Wind power is an attractive clean energy and wind farms increase with very high speed in recent years. However, as a particular obstacle, wind farms may degrade the performance of radio devices in civil aviation obviously. Therefore, wind farms may threaten the flight safety and correct impact assessment of wind farms on radio devices is important to guarantee the safety of civil aviation. In this chapter the potential impact of wind farms on radio devices in civil aviation and a review of the impact assessment procedure and methods of our research group is presented. The radio devices discussed in the chapter include surveillance devices such as primary surveillance radar (PSR) and second surveillance radar (SSR) and radio navigation devices such as very high frequency omnidirectional range (VOR) and instrument landing system (ILS). A wind farm usually comprises several wind turbines with very large size. The proper estimation of the scattering coefficient or radar cross section (RCS) of the wind turbine is of great importance to assess the impact of wind farms correctly. However, the intensity of electromagnetic scattering and the RCS of a wind turbine vary with several factors. Consequently a review of RCS estimation methods for a wind turbine of our research group is also presented in this chapter.

An important issue in low-altitude wind-shear detection is to estimate the wind speed of wind field. In this paper, a novel method for wind speed estimation with airborne phased array radar is proposed by combining space time adaptive processing and compressive sensing. The proposed method is able to achieve accurate wind speed estimate in the condition of limited number of sampling pulses, as demonstrated by numerical examples.

High range resolution (HRR) moving target indicator radar is becoming increasingly important for many military and civilian applications involving the detection and classification of moving targets within a clutter background. For ground-based HRR radar, when targets are moving slowly or near-broadside and the coherent processing interval or dwell time is not too long, the effects of range migration and range feature distortion can be ignored. Based on this assumption, relaxation-based algorithms that are robust and computationally simple are proposed for HRR feature extraction of moving targets consisting of scatterers closely spaced in range in the resence of stationary clutter. Numerical examples show that the proposed algorithms exhibit super-resolution and excellent estimation performance.