Multiangle BSAR Imaging Based on BeiDou-2 Navigation Satellite System: Experiments and Preliminary Results

Abstract

This paper analyzes the multiangle imaging results for bistatic synthetic aperture radar (BSAR) based on global navigation satellite systems (GNSS-BSAR). Due to the shortcoming of GNSS-BSAR images, a multiangle observation and data processing strategy based on BeiDou-2 navigation satellites was put forward to improve the quality of images and the value of system application. Twenty-six BSAR experiments were conducted and analyzed in different configurations. Furthermore, a region-based fusion algorithm using region-of-interest (ROI) segmentation was proposed to generate a high-quality fusion image. Based on the fusion image, typical targets such as water area, vegetation area, and artificial targets were compared and interpreted among single/ multiple-angle images. The results reveal that the multiangle imaging method was a good technique to enhance image information, which might extend the applications of GNSS-BSAR.Index Terms-Bistatic synthetic aperture radar (BSAR), global navigation satellite system (GNSS), image fusion, image interpretation, multiangle.

Improved GNSS-Based Bistatic SAR Using Multi-Satellites Fusion: Analysis and Experimental Demonstration

“…A large number of satellites with different views can be used to enhance the information space of the images, the feature value can be extracted from each image, and then the feature-level combination can be used to significantly enhance the target information [30], conducive to feature extraction, feature recognition, and classification of the target area. In [31], using the B3 signal of the BeiDou navigation system to study the segmentation method of the sensing area, the method is based on the changes of the radar cross section (RCS) of different targets under different bistatic geometric configurations, and is verified by experiments. With Galileo E5 echo imaging, the entire E5 channel can be stitched together using a spectral equalization technique to increase the 3 dB range resolution to 1.758 m, but this technique is limited to alternate binary offset carrier (AltBOC) signal and introduces essential reduction in signal-noise ratio (SNR) [14].…”

An Improved RD Algorithm for Maneuvering Bistatic Forward-Looking SAR Imaging with a Fixed Transmitter

“…Supposing that the transmitted signal is the linear frequency modulation (LFM) signal [9], and the position of the point target $P$ is $\left(x_p,y_p,0\right)$, then we have the instantaneous slant range of the high-speed maneuvering MBFL-SAR $R\left(t_{\mathrm{a}}\right)$, which can be described by: $\begin{array}{l}R\left(t_{\mathrm{a}}\right)=R_{\mathrm{t}}+R_{\mathrm{r}}\left(t_{\mathrm{a}}\right)=\sqrt{{\left(X_{\mathrm{t}}-x_{\mathrm{p}}\right)}^2+{\left(Y_{\mathrm{t}}-y_{\mathrm{p}}\right)}^2+{\left({\mathrm{H}}_{\mathrm{t}}\right)}^2}\\ +\sqrt{x_{\mathrm{p}}^2+{\left(V_{\mathrm{ry}0}{t}_{\mathrm{a}}+\frac{1}{2}{a}_{\mathrm{ry}}{t}_{\mathrm{a}}^2-y_{\mathrm{p}}\right)}^2+{\left(H_{\mathrm{r}}+V_{\mathrm{rz}0}{t}_{\mathrm{a}}+\frac{1}{2}{a}_{\mathrm{rz}}{t}_{\mathrm{a}}^2\right)}^2}\end{array}$ where $R_{\mathrm{t}}$ and $R_{\mathrm{r}}\left(t_{\mathrm{a}}\right)$ are the transmitter range and receiver range, respectively.…”

“…Chen introduced the general situation and the method for BFL-SAR [7]. In addition, several experiments have been carried out and the results demonstrate the feasibility of BFL-SAR [8,9]. Differing from monostatic SAR, the slant range in bistatic SAR has two hyperbolic functions, which are defined as double square root (DSR) terms [10], thus, the principle of the stationary phase (POSP) could not be directly used in BFL-SAR.…”

Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter

“…Compared with other opportunistic illuminators, the global navigation satellite system (GNSS) is a good choice for passive radar given its permanent global coverage, plentiful satellite resources and ease for synchronization [1][2][3][4]. Therefore, the GNSS-based passive radar (GBPR) is an innovative, all-weather, and all-time microwave tool for remote sensing and target detection applications, and it has developed very quickly in recent years [5][6][7][8][9][10][11][12][13][14][15].…”