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摘要: 自适应检测技术可有效提升岸对海警戒雷达海面目标探测性能，但海岛和陆地会导致成片或离散强杂波点，污染协方差矩阵估计的样本，海杂波的复杂性使得整片海杂波难以采用单一模型描述。为解决海面目标自适应检测时面临的非均匀样本参与协方差矩阵估计时杂波抑制性能严重下降问题和海杂波建模准确性不高的问题，该文提出一种面向海面目标检测的陆海分离和海面分区算法。首先，根据陆地回波序列的相位之间具有强相关性，而海洋回波序列为随机值这一特性，区分陆地杂波和海杂波；然后，根据擦地角对海杂波分区，拟合出每个分区的最优分布后选择合适的检测器进行自适应检测；最后，基于某S波段雷达实测数据验证该算法，检测结果与性能分析表明该算法相对传统算法可有效提高海面目标的检测率。Abstract: Adaptive detection can effectively improve the detection performance of marine surveillance radars; however, the islands or lands introduce discrete or flaky strong clutter, which may result in wrong covariance matrix estimation. Meanwhile, the complexity of the sea clutter complicates the use of a single model to describe the whole sea clutter. To solve the problem of serious degradation of clutter suppression performance when non-uniform samples participate in covariance matrix estimation and inaccuracy of sea clutter modeling, a land-sea separation and sea surface zoning algorithms are proposed for sea surface target detection. First, the land clutter and sea clutter are distinguished according to the characteristics that the phases of land echo sequences are strongly correlated while the phases of ocean echo sequences are random. Second, the sea surface is zoned according to the rubbing angle; further, the optimal distribution suited for each sea clutter zone is fitted and the appropriate adaptive detection method is selected according to the clutter distribution. Finally, the proposed algorithm is validated based on the measured data of an S-band radar. The results show that the proposed algorithm can effectively improve the detection performance of sea surface targets compared with the traditional detection algorithm.
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Key words:
- Marine Surveillance radar /
- Land-sea separation /
- Sea surface zoning /
- Adaptive detection
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图 5 基于回波相位线性度的陆海分离结果
Figure 5. Land-sea separation result based on the correlation of phases
表 1 分块后拟合结果
Table 1. Fitting result of uniformly partitioned data
所假设的分布 Rayleigh Lognormal Weibull K 拟合后服从分布的分块数 3 1 6 10 
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表 2 杂波分区后的拟合结果
Table 2. Fitting results of partitioned data according to the rubbing angle
分块 最优分布 分块 最优分布 1 Weibull 2 K分布 3 Weibull 4 Weibull 5 Weibull 6 Weibull 7 Weibull 8 K分布 9 K分布 10 K分布
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