α稳定分布噪声环境中循环平稳信号线性滤波与参数估计方法研究

Time difference of arrival (TDOA) and differential Doppler shift or frequency difference of arrival (FDOA) estimation are central topics of signal processing for decades due to their applications to the problem of locating a signal source in communication, radar, and sonar systems, and to military reconnaissance and many walks of life. In order to overcome the degradation problem of the conventional cyclic correlation matched filter (CCMF) in the presence of α stable distribution impulsive noise, by exploiting the cyclostationarity property of signals with fractional lower order cyclic statistics, a new fractional lower order cyclic correlation matched filtering method based on the max-output signal-to-noise ratio criterion is derived. Furthermore, the implementation of the fractional lower order cyclic correlation matched filter is demonstrated. For time delay estimation in the presence of multipath propagation and impulsive noise, we attempt to remove any restriction on the number of multipaths. Using the idea of multipath cancellation and multipath equalization respectively, two classes of fractional lower order cyclic algorithms are developed to determine the TDOA when the discriminable multipath time delay and the indiscriminate multipath time delay are taken into account. Performance analysis and computer simulations of these two classes methods, as well as comparison with other conventional cyclic TDOA estimation methods, are given. It is shown that the conventional fractional lower order cyclic TDOA and,-or FDOA estimation algorithms will degenerate when the cycle frequency error of the signal of interest is present. An improvement is then made to the TDOA and,-or FDOA estimation methods to alleviate the problem. Theoretical analysis of the influence of cycle frequency error on the conventional cyclic methods are given, and the new cycle frequency error correction algorithms are developed. With the use of the cycle frequency error correction effectively, the impact of cycle frequency error is reduced, and the effectiveness and robustness of the new methods can be improved. The study and complement of this project provide a useful technical support for the cyclic filtering methods and the signal selective TDOA/FDOA estimation algorithms.

针对α稳定分布噪声中循环平稳信号线性滤波和时延与多普勒频移估计方法存在的缺点和局限性，本项目以分数低阶循环统计量为研究工具进行研究。充分挖掘分数低阶循环统计量对脉冲噪声、高斯噪声和干扰的抑制能力，研究分数低阶循环相关匹配滤波器的设计和实现方法。在深入研究无限信道特别是射频信号、噪声和多径传播特性的基础上，研究新型的分数低阶循环高精度多径时延估计方法，解决多径效应对循环时延估计方法性能的影响。系统研究循环频率误差对时延与多普勒频移估计方法的影响，研究α稳定分布噪声中高精度循环频率估计技术，研究发展有效抑制循环频率误差的参数估计方法，得到性能更为优良且适用范围更宽的分数低阶循环时延与多普勒频移估计新方法，从而改善算法的估计精度和稳健性。本项目的研究成果对于提高稳定分布噪声环境中循环平稳信号的检测精度和时延与多普勒频移估计方法的性能具有一定的意义。

时间延迟和多普勒频移估计是目标跟踪和定位中的重要技术手段，在通信、雷达、声呐、导航等系统中应用广泛。通信、雷达、声呐等系统中使用的许多调制信号都具有循环平稳特性，利用信号的循环平稳特性开发高性能的信号处理方法在国内外受到了广泛重视。大量研究表明，雷达、声纳和无线通信系统的实际噪声中含有大量脉冲成分，这类噪声更适合用α稳定分布模型来表示。我们针对α稳定分布噪声中循环平稳信号的线性滤波和时延、多普勒频移估计等问题进行了深入研究。首先，研究了循环平稳信号的线性滤波问题，提出了基于分数低阶统计量的自适应滤波和分数低阶循环匹配滤波方法。循环频率是循环平稳信号处理方法应用的一个关键因素，针对这一问题本项目研究了基于分数低阶循环统计量的载频估计方法，提高了脉冲噪声中AM信号和PSK信号的载频估计精度。重点研究了基于M次方谱和功率谱的QPSK信号载频估计方法，以及基于小波变换法和延迟相乘法的QPSK信号码率估计方法。在此基础上，提出了基于四次方谱载频估计的DSP实现方法。针对时延与多普勒估计问题，提出了不需要估计权系数的多循环频率时延估计方法，提出了广义分数低阶循环时延估计方法和广义分数低阶循环模糊函数，有效的提高了循环平稳信号参数估计性能。研究了脉冲噪声下多径时延估计方法，提出了能够适应不同噪声的高分辨率广义EM和广义WR多径时延估计方法。针对脉冲噪声和相干循环平稳信号的波达方向估计问题开展研究，提出了两类能够抑制脉冲噪声和相干信号的DOA估计方法。此外，在MIMO系统的预编码和信道估计方面开展研究工作，取得了较好的成果。

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