We focus on forward-looking radar network systems for automotive usages. By using multiple radars, the radar network systems will achieve reliable detection and wide observation area. The forward-looking systems by cameras are famous. In order to realize more reliable safety, the cameras had better be used with other sensing devices such as the radar network. In the radar network, processing of the data, which is derived from the multiple receivers, is important because the processing decides the estimation performance. In this paper, we will introduce our estimation algorithm which focuses on target existence probability and virtual receivers. The performance will be evaluated by simulated targets which are both single point model and 3D target model.

With appropriate geometry configurations, bistatic Synthetic Aperture Radar (SAR) can break through the limitations of monostatic SAR for forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. This paper develops a frequency-domain imaging algorithm for translational invariant BFSAR. The algorithm uses the method of Lengendre polynomials expansion to compute the two dimensional point target reference spectrum, and this spectrum is used to perform the range cell migration correction (RCMC), secondary range compression and azimuth compression. In particular, the Doppler-centroid and bistatic-range dependent interpolation for residual RCMC is presented in detail. In addition, a method that combines the ambiguity and resolution theories to determine the forward-looking imaging swath is also presented in this paper.

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.