【作者】 李丽；

【导师】 邱天爽；

【作者基本信息】 大连理工大学 ， 信号与信息处理， 2013， 博士

【摘要】 目标参数估计和定位是雷达信号处理的一个重要内容,被广泛应用在雷达、声纳、无线通信系统定位、无线电监测和军事等领域。近年来,双基地MIMO雷达目标参数估计引起了学者的广泛关注,但是目前的回波信号模型大都假定回波信号中多普勒频率为实常数或者不考虑多普勒频率,实际上由于目标运动速度、运动方向等参数都是不断变化的,回波信号中必然存在时变的多普勒频率,若不考虑其时变特性,则必然会导致估计精度下降。针对已有算法及模型的不足,本文重点研究了双基地MIMO雷达回波信号模型及目标参数估计问题。本文主要创新工作如下：(1)针对窄带回波信号中存在时变多普勒频率问题,提出了具有线性时变多普勒频率的回波信号模型。针对这一信号模型提出了基于分数阶傅里叶变换和分数阶功率谱的两种算法,实现了高斯噪声环境下的目标参数联合估计,并结合分数低阶统计量理论,提出了FLOS-FPSD算法,实现了Alpha稳定分布噪声环境下目标参数的较好估计,具有较高的估计精度。进一步地,推导了上述参数估计的克拉美罗界。针对运动目标的三维运动会引起回波信号中存在非线性时变多普勒频率的问题,提出了具有非线性时变多普勒频率的扩展回波信号模型,并在此基础上提出了基于分数阶模糊函数算法和分数低阶统计量的FLOS-FAF算法,在混有脉冲噪声和高斯噪声的环境下实现了目标参数的较高精度估计。(2)针对宽带回波信号相对发射信号包含有多普勒频移尺度因子和时延问题,本文提出了一种扩展的宽带回波信号模型,并在此基础上提出了基于分数阶傅里叶变换和分数阶功率谱的两种目标参数估计算法,实现了高斯噪声环境下目标参数的联合估计。另外,将FLOS-FPSD算法应用到宽带回波信号参数估计问题中,实现了脉冲噪声干扰环境下目标参数较高精度估计及定位。并进一步推导了宽带回波信号模型中参数估计的克拉美罗界,得到了参数估计克拉美罗界的闭合表达式。(3)针对飞机目标运动状态信息获取上存在障碍和回波信号具有时变多普勒频率的问题,提出了一种扩展的近场连续回波信号模型,将分数阶模糊函数理论与投影近似子空间跟踪算法相结合提出了基于FAF-PAST的目标参数估计算法,实现了高斯噪声环境下飞机目标参数的动态估计。同时考虑了脉冲噪声的影响,提出了基于FLOS_FAF的投影近似子空间角度动态估训(FF_RLM_PAST)算法,既能有效抑制脉冲噪声的干扰,也能很好实现飞机目标参数的动态估计。更多还原

【Abstract】 Target parameter estimation is an important aspect in radar signal processing, which has been widely used in radar, sonar, wireless communication positioning system, radio monitoring and military fields. In recent years, target parameter estimation in bistatic MIMO radar system has attracted more and more attentions, but Doppler frequency was ignored or assumed as time-invariant in most existing models. In fact, due to the velocities and moving directions of targets varying constantly, the received signals contain Doppler frequency with time-variant. If Doppler frequency is assumed as time-invariant, the performance of parameter estimation will degrade. To overcome the drawbacks of traditional models and methods, this dissertation mainly studies the model for received signals and the target parameter estimation methods under different noise environments in bistatic MIMO radar system. The main contributions are listed as follows:(1) Since a narrow received signal contains time-varying Doppler frequency, this dissertation proposes a signal model with linear time-varying Doppler frequency. For this signal model, by the fractional Fourier transform (FRFT), two approaches and the fractional power spetrum density are proposed to estimate target parameters in the Gaussian noise environment. This dissertation, combining fractional lower order statistics (FLOS) and fractional power spectrum, proposes a novel approach to precisely estimate target parameters in the impulsive noise environment. Furthermore, the Cramer-Rao bound for target parameter estimation is derived. Due to three dimensional motion characteristics of the target, the received signal may contain nonlinear time-varying Doppler frequency. We propose an extented signal model, then propose the fractional ambiguity function (FAF) algorithm and the FLOS-FAF algorithm to accurately estimate target parameters in both Gaussian and impulsive noise environments.(2) Since the wideband echo signal, in comparison with the transmitted signal, often contains time delay and Doppler stretch, which can not be estimated availably by the narrowband model, this dissertation proposes an extented wideband received signal model, then FRFT based algorithm and fractional power spectrum density algorithms are proposed to estimate jointly target parameters in the Gaussian noise environment. Then the FLOS-FPSD algorithm is applied for target paramters estimation of wideband signal model, which accurately achieves target parameter estimation and localization in the impulsive noise environment. Furthermore, the Cramer-Rao bound for target parameter estimation is derived and computed in closed form which shows its good performance.(3) For the difficulty on collection of moving information on a civil airplane, this dissertation proposes an extented model for received signals. We combine FAF and the projection approximation subspace tracking (PAST) algorithm and propose the FAF-PAST method to achieve airplane parameter dynamic estimation in the Gaussian noise environment. Furthermore, the FF-RLM-PAST algorithm based on FLOS-FAF method is proposed to estimate airplane parameters in the impulsive noise environment, which effectively suppresses the impulse noise and achieves parameter dynamic estimation of airplane accurately.更多还原