In order to suppress the multipath interference in global navigation satellite system, two algorithms based on NLS (nonlinear least square) parameter estimation are proposed. Instead of the classic delay lock loop, the first proposed algorithm estimates the parameters of the line of sight signal and the multipath interference in the correlation domain. The NLS cost function is solved by WRELAX (weighted Fourier transform and RELAXation), which decouples the multidimensional optimization problem into a sequence of one-dimensional optimization problems in a conceptually and computationally simple way. In order to further reduce the complexity, the second NLS algorithm utilizing the characteristic of the C/A code is proposed, which estimate the parameters in the data domain. Finally, the two proposed algorithms are compared with the existing multipath interference methods and show excellent performance and less computational burden.

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.

This paper provides a solution for systematic bias estimation of radar without priori information of data association based on the probability hypothesis density (PHD) filter aided by automatic dependent surveillance broadcasting (ADS-B). Novel dynamics model and measurement model of systematic bias are developed by using ADS-B surveillance data as the high-accuracy reference source. The Gaussian mixture probability hypothesis density (GM-PHD) filter is applied for recursive estimation of systematic bias by introducing the novel dynamics model and measurement model of systematic bias into the filter. Numerical results are provided to verify the effectiveness and improved performance of the proposed method for systematic bias estimation.