Universal FRFT-based algorithm for parameter estimation of chirp signals

The realization of the parameter estimation of chirp signals using the fractional Fourier transform （FRFT） is based on the assumption that the sampling duration of practical observed signals would be equal to the time duration of chirp signals contained in the former. However, in many actual circumstances, this assumption seems unreasonable. On the basis of analyzing the practical signal form, this paper derives the estimation error of the existing parameter estimation method and then proposes a novel and universal parameter estimation algorithm. Furthermore, the proposed algorithm is developed which allows the estimation of the practical observed Gaussian windowed chirp signal. Simulation results show that the new algorithm works well.

Wideband angle of arrival estimation of chirp signals using virtual Wignerville distribution

To estimate the angle of arrivals （AOA） of wideband chirp sources, a new timo-frequency algorithm is proposed. In this method, virtual sensors are constructed based on the fact that the steering vectors of wideband chirp signals are linear and vary with time. And the randon Wignersville distribution （RWVD） of real sensors and virtual sensors are calculated to yield the new time-invariable steering vectors, furthermore, the noise and cross terms are suppressed. In addition, the multiple chirp signals are selected by their time-frequency points. The cost of computation is lower than the common AOA estimation methods of wideband sources due to nonrequirement of frequency focusing, interpolating and matrix decomposition, including subspace decomposition. Under the lower signal noise ratio （SNR） condition, the proposed method exhibits better precision than the method of frequency focusing （FF）. The proposed method can be further applied to nonuniform linear array （NLA） since it is not confined to the array geometry. Simulation results illustrate the efficacy of the proposed method.

Chirp-Rate Resolution of Fractional Fourier Transform in Multi-component LFM Signal

Distinguishing close chirp-rates of different linear frequency modulation （LFM） signals under concentrated and complicated signal environment was studied. Firstly, detection and parameter estimation of multi-component LFM signal were used by discrete fast fractional Fourier transform （FrFT）. Then the expression of chirp-rate resolution in fractional Fourier domain （FrFD） was deduced from discrete normalize time-frequency distribution, when multi-component LFM signal had only one center frequency. Furthermore, the detail influence of the sampling time, sampling frequency and chirp-rate upon the resolution was analyzed by partial differential equation. Simulation results and analysis indicate that increasing the sampling time can enhance the resolution, but the influence of the sampling frequency can he omitted. What＇s more, in multi-component LFM signal, the chirp-rate resolution of FrFT is no less than a minimal value, and it mainly dependent on the biggest value of chirp-rates, with which it has an approximately positive exponential relationship.

Parameter Estimation of Multi-component Chirp Signal Based on Differential Evolution

Chirp signals show energy aggregation in the fractional Fourier domain(FrFD) w hich can be used to estimate the parameter of the signals. In this paper,a parameter estimation method for multi-component chirp signal w hich corrupted by w hite Gaussian noise is proposed based on the discrete fractional Fourier transform(DFrFT) and the differential evolution( DE) algorithm. The proposed algorithm uses the DE algorithm instead of the conventional fine search algorithm to detect the peak of the signals in the FrFD. The paper simulated the influence of the noise and the resolution of the proposed algorithm. The results of the simulation show the proposed method does not only improve the estimation accuracy of the peak coordinate,but also reduces time consuming.

Multi-subpulse process of large time-bandwidth product chirp signal

To prevent the long-time coherent integration and limited range window stumbling blocks of stretch processing and reduce computational complexity, a novel method called multi-subpulse process of large time-bandwidth product linear frequency modulating （ LFM ） signal （ i. e. chirp ） is proposed in this paper. The wideband chirp signal is split up into several compressed subpulses. Then the fast Fourier transform （FFT） is used to reconstruct the high resolution range profile （ HR- RP） in a relative short computation time. For multi-frame, pulse Doppler （PD） process is performed to obtain the two-dimension range-Doppler （R-D） high resolution profile. Simulations and field ex- perimental results show that the proposed method can provide high-quality target profile over a large range window in a short computation time and has the promising potential for long-time coherent in- tegration.

Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.

Instantaneous frequency estimation of multi-component Chirp signals in noisy environments

A classical time-varying signal, the multi-component Chirp signal has been widely used and the ability to estimate its instantaneous frequency （IF） is very useful. But in noisy environments, it is hard to estimate the 1F of a multi-component Chirp signal accurately. Wigner distribution maxima （WDM） are usually utilized for this estimation. But in practice, estimation bias increases when some points deviate from the true IF in high noise environments. This paper presents a new method of multi-component Chirp signal 1F estimation named Wigner Viterbi fit （WVF）, based on Wigner-Ville distribution （WVD） and the Viterbi algorithm. First, we transform the WVD of the Chirp signal into digital image, and apply the Viterbi algorithm to separate the components and estimate their IF. At last, we establish a linear model to fit the estimation results. Theoretical analysis and simulation results prove that this new method has high precision and better performance than WDM in high noise environments, and better suppression of interference and the edge effect. Compared with WDM, WVF can reduce the mean square error （MSE） by 50% when the signal to noise ration （SNR） is in the range of-15dB to -11dB. WVF is an effective and promising 1F estimation method.

The receiving of chirp signals with frequency invariant beam response

A frequency invariant response algorithm is proposed for getting over the signal distortion in high frequency when the beamformer receives the broadband signals. In this method, the weight vector of the reference beam pattern is designed firstly, then the weight vectors when the beam response in the frequency sub-bands approaching the reference response mostly are computed by optimized method or adaptive method. By this method, the beam pattern in each sub-band can be almost kept invariable. As a result, the receiving chirp signal can be improved. Owing to the adoption of the adaptive method to approach the optimized vectors, it has no restricting of the array format and the element directivity, and the method can be used widely.

A novel M-ary modulation technique based on optimal chirp signal design

A novel M-ary chirp modulation technique based on an optimal chirp signal design is proposed in this paper to offer higher data rate for an indoor wireless chirp spread spectrum communication system. Both linear chirp signals and combined chirp signals are used in this system to reduce the effect of the cross correlation, and simplify the complexity of the system. The optimal scheme of de- signing both linear chirp signals and combined chirp signals is discussed to minimize the value of the cross correlation and obtain a better system performance. Simulation results show that, compared with the binary orthogonal chirp modulation technique, the M-ary chirp modulation technique based on an optimal chirp signal set has a higher data rate with a reasonable bit-error rate （BER） perform- ance under both additive white gaussian noise （AWGN） channel and indoor wireless channel.

基于多特征融合的Chirp扩频通信调制样式分类识别方法

自动调制分类(AMC)在频谱监测和认知无线电中具有重要意义。近年来,Chirp扩频通信(CSS)由于其良好的抗干扰能力和稳健性得到了较大发展,但是对CSS信号的AMC方法却鲜有研究。针对这种情况,该文提出了一种基于多特征融合(MFF)的CSS信号调制分类方法,利用频谱和时频图特征融合学习并引入注意力模块来实现CSS信号调制识别。对11类CSS信号调制样式的仿真实验结果表明,该方法有优越的识别性能。

基于多分量LFM信号时频分析的水声多普勒和时延估计研究

在水声多普勒因子和时延估计研究实用化的进程中,利用多分量线性调频(LFM)信号实现估计的算法研究越来越普遍。针对多分量LFM信号时频域存有交叉项时各分量参数估计不准确的问题,提出基于非完全残差与脊线段匹配的自适应模态分解方法。该方法采用非完全残差函数保留了交叉点处的部分时频信息,利用脊线段匹配方法提供更精确的预设时频脊线,改进了各分量LFM信号调频斜率和起始频率的估计精度。联合两个估计量进一步给出了多普勒因子和时延估计的算法。仿真结果表示,较现有模态分解算法,所提改进方法有效解决了估计分量过程中交叉区间断裂带来的估计误差;水声多径的条件下,该方法的多普勒因子和时延估计精度优于对比的现有方法。

Chirp-UWB系统匹配滤波解调接收机的设计与实现

Chirp-UWB通信系统具有低功率谱密度、强抗干扰能力、易同步实现、远距离通信以及高的测距精度等优点,因而获得研究人员的广泛关注和研究。Chirp-UWB通信系统获得这些优良特性的关键之一是接收机采用匹配滤波解调技术实现。利用MATLAB/Simulink仿真平台,对匹配滤波解调的Chirp-BOK UWB通信系统进行了建模,构建的系统模型参数修改方便、实时性强。改变信道参数,能够检验系统在不同信噪比条件下的接收机的解调性能,为接收机硬件电路设计提供理论依据与参考,极大地减少了硬件电路设计和实验过程的盲目性。

分数阶Fourier域中多分量chirp信号的遮蔽分析

本文从分数阶Fourier变换与时频分布的关系入手,在离散分数阶Fourier变换算法基础上导出了单分量chirp信号分数阶Fourier谱强度的近似表达,并依据分数阶Fourier变换的线性性质,得到了调频率不同的两分量chirp信号间分数阶Fourier谱相互遮蔽的量化结果,给出了图例分析,并进行了仿真验证.通过本文的分析可以知道分数阶Fourier域中调频率不同的多分量chirp信号间的相互遮蔽主要取决于各自的幅度、调频率和采样时间.当多分量chirp信号幅度、调频率确定后,可以通过延长采样时间来降低各分量间的相互遮蔽.

Chirp信号及其在超声导波检测中的应用

传统的超声导波激励信号为窗函数调制的正弦波信号,其突出优点是频带窄,从而可以最大限度地抑制频散。但是此种信号在扫频测试和多模态、多频段检测中将需要逐次变换中心频率,且为达到较高的信噪比,每次测量采集时均需几十甚至上百次的平均,耗时耗能。提出了线性chirp和Gaussian-chirp 2种信号作为超声导波激励信号。线性chirp信号频带宽,通过对其检测接收信号进行后处理,可得到等同于传统的窗函数调制的正弦波信号激励时的检测接收信号,且被调制的正弦波频率可以为线性chirp信号频带范围内任意频率,特别适用于扫频测试和多模态、多频段检测。Gaussian-chirp信号幅频特性与高斯窗调制的正弦波信号完全相同,但单次测量即可达到常规激励信号上百次平均采集的信噪比,测量过程省时节能。实验采用2片置于铝板表面的压电晶片激励产生兰姆波,分别验证了2种chirp信号激励超声导波及其信号后处理方法的有效性。

基于Radon-Ambiguity变换和分数阶傅里叶变换的chirp信号检测及多参数估计

研究噪声环境中chirp信号的检测以及多参数估计问题.分析和比较了Radon-Wigner变换(RWT)法、Radon-Ambiguity变换(RAT)法和分数阶傅里叶变换(FRFT)扫描法的优缺点.提出了一种基于RAT和FRFT的新算法,并提出了幅度估计的一种适合于计算的表达式.最后通过计算机仿真验证了该方法的有效性.这种方法与RWT法和FRFT扫描法相比,将chirp信号的检测问题变为一维搜索问题,简化了计算,与单纯的RAT法相比,有效地解决了chirp信号的初始频率和幅度的估计问题.因此,适合于对chirp信号多参数联合估计.

复合材料板Chirp激励的Lamb波成像技术研究

作为一种快速、高效的无损检测方法,Lamb波技术在结构健康监测领域具有巨大的应用潜力并受到广泛关注。采用线性宽带Chirp信号作为激励信号,替代传统的窄带Tone burst信号。响应信号经过后处理可以解调出其带宽范围内任意中心频率的等效Tone burst响应信号。优化设计一种压电传感器,能够在低频段激励和接收纯净的A0模态。由于A0模态对板中缺陷非常敏感,使信号更便于分析。将这种传感器按照稀疏阵列的形式布置于准各向同性复合材料板上,对模拟缺陷进行检测。通过实验所得Chirp信号的检测数据解调出多个中心频率下的响应信号,结合椭圆成像技术和数据融合方法进行缺陷成像,实现了板中缺陷定位,并且多个频率下响应信号融合后的成像结果具有更高的分辨率、对比度和定位精度。

分数阶Fourier变换对多分量Chirp信号中心频率的分辨能力

研究了当多个同调频率的Chirp信号相近时,分数阶Fourier变换(FRFT)对中心频率的分辨能力问题。从FRFT在数值计算中对中心频率的估计区间出发,推导出了对中心频率的分辨能力表达式,根据偏微分方程进一步分析了采样时间、采样频率和信号调频率对分辨能力的影响。计算机仿真结果表明,当采样频率为400 MHz,采样时间为2μs;信号调频率的大小不超过100MHz/μs时,FRFT对中心频率的最小分辨力与调频率约成凹形抛物线关系,并且大于2 MHz.

基于修正Chirp-fourier变换的数字水印算法

提出了一种基于修正Chirp-fourier变换的数字水印算法,该算法直接在空域中,嵌入Chirp信号作为水印信息,用修正Chirp-fourier变换对这一特定结构的水印信息进行盲检测。仿真结果表明,该算法透明性好、检测速度快,在对常见的图像处理过程中具有鲁棒性。

一种有效的基于Chirplet自适应信号分解算法

基于线性调频小波 (chirplet)的自适应信号分解法 ,将待分析的线性调频 (Chirp)信号分解成为一组chirplet基函数的线性叠加 ,能够更清楚地表述Chirp信号的时频特征 .其中关键的问题 ,是如何自适应地估计与信号最匹配的chirplet,这将影响到自适应分解的效果 .目前 ,还没有一种有效chirplet估计算法 .本文提出了一种新的chirplet估计算法 ,该法充分利用了chirplet的特点 ,具有较高的参数估计精度 .

Chirp技术及其在海底浅层勘探中的应用

Ch irp子波因其宽的频带、长的脉冲延续时间和似白噪音的频率扫描特点,经过子波振幅包络和相关等处理后,具有很高的地层分辨率和资料信噪比,因而在海底浅地层调查中被广泛的采用。文中从Ch irp子波的波形生成开始,逐次讨论它的子波振幅包络和相关处理技术,以及它的分辨率的问题,最后以工程勘查中的实际例子证明Ch irp技术的优越性,同时还讨论了其技术的局限性,以便能对Ch irp技术有个深刻的了解。