共查询到20条相似文献,搜索用时 125 毫秒
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当雷达发射脉冲个数比较少、噪声方差比较大时,单独使用Wigner-Hough变换(WHT)估计目标径向加速度时会有较大的误差,针对这一问题提出一种Wigner-Hough变换和中值滤波相结合的估计加速度方法,首先研究了相参脉冲回波信号Wigner-Ville分布(WVD)特点,讨论了谱峰位置与瞬时频率的关系,然后采用Hough变换和中值滤波方法时Wigner-Ville分布(WVD)峰值信息进行处理来提取信号的调频率,最后根据加速度估计公式估计出目标加速度,最后进行了仿真验证,在不同积累脉冲个数和信噪比条件下证明了该算法的有效性. 相似文献
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水下目标辐射线谱噪声的多普勒信息可用于估计目标上的噪声源参数。由于噪声源的低频和低速特性,其多普勒频移变化微弱,需要在选定窄带内进行高分辨率时频分析。Wigner-Ville分布(WVD)具有良好的时频聚焦性能,但是当频率分辨率要求较高时,其计算量和存储空间长度也大幅增加。该文提出一种选带细化WVD的快速数值计算方法。该方法对线性调频Z变换(CZT)进行改进并与WVD相结合,可以大幅提高选定窄带内高分辨率WVD时频分析的计算效率。数值仿真和海试数据验证了方法的有效性。 相似文献
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时频分布与雷达信号的多目标分辨 总被引:1,自引:0,他引:1
编队飞行的目标,由于它们之间的间距很小。利用目标回波多普勒频率的差别有可能分辨目标。本文介绍了时频分布的基本概念。用线性调频(LFM)信号模型来表示多普勒回波信号的变化.采用时频分布的方法来分辨编队目标。并举用常见的Wigner-Vill分布(WVD)、Choi—Williams分布、Wigner-Hough变换对窄带多目标雷达信号数据进行了分析比较。 相似文献
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针对合成孔径雷达(Synthetic Aperture Radar,SAR)成像中真假目标难以区分的问题,提出了一种极化合成孔径雷达(Polarmetric SAR,PolSAR)成像固定极化转发式干扰诱饵目标鉴别方法。该方法首先通过极化熵对目标进行分类,提取低极化熵的目标并对目标进行Wigner-Ville分布(Wigner-Ville Distribution,WVD)变换,再利用Hough变换计算WVD图像中的曲线斜率,将计算出的多普勒调频率与理论调频率进行对比,进而完成真实目标与假目标的鉴别,最终通过极化对消对鉴别出的目标进行抑制。实验结果验证了该方法的有效性。 相似文献
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基于时频分析的跳频信号的参数盲估计方法 总被引:2,自引:0,他引:2
采用平滑伪Wigner—Ville分布(SPWVD)来估计跳频信号载波频率,提取频率脊线,对频率脊线进行一次小波变换来估计未知跳频信号的参数,该方法可以在不需要知道跳频信号任何先验参数的情况下,估计出信号的码元速率,载波频率改变时刻。对频率编码信号的WVD,PWVD和SPWVD结果作了比较,指出了SPWVD的优点,给出了基于时频分析跳频信号参数估计的具体算法步骤.计算机仿真表明该方法是行之有效的。 相似文献
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针对在低信噪比下机载气象雷达回波多普勒参数(谱矩)估计不准确的问题,本文在气象目标的雷达回波频谱服从高斯分布的基础上,给出了一种利用协方差矩阵分解的快速参数化谱矩估计算法。通过理论分析,推导出雷达回波的协方差矩阵具有范德蒙结构特性,进而将用于谱矩估计的代价函数转化为类傅里叶变换结构,然后进一步通过快速傅里叶变换和高斯加权滑窗计算代价函数,实现快速的谱矩估计。仿真实验结果表明,该方法在信噪比低于5 dB时仍可以有效估计雷达回波的谱矩参数,同时运算复杂度大大降低,而且在谱宽值较大情况下仍能保持较好的估计性能。 相似文献
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现代的测量雷达通常要对目标进行实时跟踪,因此必须实时、准确地估计出目标方位。该文针对测量雷达的特殊情况,利用高分辨DOA估计理论,提出了一种新的实时测角方法。和传统的基于比相原理的FFT法相比,在计算量基本保持不变的情况下,新方法在低信噪比时具有好得多的估计效果。 相似文献
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为了实现少快拍、低信噪比(SNR)条件下的水下目标快速方位估计,该文建立矢量水听器阵列方位估计稀疏表示模型。利用实值转化技术将复数方向矩阵转化到实数域,以便利用平滑L0算法对稀疏信号矩阵进行重构从而得到方位估计结果。该文改进平滑L0算法,利用收敛性更好的复合反比例函数(CIPF)函数作为平滑函数以及提出促稀疏加权的方法,该方法通过加权的方式修正噪声条件下L2范数作为迭代初始值偏离稀疏解较远的问题来促进算法快速收敛于稀疏解。通过仿真验证了该文提出的基于实值转换的促稀疏加权平滑L0算法在少快拍、低信噪比的条件下可以实现优于传统子空间类算法的性能,并且在保证性能的同时,显著提高方位估计的速度。 相似文献
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Bayesian Multi-Object Filtering With Amplitude Feature Likelihood for Unknown Object SNR 总被引:1,自引:0,他引:1
In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multiple-target state estimation by obtaining more accurate target and false-alarm likelihoods. Target amplitude feature is well known to improve data association in conventional tracking filters, such as probabilistic data association and multiple hypothesis tracking, and results in better tracking performance of low signal-to-noise ratio (SNR) targets. The advantage of using the target amplitude approach is that targets can be identified earlier through the enhanced discrimination between target and false alarms. One of the limitations of this approach is that it is usually assumed that the SNR of the target is known. We show that the reliable estimation of the SNR requires a significant number of measurements, and so we propose an alternative approach for situations where the SNR is unknown. We illustrate this approach in the context of multiple targets for different SNRs in the framework of finite set statistics (FISST). Furthermore, we illustrate how this can be incorporated into approximate multiple-object filters derived from FISST, including probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. We present simulation results for Gaussian mixture implementations of the filters that demonstrate a significant improvement in performance over just using location measurements. 相似文献
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In the channel-varying environment, it is very important to estimate the signal to noise ratio (SNR) of received signal and to transmit the signal effectively for the modern communication system. The performance of existing non-data-aided SNR estimation methods are substantially degraded for high level modulation scheme such as M-ary amplitude and phase shift keying or quadrature amplitude modulation. In this paper, we propose a SNR estimation method which uses zero point auto-correlation of received signal per block and auto/cross-correlation of decision feedback signal in orthogonal frequency division multiplexing (OFDM) system. Proposed method can be studied into two types; Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be possible for the practical implementation due to correlation based the estimation method and they show more stable estimation performance than the previous SNR estimation methods. Also, we mathematically derive the SNR estimation expression according to computational difference of auto/cross-correlation. Finally, Monte Carlo simulations are used to verify the proposed method. 相似文献