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本文讨论了对于由不同杂波源(如海浪和云雨)产生的具有未知、时变统计特性的杂波回波同时进行滤波的问题。所讨论的方法(即广义自适应MTI方法)是对每一杂波频谱的平均多卜勒频率进行估计,然后用调谐在估计频率上的二项式动目标显示(MTI)滤波器进行杂波对消。本文分别讨论了几种估计方法,即:皮萨兰可(Pisarenko)模型、自回归(AR)模型、自回归滑动平均(ARMA)模型。通过限制每个雷达分辨单元中所期望的杂波数量保证了这些方法在雷达中的应用,结果也限制了算法的复杂程度。本文给出了解析和仿真结果以便比较采用不同方法所得到的性能。其性能用估计精度和改善因子(IF)表示。业已发现其改善因子相对于理论上的最优值而言损失可限制得很小。
This paper discusses the problem of simultaneous filtering of clutter echoes with unknown and time-varying statistical characteristics generated by different sources such as waves and cloud rain. The method in question, the generalized adaptive MTI method, is to estimate the average Doppler frequency for each clutter spectrum and then clutter with a binomial Moving Target Display (MTI) filter tuned to the estimated frequency Cancellation Several estimation methods are discussed in this paper, namely: Pisarenko model, autoregressive (AR) model and autoregressive moving average (ARMA) model. By limiting the number of clutter expected in each radar resolution unit, these methods are guaranteed to be used in radar and the results also limit the complexity of the algorithm. This paper presents the analytical and simulation results in order to compare the performance obtained with different methods. Its performance is expressed in terms of estimation accuracy and improvement factor (IF). It has been found that the loss of the improvement factor relative to the theoretical optimum can be limited to a very small extent.