Power minimisation approach is an effective interference suppression algorithm for satellite navigation systems. It forms automatically deep nulls in the direction-of-arrival (DOA) of interferences without prior information about the DOAs of satellite signals and interferences. However, it cannot provide flat gains for other directions. Thus, the desired satellite signals may be partly suppressed when they locate in the shallow nulls. By combining eigenvalue thresholding method and l1-norm constraint, a new interference suppression algorithm is proposed for satellite navigation systems that would provide approximately flat gains in all directions except that of interferences. However, the l1-norm constraint leads to a non-smooth optimisation problem which cannot be solved by the conventional gradient-based algorithm. After that, by utilising the proximal operator, an iterative algorithm is proposed. The simulations demonstrate the effectiveness of the proposed algorithm.

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.

It is well known that some global navigation satellite system (GNSS) signals operate on the aeronautical radionavigation services (ARNS) frequency band, which is also utilized by other aeronautical service signals, such as pulsed signals from the distance-measuring equipment (DME) system. Thus, the high-power DME signal, as interference, will significantly degrade the performance of the GNSS. Some algorithms, including time blanking, frequency notch filtering, and hybrid filtering, have been proposed to suppress DME interference. However, when the density of the DME pulse is high, they will bring a huge loss of the desired satellite signal. This paper proposes a hybrid approach for DME interference suppression by combining a novel parametric algorithm and the wavelet-packet-based algorithm. An overlap detection algorithm is also proposed to automatically switch between the two algorithms. When there is no overlapped pulse, the parametric method will be utilized to suppress the interference and retain more of the desired satellite signal. For overlapped DME pulses, the interferences are suppressed by the wavelet-packet transformation algorithm. The problem of parameter selection for wavelet-packet transformation is also discussed systematically. The trade-off between performance and complexity is obtained by adaptively selecting between the traditional algorithm and the proposed hybrid approach based on a pulse density detection algorithm. Numerical results are presented to demonstrate the effectiveness of the proposed approach.

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.

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.