Monitoring the wakes of ships at sea is one of the important applications of ocean remote sensing. Extraction of ship wake can be done using either synthetic aperture radar (SAR) images or optical images containing ship wake. However, wake detection based on SAR images or optical images can only obtain two-dimensional features of wake, although they are indeed three-dimensional (3D). In this paper, we demonstrate for the first time that 3D Kelvin wake can be retrieved by using the observation data of Tiangong-2 interference imaging radar altimeter (TG2-InIRA). The TG2-InIRAadoptsnear-nadir incidence (1° 8°) and a short interferometric baseline, the acquired images exhibit quite different features from any other SAR images (20° 60°), and the obtained high-quality interferometric phases can be applied to reconstruct the 3D ship wake. The reconstruction method is described in detail along with intermediate and final results presented.
Noise radar has been applied in many fields since it was proposed more than 50 years ago. However, it has not been applied to interferometric SAR imaging yet as far as we know. This paper introduces our recent work on interferometric noise radar. An interferometric SAR system was developed which can transmit both chirp signal and chaotic noise signal (CNS) at multiple carrier frequencies. An airborne experiment with this system by transmitting both signals was carried out, and the data were processed to show the capability of interferometric SAR imaging with CNS. The results shows that although the interferometric phase quality of CNS is degraded due to the signal to noise ratio (SNR) is lower compared with that of chirp signal, we still can get satisfied DEM after multi-looking processing. Another work of this paper is to apply compressed sensing (CS) theory to the interferometric SAR imaging with CNS. The CS theory states that if a signal is sparse, then it can be accurately reconstructed with much less sampled data than that regularly required according to Nyquist Sampling Theory. To form a structured random matrix, if the transmitted signal is of fixed waveform, then random subsampling is needed. However, if the transmitted signal is of random waveform, then only uniform subsampling is needed. This is another advantage of noise signal. Both the interferometric phase images and the DEMs by regular method and by CS method are processed with results compared. It is shown that the degradation of interferometric phases due to subsampling is larger than that of amplitude image.
We propose to apply the compressive sensing technique to the design of satellite radar altimeter for increasing the sampling time window (STW) while keeping the same data rate so as to enhance the tracking robustness of an altimeter. A satellite radar altimeter can measure the range between the satellite platform where it is aboard and the averaged sea surface with centimeter level accuracy. The rising slope of the received waveform by altimeter contains important information about the sea surface, e.g. the larger the slope of the waveform, means the smoother the sea surface. Besides, the half-power point of the slope refers to the range information. For satellite altimeter, due to the rising slope just occupies fewer range bins compared with the whole range bins illuminated by the long pulse signal, i.e. the signal is sparse in this sense, thus compressive sensing technique is applicable. Altimeter echoes are simulated and the waveforms are constructed by using the traditional method as well as by compressive sensing (CS) method, they are very well agreed with each other. The advantage of using CS is that we can increase the sampling time window without increasing the data, thus the tracking capability can be enhanced without sacrificing the resolution.
KEYWORDS: Velocity measurements, Radar imaging, Radar, Signal to noise ratio, Motion measurement, Detection and tracking algorithms, Monte Carlo methods, Signal processing, Multiple scattering, Imaging systems
Stepped-chirp signal (SCS) is widely used by wideband high-resolution radars; however, its bandwidth synthesizing suffers from motion-induced phase errors resulting from the radial velocity of the target, especially for high-speed targets. So motion compensation is very crucial in signal processing for this kind of radar using SCS. Based on the cross-correlation inner frame method for velocity measurement of a single-scattering-center target, the multiple cross-correlation method (MCCM) is proposed for measuring the velocity of a complex target with multiple scattering centers (MSC), which is called SCS-MCCM. By this algorithm, not only the radial velocity, but also the real velocity of a target can be measured under the assumption that it is straight moving. After obtaining the velocity, motion compensation is conducted so as to realize high-resolution imaging. Theoretical analysis and simulations show that the proposed method is feasible and effective for an MSC target.
The experimental work on testing the wide-band transmitters and receivers developed for Ka-band and Ku-band radar
systems, as well as the signal processing algorithms were introduced. A city light-railway train was selected as the
imaged target. The wide-band transmitters and receivers were designed based on the stepped-frequency chirp signal
(SFCS) with 2GHz bandwidth synthesized. The Super-SVA technique was used to deal with the case of transmitting
SFCS with band gaps between subchirps for purpose of achieving the same bandwidth using as less as possible subpulses.
Both Ka-band and Ku-band high-resolution radar images were obtained, which show that Ka-band images are much
clear than that of Ku-band as we expect. There are two reasons to explaining this, one reason is due to the
electromagnetic scattering of train itself are different for Ka-band and Ku-band frequencies, and the other reason is due
to the interactions, i.e. multi-reflection or multi-scattering between the train and the side metal fences or the lamp post