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A Modified Method of Removing Ground Clutter from Wind Profiler Radar Based on Adaptive Wavelet Threshold
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摘要: 风廓线雷达探测过程中电磁波传输会受到各类杂波的干扰，其中，地物是主要来源。从功率谱数据上看，地物杂波主要集中在零频附近，且幅度较高，不加以抑制会影响气象回波的识别。针对目前常用的小波阈值滤波法在处理近零频回波被杂波覆盖时效果不佳的情况，该文结合风廓线雷达特点，提出一种根据小波分解高频系数自适应确定阈值的方法，并通过模拟数据与风廓线雷达实测数据进行检验，结果表明：即便信号靠近零频，且被杂波覆盖，该方法也能快速准确识别信号回波。同时，该算法原理简单、计算量小、易于实现，在实际应用中能够增加谱峰识别准确率，可为改善风廓线雷达产品质量提供参考。Abstract: Wind profiler radar (WPR) can be used to retrieve real-time atmospheric wind field data of high resolution. Backscattered echo caused by irregularities of atmospheric refractive index is received by radar antenna and wind velocities is calculated with Doppler frequency shifting speed formula. It is widely used in fields of very short-term weather forecasting, airport operations and public protection, air pollution monitoring, wind field analyses and forecasts of toxic plume trajectories resulting from chemical or nuclear incidents. As a result of being widely used in different situations, WPR is always sited near the city with a large population and complicated geographical environment. Transmission of electromagnetic wave during WPR detecting period is often interfered by various clutters that contaminate WPR data introduce bias in moments and wind velocity estimation. Of all clutters, ground clutter is the primary source because it happens more often than the others. Ground clutter is radar return from more or less stationary targets such as trees, buildings near the cited place. How to eliminate the influence of ground clutter is a most concerned aspect. Ground clutter mainly concentrates around the zero-frequency and it has high amplitude on the power spectrum. The most frequently used methods, such as traditional wavelet threshold processing and zero-frequency elimination of 3 points, both have the ability to separate the meteorology echo from the ground clutter when the turbulent peak is away from the zero-frequency and not covered with ground clutter peak. However, when the near zero-frequency echo is taken into consideration, both of the traditional methods meet their limitation. Based on the wavelet high frequency coefficients, a method of determining threshold adaptively is proposed and the validation of the method is done by using of simulated data and WPR measured data. The corresponding power spectrum before and after self-adapting wavelet threshold processing are compared. Results show that this method performs well even when the signal is close to the zero-frequency and covered completely. Meanwhile, the method has some important features, such as simple theory, small amount of calculation and easy to implement. Cases analysis shows that self-adapting threshold processing can increase the accuracy of peak identification, also provide approach and basis for improving the WPR products.
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图 3 1.5 Hz信号小波系数图
(a) 传统阈值方法，(b) 自适应阈值方法
Fig. 3 Wavelet coefficients of 1.5 Hz signal
(a) traditional threshold processing, (b) self-adapting threshold processing
图 4 1.5 Hz信号功率谱图
(a) 原始功率谱，(b) 零频中心3点剔除法处理后的功率谱，(c) 传统阈值方法处理后的功率谱，(d) 自适应阈值方法处理后的功率谱
Fig. 4 Power spectrum of 1.5 Hz signal
(a) original power spectrum, (b) power spectrum after zero-frequency elimination of 3 points, (c) power spectrum after traditional threshold processing, (d) power spectrum after self-adapting threshold processing
图 5 50 Hz模拟数据功率谱图
(a) 原始功率谱, (b) 自适应阈值方法处理后的功率谱
Fig. 5 Power spectrum of 50 Hz signal
(a) original power spectrum, (b) power spectrum after self-adapting threshold processing
图 6 2014年5月17日12：50成都信息工程大学风廓线雷达Ⅰ路和Q路信号图及小波系数
(a) 原始Ⅰ路和Q路信号，(b) 重构Ⅰ路和Q路信号，(c) 原始小波系数，(d) 自适应阈值方法处理后的小波系数
Fig. 6 The time series of Ⅰ component and Q component and wavelet coefficients of WPR at CUIT at 1250 BT 17 May 2014
a) original time series of Ⅰ component and Q component, (b) reconstructed time series of Ⅰ component and Q component, (c) original wavelet coefficients, (d) wavelet coefficients after self-adapting threshold processing
图 7 2014年5月17日12：50成都信息工程大学风廓线雷达分功率谱
(a) 原始功率谱，(b) 自适应阈值处理后的功率谱
Fig. 7 Power spectrum of WPR at CUIT at 1250 BT 17 May 2014
(a) original power spectrum, (b) power spectrum after self-adapting threshold processing
图 8 2014年4月25日09：30成都信息工程大学风廓线雷达功率谱
(a) 原始功率谱，(b) 自适应阈值方法处理后的功率谱，(c) 零频中心3点剔除方法处理后的功率谱图
Fig. 8 Power spectrumof WPR at CUIT at 0930 BT 25 Apr 2014
(a) original power spectrum, (b) power spectrum after self-adapting threshold processing, (c) power spectrum after zero-frequency elimination of 3 points
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