Passive SAR satellite (PASSAT) system: airborne demonstrator and first results

Abstract

This paper presents the development and execution of an airborne experimental campaign as part of the continuing investigation of a passive Synthetic Aperture Radar using digital television broadcasting stations as illuminators of opportunity, and micro-/nano-satellite receivers in Low Earth Orbit. For the flight experiments, a hardware demonstrator was developed that utilised two receiving antennas, allowing both cross-correlation and auto-correlation range compression schemes, and was mounted to an airborne platform to image stationary rural areas up to 50 km from the transmitter. This paper presents the first image results of these experiments as well as initial analysis of image formation aspects including, range compression scheme and effects on the image quality of the signal to noise on the reference channel.

Improved Passive SAR Imaging With DVB-T Transmissions

“…In a single-channel receiver, containing both the direct and reflected signals, RC can be obtained by auto-correlating the recorded radar data in the channel [6], [15]. As DVB-T transmits continuously, an equivalent Pulse Repetition Frequency (PRF) can be chosen as that of a DVB-T symbol, which for our case is 1082.3Hz.…”

“…In using non-cooperative illuminators of opportunity, such systems have several characteristic advantages compared to active radar, including cost effectiveness, silent operation, no need for frequency allocation and licensing, and a reduced footprint in terms of electromagnetic pollution [1][2][3]. Many different types of available signals, including FM radio [4], Digital Audio Broadcast (DAB) [5], DVB-T [6], Global Navigation Satellite Systems (GNSS) [7], have been investigated for various applications, which include but are not limited to image formation, target location, moving target and coherent change detection.…”

An Image Formation Algorithm for Bistatic SAR Using GNSS Signal With Improved Range Resolution

“…Hence, in this paper, we utilize the range compressed signal and its second order differentiation result to achieve the range performance improvement, shown as (15), where the main lobe of target can be sharpened, and the side lobes still stay at a lower level, rather than applying the Diff2 operator on the squared range compressed signal. The first part is the range compressed signal, which can be calcualted by (8). The second part is a differentiation operation applied on the range compressed signal, which is a convolution result, shown in (7) previously.…”