS, the CPF method was mainly discussed. And an estimation algorithm and its fast implementation were proposed for multi-component LFM signals based on weighted mean CPF. In order to be used in practical discrete sampling system, CPF's FFT algorithm was deduced and improved by rounding the nearest sampling point.
Additionally, the hardware implementation of processing of pulse compression radar signals was put into practice. On the hardware platform of processing of radar signals constructed by 4 chips of 300MHz ADSP-TS101S, the recognition of modulation types was realized according to the feature of spectrum and band, the parameter estimation of BPSK signal was achieved by instantaneous autocorrelation with time-domain addition, and the parameter estimation of LFM and NLFM signals was implemented based on CPF. Joint test between the wideband digital channelized receiver and the parameter estimation hardware platform was done successfully.
论文首先给出了脉压雷达信号处理的系统框图,本文的研究内容和所做的工作均是围绕着这样的系统结构进行的。针对脉压雷达信号的脉内调制方式识别,首先分析了脉压雷达信号的脉内调制特征,提出了一种从粗到细的调制类型识别方法,它首先根据信号的频谱带宽特征将信号粗分为相位编码信号和调频信号两类,然后使用类内细分的方法实现了细分类,该方法计算简单,方便有效,具有很高的工程应用价值；然后在统计模式识别方面,提出了一种基于自适应相像系数的脉压雷达信号全分类方法,通过构造联合特征分布和建立判决准则,实现了脉压雷达信号的调制类型识别。这些方法都为后续根据调制类型识别结果选取有针对性、高效率的调制参数估计方法提供了必要的前提。
在相位编码信号的参数估计方面,运用小波理论,首先针对含载频的信号,提出一种双尺度的小波变换法,利用粗细定位相结合的思想实现了对二相编码信号奇异点的精确定位,该方法尺度不易选取且低信噪比性能不佳；进一步针对去载频的相位编码信号,提出了一种基于乘积性多尺度小波变换的MPSK信号码速率估计算法,可以在较低信噪比提取突变点,采用FFT算法来估计码速率,使低信噪比性能进一步提高,且尺度的选取变得简便和有章可循。最后提出了一种基于时域累加瞬时自相关的PSK信号参数估计算法,通过累加后的时域波形便可提取突变点,对变换的结果做FFT,同样可以估计信号的码速率,该算法简单实用,同时也具有很优越的估计性能,在后续的硬件平台中得到了应用。
针对多项式相位信号(PPS)的检测问题,首先提出了一种基于重排小波-Radon变换的多分量LFM信号检测算法,该方法有效提高了时频分布图的聚集性,同时也起到了抑制噪声干扰的作用,辨识效果明显提高。针对WVD变换在检测多分量LFM信号和NLFM信号时的不足,讨论了基于PWVD和LWVD的NLFM信号检测算法,提出了一种基于乘积性谱图-WVD(PSWVD)变换的多分量PPS信号的检测方法,该方法综合了谱图和WVD变换的优势,在去除WVD变换交叉项的同时保持了良好的频率聚集性和优良的低信噪比性能。针对PPS的参数估计,重点讨论了基于三次相位函数(CPF)的PPS信号参数估计算法,提出了一种基于加权平均三次相位函数的多分量LFM信号参数估计算法及其快速实现算法。推导了三次相位函数的FFT快速算法,进一步采用了舍入最近采样点的方法改进算法,使其可以应用于实际的离散采样系统。
最后对脉压雷达信号处理的硬件实现做了一些探索。使用四片300MHz的浮点ADSP-TS101S芯片搭建了雷达信号硬件处理平台,基于频谱特征实现了信号类型的识别,基于时域累加瞬时自相关法实现了BPSK信号的参数估计算法,基于CPF法实现了LFM和NLFM雷达信号参数估计算法。并且成功进行了宽带数字信道化接收机与参数识别分机的联合测试。
Pulse compression radar with low probability of intercept brings a new challenge to the modern ARM seeker and other passive surveillance receivers. In present study, the intra-pulse modulation feature analysis and processing of pulse compression radar signals in ARM seeker were carried out, taking BPSK and QPSK signals, LFM and NLFM signals as main objects. And the recognition of modulation types and their corresponding further processing were studied, including the parameter estimation for PSK signals, detection and parameter estimation for frequency modulation signals. Additionally, some hardware experiments were done for the processing of pulse compression radar signals, which supplied necessary parameters for sorting, intercepting, matching and tracking of pulse compression radar signals in ARM seeker.
This study was shown around the block diagram of processing system of pulse compression radar signals. For the recognition of modulation types, the intra-pulse modulation feature of signals was analyzed, and a recognition method from the rough to the detailed was proposed. It firstly classified signals roughly into PSK signals and FM signals according to the feature of spectrum band, and then got the detailed classification in each group. The method is easy for computation and valid for performance, which is very valuable in engineering application. Besides, as to statistical pattern recognition, a full recognition algorithm based on adaptive resemblance coefficient was proposed, which realized the modulation type recognition by constructing joint feature distribution and setting up a decision rule. These methods provided the necessary prerequisite for later choosing corresponding and efficient algorithms of parameter estimation.
On the parameter estimation of PSK signals, using wavelet theory, a double-scale wavelet transform was proposed for signals with carrier frequency which could locate singularities precisely by combining rough location and accurate location. However, this method is difficult to choose scales and has poor performance of lower SNR. Thus, an estimation algorithm of symbol rate for MPSK signals without carrier frequency was put forward based on product multi-scale wavelet transform, which could extract singularities and estimate symbol rate by FFT transform in lower SNR, and it was easier and rule-based to choose scales. Finally, a parameter estimation method based on instantaneous autocorrelation with time-domain addition was proposed, which could extract singularities through the cumulative time-domain waveform and could also estimate symbol rate by FFT transform. It is easy and practical, and has good estimation performance, which is applied in the following hardware platform.
On the detection of PPS, a method of wavelet reassignment and Radon transform was proposed to detect multi-component LFM signals, which improves the aggregation of time-frequency distribution effectively, inhibits noise and enhances the detection performance obviously. Due to the disadvantage of WVD transform in the detection of multi-component LFM signals and NLFM signals, the detection methods of PWVD and LWVD transform were discussed, and a detection method called Product Spectrogram-WVD(PSWVD) transform for multi-component PPS was proposed. This new detection method integrated the superiors of spectrogram and WVD, which removed cross-terms of WVD and held good frequency aggregation and anti-noise performance. On the parameter estimation of PPS, the CPF method was mainly discussed. And an estimation algorithm and its fast implementation were proposed for multi-component LFM signals based on weighted mean CPF. In order to be used in practical discrete sampling system, CPF's FFT algorithm was deduced and improved by rounding the nearest sampling point.
Additionally, the hardware implementation of processing of pulse compression radar signals was put into practice. On the hardware platform of processing of radar signals constructed by 4 chips of 300MHz ADSP-TS101S, the recognition of modulation types was realized according to the feature of spectrum and band, the parameter estimation of BPSK signal was achieved by instantaneous autocorrelation with time-domain addition, and the parameter estimation of LFM and NLFM signals was implemented based on CPF. Joint test between the wideband digital channelized receiver and the parameter estimation hardware platform was done successfully.
引文
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