微波雷达双边滤波云检测新方法的研究

doi:10.11867/j.issn.1001-8166.2020.100

云是影响天气、气候变化的重要因子，也是目前造成气候模拟的最大不确定因素之一，云的长期准确观测，对验证和约束模式结果，减少云对气候变化模拟预测造成的不确定性有重要意义。毫米波云雷达通过接受云滴粒子回波信号，可以获取云的三维结构特征，是云探测的有力工具。以兰州大学半干旱气候与环境观测站Ka波段云雷达和CloudSat星载W波段云雷达为例，详细介绍了一种基于双边滤波的噪声压缩思想，对地—空基云雷达信号回波和背景噪声区分识别的云检测改进算法。通过将图像平滑处理的双边滤波思想引入毫米波雷达云检测算法当中，在压缩雷达背景噪声的同时，保持了弱信号边缘的清楚完整性，从而识别出更多被以往算法忽略的真实信号，并与地面和星载激光雷达同步观测对比，证实了该算法能显著降低传统算法的漏检率，提高毫米波雷达云检测准确度。同时，还以飞行器航迹检测为例，探讨了该算法在微波雷达对目标物探测应用中的改进，说明该算法对增强雷达小散射截面目标物识别方面的普适性。由此认为该方法对微波主动雷达目标物检测，特别是弱信号物体识别提供了一种有效改进。

关键词: 云检测, 双边滤波, 毫米波云雷达, 航迹检测, 目标物识别

Clouds play an import role in weather and climate change， and are one of the most principal sources of uncertainty in climate projection. Long-term accurate observations of clouds are vital to validating and constraining model simulations， and reducing the uncertainty caused by clouds in climate models. The millimeter-wavelength cloud radar is a powerful tool for cloud observation by directly detecting signal backscattered from cloud droplets， and thus can provide cloud three-dimensional features. In this paper， we presented in detail an improved cloud detection algorithm to distinguish real cloud echoes from radar background noise. The bilateral filter idea from image process was adopted into millimeter-wave cloud detection algorithm， which compressed the radar noise while preserving cloud edge， therefore being able to identify more real weak signal ignored by traditional cloud mask methods. We also used the Ka-band Zenith Radar （KAZR） in the Semi-Arid Climate and Environment Observatory of Lanzhou University （SACOL）， and the W-band cloud profiling radar aboard CloudSat， along with the synchronized lidar measurements to demonstrate that the improved algorithm could significantly reduce false negative rate， and increase the cloud detection accuracy. This paper also discussed the advantages of this algorithm for microwave radar in other remote sensing applications， taking track detection as an example. It shows that the algorithm could be generally used for small radar cross section target recognition. We believe this method will enhance the active microwave radar target detection ability especially for the objects with small radar cross section.

Key words: Cloud detection, Bilateral filter, Millimeter-wave cloud radar, Track detection, Target recognition

中图分类号:

P426.5

图1 双边滤波示意图
（a）二维高斯函数；（b）标记函数，根据实际回波分布对高斯滤波赋值不同权重；（c）由高斯函数和标记函数构建的双边滤波器； i与 j是距中心点的雷达距离库位置

Fig.1 Schematic diagram of bilateral filter
(a) Two-dimensional Gaussian function; (b)

δ

function,assigning different weights to the Gaussian filter according to the actual echo distribution; (c) Bilateral filter,constructed by the Gaussian function and the

δ

function； i and j are the indexes in the filter window

Fig.1 Schematic diagram of bilateral filter
(a) Two-dimensional Gaussian function; (b)

δ

function,assigning different weights to the Gaussian filter according to the actual echo distribution; (c) Bilateral filter,constructed by the Gaussian function and the

δ

function； i and j are the indexes in the filter window

图2 双边滤波模拟测试图
（a）不同强度目标原始回波；（b）图a的回波分布；（c）对图a进行高斯滤波后的回波；（d）图c的回波分布；（e）对图a进行双边滤波后的回波；（f）图e的回波分布；右列图中，蓝色表示噪声分布，红色表示信号分布；

m

和

σ

分别为噪声的平均值和标准差

Fig.2 Test of bilateral filter
(a) Raw echoes of different intensity targets; (b) Echo distribution of Fig.(a); (c) Echoes after Gaussian filter to Fig.(a); (d) Echo distribution of Fig.(c); (e) Echoes after bilateral filter to Fig.(a); (f) Echo distribution of Fig.(e); In the right panels,blue indicates the noise distribution and red indicates the signal distribution;

m

and

σ

are the mean and standard deviation of the noise,respectively

m

和

σ

分别为噪声的平均值和标准差

m

and

σ

are the mean and standard deviation of the noise,respectively

图3 地基雷达云检测算法流程图
m和 σ分别为噪声的平均值和标准差，下标

0

和 n分别表示原始噪声和压缩后的噪声;

N t

和

N r

分别为理论和实际上，滤波器内回波大于

m 0 + σ 0

的距离库个数;右侧虚线框内为左侧噪声压缩的详细步骤

Fig.3 Flowchart of ground-based radar cloud detection algorithm
m and σ are the mean and standard deviation of the noise,respectively; Subscript 0 and n mean original and compressed noise;

N t

and

N r

are the theoretical and real number of echoes greater than

m 0 + σ 0

in the filter box; The dashed boxes in the right side indicates the detailed steps of noise compression in the left side

图3 地基雷达云检测算法流程图
m和 σ分别为噪声的平均值和标准差，下标

0

和 n分别表示原始噪声和压缩后的噪声;

N t

和

N r

分别为理论和实际上，滤波器内回波大于

m 0 + σ 0

的距离库个数;右侧虚线框内为左侧噪声压缩的详细步骤

Fig.3 Flowchart of ground-based radar cloud detection algorithm
m and σ are the mean and standard deviation of the noise,respectively; Subscript 0 and n mean original and compressed noise;

N t

and

N r

are the theoretical and real number of echoes greater than

m 0 + σ 0

in the filter box; The dashed boxes in the right side indicates the detailed steps of noise compression in the left side

图4 2014年11月13日14：00-23:00地基雷达云检测算法结果
（c）中红色表示双边滤波算法相比传统算法多识别的云信号，黄色表示两种方法同时检测到的云，蓝色表示双边滤波比传统算法遗漏的云信号，黑点表示激光雷达特征检验算法识别的云边界

Fig.4 The result of hydrometeor detection method for ground-based cloud radar from 14:00 to 23:00 on November 13,2014
In Fig. （c）, red indicates the cloud mask detected by the bilateral filter algorithm but not by the traditional algorithm, yellow indicates the cloud mask detected by both methods simultaneously, blue indicates the cloud mask missed by the bilateral filter algorithm compared to the traditional algorithm, and black dots indicate the cloud boundaries identified by the lidar feature detection algorithm

图5 2013年8月18日15:00-22:00地基雷达云检测算法结果
（c）中红色表示双边滤波算法相比传统算法多识别的云信号，黄色表示两种方法同时检测到的云，蓝色表示双边滤波比传统算法遗漏的云信号，黑点表示激光雷达特征检验算法识别的云边界

Fig.5 The result of hydrometeor detection method for ground-based cloud radar from 15:00 to 22:00 on 18 August 2013
In Fig. （c）, red indicates the cloud mask detected by the bilateral filter algorithm but not by the traditional algorithm, yellow indicates the cloud mask detected by both methods simultaneously, blue indicates the cloud mask missed by the bilateral filter algorithm compared to the traditional algorithm,and black dots indicate the cloud boundaries identified by the lidar feature detection algorithm

图6 星载雷达云检测算法流程图
虚线框为双边滤波的噪声压缩方法

Fig.6 Flowchart of the spaceborne radar cloud detection algorithm
The dashed box shows the bilateral filter noise compression method

图7 2006年6月22日50°~57°S星载雷达云检测算法结果
（c）红色表示双边滤波算法相比传统算法多识别的云信号，黄色表示被两种方法同时检测到的云，蓝色表示双边滤波算法遗漏的，黑点表示CALIPSO识别的云边界

Fig.7 The result of hydrometeor detection method for spaceborne radar on June 22,2006,from 50°S to 57°S
（c）Red indicates the cloud mask detected by the bilateral filtering algorithm but not by the traditional algorithm, yellow indicates the cloud mask detected by both methods simultaneously, blue indicates the cloud mask missed by the bilateral filtering compared to the traditional algorithm, and black dots indicate the cloud boundaries identified by CALIPSO

图8 2006年6月22日45°~36°S星载雷达云检测算法结果
（c）红色表示双边滤波算法相比传统算法多识别的云信号，黄色表示被两种方法同时检测到的云，蓝色表示双边滤波算法遗漏的，黑点表示CALIPSO识别的云边界

Fig.8 The result of hydrometeor detection method for spaceborne radar on 22 June 2006,from 45°S to 36°S
（c）Red indicates the cloud mask detected by the bilateral filtering algorithm but not by the traditional algorithm,yellow indicates the cloud mask detected by both methods simultaneously,blue indicates the cloud mask missed by the bilateral filtering compared to the traditional algorithm,and black dots indicate the cloud boundaries identified by CALIPSO

图9 航迹检测流程图
虚线框为双边滤波的噪声压缩方法

Fig.9 Flowchart of the track detection
The dashed box shows the bilateral filter noise compression method

图10 航迹检测模拟测试图
(a)原始航迹信号，其中颜色偏红表示回波强度越强，偏蓝表示回波强度越弱；(b)对图a进行航迹检测的结果；（c）对图a进行双边滤波的噪声压缩后的信号；（d）对图c进行航迹检测结果，其中红线表示比图b中多检测到的航迹

Fig.10 Test of track detection
(a) Raw track signal， the red indicates stronger echoes and blue means weaker echoes; (b) Results of track detection for Fig.（a）; (c) Signal after bilateral filter to Fig.（a）; (d) Results of track detection for Fig.（c）,where the red line indicates more tracks detected than in Fig.（b）

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A Novel Bilateral Filter Hydrometeor Detection Method for Microwave Radar