一种正切调频与混沌编码复合信号脉冲压缩及旁瓣抑制

非线性调频信号和二相编码信号在脉冲压缩雷达中得到广泛使用,本文对其进行分析后推导出一类非线性正切调频-混沌二相编码复合调制信号的时域表达式,这种信号兼有原两种信号的特点,仿真结果表明,该复合调制信号容易产生和处理,其匹配滤波结果对多普勒频移也不敏感,但对其匹配滤波后的离散距离旁瓣用常规的方法抑制效果不佳,本文采用网络综...     

Taylor四相编码信号脉冲压缩算法优化与仿真

通过对Taylor四相编码信号的研究，分析了其优缺点，并根据其不足和雷达信号处理中的实际情况，提出多普勒频率多通道补偿法和频域旁瓣抑制法，并进行仿真。仿真结果表明，这两种方法是可行的，较好地改善Taylor四相编码信号的脉冲压缩性能。     

旁瓣抑制下的编码信号选取方法

雷达系统中,相位编码信号对多普勒频率非常敏感。根据相位编码信号的这一特点,对于旁瓣抑制条件下的相位编码信号,介绍一种综合选取法,对其进行码型设计,以达到尽量降低旁瓣抑制滤波器对于信号多普勒性能的影响的目的,并与传统的利用最优码设计编码信号码型的性能进行比较。     

雷达频域脉压系统的设计

采用脉冲压缩技术的雷达信号形式主要有线性调频、相位编码和非线性调频信号等.高性能、高速、超大规模DSP芯片及可编程器件的问世和广泛应用,解决了脉冲压缩技术在实际应用中的瓶颈问题.通过采用多通道频域脉压技术,解决了采用大时宽相位编码信号的雷达系统由于脉冲压缩处理而造成运动目标检测性能下降的问题.     

舰载超低空搜索雷达脉冲压缩技术特点研究

本文从超低空搜索雷达对脉冲压缩技术的要求出发,从脉压主副瓣比,时间带宽积,多普勒频率敏感性,展宽及压缩方法实现等几方面,对线性调频,二相编码,法兰克多相码,Ｐ１,Ｐ２,Ｐ３,Ｐ４多相码,基于非线性调频的多相码等脉压体制进行了详细的分析,指出ＮＬＦＭ多相码脉压技术是超低空搜索雷达的较好选择,文中还详细介绍了该型多相码的产生实现技术以及雷达整机试验结果。     

一种抑制模糊度函数特定区域旁瓣级的波形设计方法

针对发射波形为相位编码,探测低速运动目标时存在的模糊度函数旁瓣峰值过高,容易淹没微弱目标的问题,提出了在特定区域最小化模糊度函数旁瓣级的多相编码波形设计优化算法。该算法利用低速运动目标多普勒频移很小的特点,对模糊度函数进行简化,提出了离散模糊度函数的概念,在此基础上将模糊度函数峰值附近旁瓣级的最小化转化为离散模糊度函数峰值附近旁瓣级的最小化问题,并利用循环算法进行相位编码的设计。计算机仿真结果表明,所提出的波形设计方法能够有效抑制离散模糊度函数原点附近的旁瓣级,提高探测系统对微弱低速运动目标的检测能力。     

一种低截获概率雷达的移频干扰研究

设计了一种调频连续波—相移键控复合调制的低截获概率雷达信号,并分析了频率、调频斜率两个特征对信号匹配滤波的影响。从这两个特征入手研究利用特征变化对雷达信号的干扰。详细分析了假多普勒移频干扰对雷达信号的影响和干扰效果,在此基础上提出了锯齿波移频改进干扰方法,并给出了仿真实验,结果表明可以实现较好的干扰效果,验证了移频干扰是对低截获概率雷达的一种有效干扰。     

低信噪比下非线性调频信号瞬时频率估计

多项式Wigner-Vill分布(PWVD)能很好地对非线性调频信号进行分析.利用PWVD平面七的尖峰值作为非线性调频信号的瞬时频率的估计.在PWVD上,对估计的瞬时频率进行差分,利用信号差分方程的特征,结合直方图统计方法在低信噪比情况中从PWVD平面提取方差很小的瞬时频率点,从而达到精确估计的目的.     

线性调频信号仿真及其特性分析

线性调频(LFM)信号是雷达信号处理系统中应用较为广泛的大时宽带宽积信号。针对线性调频信号进行了时频特性分析,然后运用模糊函数对该信号的性能进行分析,得出该信号具有良好的距离分辨率和速度分辨率,最后对线性调频信号的脉冲压缩过程和结果进行了讨论,并得到了雷达目标回波信号经过脉冲压缩后的仿真结果。     

一种线列阵反卷积波束形成高分辨算法

传统的水声阵列波束形成方法在非声源方向存在旁瓣,直接影响目标的检测精度。本文针对波束形成中旁瓣抑制问题,在常规波束形成中引入反卷积算法,提出一种基于反卷积的高方位分辨算法。将常规波束形成作为一个线性系统,由均匀线阵的波束指向性出发,利用Richardson-Lucy迭代求解点声源散射函数,以常规波束形成输出结果作为系统的输入,与声源点散射函数相卷积,抑制阵列指向性函数对输出的影响,缩窄主瓣,抑制旁瓣。在此基础上给出纯净目标辐射噪声的提取方法,归纳总结算法流程。仿真结果表明,反卷积波束形成算法能够实现多目标的调制谱分离,具有更优的多目标角度分辨力,有效提高目标检测性能。     

基于卡尔曼滤波的水声信号线谱增强（英文）

To detect weak underwater acoustic signals radiated by submarines and other underwater equipment, an effective line spectrum enhancement algorithm based on Kalman filter and FFT processing is proposed. The proposed algorithm first determines the frequency components of the weak underwater signal and then filters the signal to enhance the line spectrum, thereby improving the signal-to-noise ratio(SNR). This paper discussed two cases: one is a simulated signal consisting of a dual-frequency sinusoidal periodic signal and Gaussian white noise, and the signal is received after passing through a Rayleigh fading channel;the other is a ship signal recorded from the South China Sea. The results show that the line spectrum of the underwater acoustic signal could be effectively enhanced in both cases, and the filtered waveform is smoother. The analysis of simulated signals and ship signal reflects the effectiveness of the proposed algorithm.     

锚链力实测样本的统计预报

锚链力的实测样本是一组均值不为零的随机信号，且存在着高，低频两种成分。它的预报方法以往有所不同。本文将总的锚链力实测信号的分解成高、低频两部分，分别作统计分析。对低频信号用威布尔超越概率作图求出最可能极大值，再与高频信号的正１／３有义值及预报力相加，作为总的最大值。     

一种级联型自适应滤波器的混响抑制技术

在浅海环境中混响是造成主动声呐性能下降的主要原因之一。混响是由发射信号引起的,其频域上覆盖区域与发射信号基本重合,时域上与发射信号及目标回波强相关,这给混响和目标的分离造成了很大的困难。本文借鉴PD雷达中的动目标检测方法,提出一种适用于声呐动目标检测的滤波器设计算法。该算法利用运动目标回波和混响在时频域上的不同特性,设计了级联自适应滤波器实现混响抑制和目标增强。在此基础上进行匹配滤波等处理可以获得理想的效果。该算法可大幅提高信混比,有效改善运动目标的检测能力。     

A signal decomposition method based on repeated extraction of maximum energy component for offshore structures

Contrary to most signal decomposition methods that usually decompose an original signal into a series of components simultaneously, a novel approach based on repeated extraction of Maximum Energy Component (MEC) is proposed. The approach starts from determination of the MEC referring to the estimated Power Spectral Density (PSD) function, and then represents the MEC by employing an exponential function to fit the original signal. By defining a stopping criterion based on two adjacent estimated PSDs, each MEC can be accurately extracted with an improved performance throughout the entire signal decomposition. To verify the proposed method, a single degree-of-freedom system subject to harmonic loads has been examined. Numerical results show that the analytical response can not only be decomposed into four MECs corresponding to the excitation and the system, respectively, but also provide an accurate estimation of natural frequency and damping ratio of the system. Meanwhile, by observing results from the Ensemble Empirical Mode Decomposition (EEMD), Variational Mode Decomposition (VMD) and Prony based on state-space model (Prony-SS), an improved decomposition accuracy has been achieved from the proposed approach. Furthermore, experimental data from the Norwegian Deepwater Programme and two sets of field-test data from one fixed offshore platform and an offshore wind turbine have been used to demonstrate the correctness of the developed signal decomposition method. It is noted that divergence in results by Prony-SS can be observed when a very large model order is used, while the proposed method provides the better decomposition and reconstruction of signals.     

相位检测频率系统的设计

提出一种新的相位重合检测技术测量频率,相位重合检测技术是用被测信号和标频信号的相位重合点来触发测量闸门,使测量闸门信号不仅与被测信号,而且与标频信号都同步,这种方法消除了一般测频系统中的±1字的误差,并且实现宽频率范围内的高精度测量。     

一种通用型基于经验模态分解的小波阈值滤波方法研究

首先针对中高频水声信号,提出一种改进的经验模态分解加小波软阈值滤波方法;然后将信号进行带通滤波处理及经验模态分解,将分解得到的各个模态转换为频域信号,采用小波软阈值方法在频域上对这些模态进行滤波,最后对信号进行重构,并将其转换为时域信号。分别采用本方法和原时域上的小波阈值方法对不同频率的水声信号进行滤波,经计算分析可知,对频率小于800 Hz的水声信号,采用原方法可获得较好的滤波效果;当信号频率大于800 Hz时,采用本方法的滤波效果更好,因此应针对不同频率的水声信号,选择合适的滤波方法,以获得满意的滤波效果。     

A new source number estimation method based on the beam eigenvalue

Most source number estimation methods based on the eigenvalues are decomposed by covariance matrix in MUSIC algorithm. To develop the source number estimation method which has lower signal to noise ratio and is suitable to both correlated and uncorrelated impinging signals, a new source number estimation method called beam eigenvalue method (BEM) is proposed in this paper. Through analyzing the space power spectrum and the correlation of the line array, the covariance matrix is constructed in a new way, which is decided by the line array shape when the signal frequency is given. Both of the theory analysis and the simulation results show that the BEM method can estimate the source number for correlated signals and can be more effective at lower signal to noise ratios than the normal source number estimation methods.     

基于FFT-FS频谱细化技术的船模耐波性试验测量信号分析方法研究

针对FFT频率分辨率受限制且精度不高的缺点以及船模对波浪的运动响应频率是低频且窄带的特点,采用FFT-FS方法对测量的运动响应信号在其对应的窄带频段内进行频谱细化获得更细致准确的信号频域特征;通过对该算法的运算量及分析精度的研究表明该算法是一种运算效率高、分析精度高且受信号本身信噪比影响小的频域分析方法;通过对船模运动响应的仿真信号和实测信号的分析表明该算法确实能有效地用于船模耐波性试验测量数据的定量分析。     

A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines

The Hilbert-Huang transform (HHT) has been widely applied and recognised as a powerful time-frequency analysis method for nonlinear and non-stationary signals in numerous engineering fields. One of its major challenges is that the HHT is frequently subject to mode mixing in the processing of practical signals such as those of offshore wind turbines, as the frequencies of offshore wind turbines are typically close and contaminated by noise. To address this issue, this paper proposes a new time-frequency analysis method based on single mode function (SMF) decomposition to overcome the mode mixing problem in the structural health monitoring (SHM) of offshore wind turbines. In this approach, the structural vibration signal is first decomposed into a set of window components using complex exponential decomposition. A state-space model is introduced in the signal decomposition to improve the numerical stability of the decomposition, and then a novel window-alignment strategy, named energy gridding, is proposed and the signals are constructed in the corresponding gridding. Furthermore, energy recollection is implemented in each gridding, and the reassembling of these components yields an SMF that is comparable to the intrinsic mode function (IMF) of the HHT, but with a significant improvement in terms of mode mixing. Four case studies are conducted to evaluate the performance of the proposed method. The first case attempts to detect three different frequencies in a simulated time-invariant signal. The second case attempts to test a synthesised signal with segmental time-varying frequencies (each segment contains three different frequencies components). The analysis results in these two cases indicate that mode mixing can be reduced by the proposed method. Furthermore, a synthesised signal with slowly varying frequencies is used. These analysis results demonstrate the effective suppression of non-relevant frequency components using SMF decomposition. In the third case, the experimental data from vortex-induced vibration (VIV) experiments sponsored by the Norwegian Deepwater Programme (NDP) are used to evaluate the proposed SMF decomposition for vibration mode identification. In the final case, field data acquired from an offshore wind turbine foundation and offshore wind turbine are analysed. The mode identification results obtained using SMF decomposition are compared with those produced by the HHT. The comparison demonstrates superior performance of the proposed method in identifying the vibration modes of the VIV experimental and field data.