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杂波边缘
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  clutter edges
    The analytic results show that an improved performance over TM is obtained both in homogeneous background and in clutter edges situation,and QBW exhibits robustness in multiple interfering targets environment.
    分析结果表明,它在均匀背景和杂波边缘背景中的性能均比TM获得了改善,同时对多目标也呈现了较好的鲁棒性。
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  clutter edge
Again, the isolated target at range bin 150 is detected and the target at range bin 230 is masked by the clutter edge.
      
Figure 19 shows the loss in IF around the clutter edge scenarios of Figure 18, and show that the losses can be substantial.
      
Think about a weak edge at object boundary, or a strong clutter edge in the background.
      
  clutter edges
However, the false alarm rate performance of OSSO-CFAR(Constant False Alarm Rate) scheme at clutter edges is worsen with increasing the postdetection integrated pulses.
      
Notice that after learning, not only clutter edges are suppressed but also the correct edge polarities are recovered.
      
Output from a directional Gaussian edge detector shows that there are many clutter edges present as potential distractors.
      
We want to inhibit clutter edges and recover correct polarity.
      


In order to make the detector perform robustly, an intuitive solution can be obtained by censoring the clutter observations from large and small deriations. In recent studies,censoring techniques have been implemented using order statistics (OS)[1] and trimmed mean (TM)[2] meth-ods. In order to utilize censored data, we present a quasi-best weighted (QBW) order statistics method resulting in an improved detection performance while censoring outliers. Under the assump-tion of Swerling I target and Rayleigh distributed...

In order to make the detector perform robustly, an intuitive solution can be obtained by censoring the clutter observations from large and small deriations. In recent studies,censoring techniques have been implemented using order statistics (OS)[1] and trimmed mean (TM)[2] meth-ods. In order to utilize censored data, we present a quasi-best weighted (QBW) order statistics method resulting in an improved detection performance while censoring outliers. Under the assump-tion of Swerling I target and Rayleigh distributed clutter.the analytic expressions of Pfa,,Pd and ADT are derived. The analytic results show that an improved performance over TM is obtained both in homogeneous background and in clutter edges situation,and QBW exhibits robustness in multiple interfering targets environment. In specific cases,QBW reduces to CMLD[4]

为了提高恒虚警检测器在均匀背景中的检测性能及增强对干扰的鲁棒性,本文提出了一种准最佳加权有序统计恒虚警检测器,并应用了文献[3]提出的自动筛选技术。在Swerling Ⅰ型目标及瑞利杂皮假设下,本文推导出了它的P_(fa)、P_d及ADT的数学解析表达式。分析结果表明,它在均匀背景和杂波边缘背景中的性能均比TM获得了改善,同时对多目标也呈现了较好的鲁棒性。在特殊情况下,QBW退化为CMLD。

The performance analysis of MOSCMCFAR detector in nonhomogeneous background caused by clutter edges and multiple targets is carried out. The analytic expression of the peak of false alarm rate at clutter edges is derived. The analytic results show that the advantage of MOSCM mainly lies in nonhomogeneous background, the ability of MOSCM to control the rise of false alarm is more effective than that of GOSCA and OS, while MOSCM also exhibits good robustness in multiple target situations. A compromise can be made...

The performance analysis of MOSCMCFAR detector in nonhomogeneous background caused by clutter edges and multiple targets is carried out. The analytic expression of the peak of false alarm rate at clutter edges is derived. The analytic results show that the advantage of MOSCM mainly lies in nonhomogeneous background, the ability of MOSCM to control the rise of false alarm is more effective than that of GOSCA and OS, while MOSCM also exhibits good robustness in multiple target situations. A compromise can be made in homogeneous background leading to a great improvement on the performance of MOSCM in multiple target situations. For example,when IL=4, IR=2, an improvement of nearly 2 dB over OS is obtained.

分析了MOSCM恒虚警(CFAR)检测器在多目标和杂波边缘非均匀背景中的性能,给出了它在杂波边缘情形中虚警尖峰的数学解析表达式.分析结果表明,它带来的优势主要体现在非均匀背景中,它在杂波边缘中的虚警控制能力比GOSCA和有序统计(OS)有效,对多目标情况也呈现了较好的鲁棒性,它可以均匀背景中较小的代价换取在多目标值况下性能的较大改善,如当IL=4,IR=2时,它比OS改善了近2dB.

Aiming at the detection and tracking of multiple point targets distributed in cl oudy background,a new method,namely the multi-stages trace association is pre se nted.In the preprocessing,a module based on local auto-adaptive contrast thres h old is given to remove the slowly varying clutter and at the same time,the nois e with high intensity and the edge of clutter are filtered with a bi-directional high order correlation filter.When constructing deep-searching trees the adopt ion of searching strategy from...

Aiming at the detection and tracking of multiple point targets distributed in cl oudy background,a new method,namely the multi-stages trace association is pre se nted.In the preprocessing,a module based on local auto-adaptive contrast thres h old is given to remove the slowly varying clutter and at the same time,the nois e with high intensity and the edge of clutter are filtered with a bi-directional high order correlation filter.When constructing deep-searching trees the adopt ion of searching strategy from both directions can be pruned earlier.In the post pro cessing the energy along the candidate trajectories are accumulated and it can c ut the false traces efficiently.Moreover,for the spatial-temporal consistency a nd continuity of the targets it can lock the true targets as soon as possible.Th e experimental results show that it can detect and track dim overlapping point t a rgets with arbitrary trajectories accurately.It is advantageous over the traditi onal correlation algorithms in searching space and time complexity.

构建了一种多阶段轨迹融合算法,预处理阶段利用局部对比度自适应门限抑制杂波,同时结合高阶相关滤波器剔除噪声和杂波边缘.在轨迹互联阶段,多阶段的双向搜索算法辅佐以目标航迹的能量累积,能够尽早锁定目标.实验分析表明,该方法能够准确的检测和跟踪作任意轨迹运动的多个点目标,在搜索空间和时间复杂度上明显优于传统的相关性算法.

 
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