Ocean Surface Observations Using the TanDEM-X Satellite Formation

The TanDEM-X SAR satellite formation permits improved ocean surface observations by means of bistatic along-track interferometry (ATI) when compared to single-satellite systems. The flexible imaging geometry of its two cooperating SAR sensors forms an interferometer that can achieve very high sensitivity to motions of objects on ground. This way, radar imaging of surface currents with unprecedented accuracy, high spatial resolution and wide coverage at the same time becomes possible. We demonstrate the capabilities of the sensor in the contexts of tidal current mapping, measurement of thermohaline and wind-driven ocean currents as well as detection of areas with surface films. We have developed a dedicated postprocessing system for TanDEM-X image products that allows extracting surface current information from the data. By this paper, we address bistatic data acquisition and processing aspects for sea surface imaging with TanDEM-X like interferometric baseline geometry, temporal decorrelation, and phase calibration. We present a variety of examples of data evaluation that clearly demonstrate the application potential of the methodology

Wide-Angle Multistatic Synthetic Aperture Radar: Focused Image Formation and Aliasing Artifact Mitigation

Traditional monostatic Synthetic Aperture Radar (SAR) platforms force the user to choose between two image types: larger, low resolution images or smaller, high resolution images. Switching to a Wide-Angle Multistatic Synthetic Aperture Radar (WAM-SAR) approach allows formation of large high-resolution images. Unfortunately, WAM-SAR suffers from two significant implementation problems. First, wavefront curvature effects, non-linear flight paths, and warped ground planes lead to image defocusing with traditional SAR processing methods. A new 3-D monostatic/bistatic image formation routine solves the defocusing problem, correcting for all relevant wide-angle effects. Inverse SAR (ISAR) imagery from a Radar Cross Section (RCS) chamber validates this approach. The second implementation problem stems from the large Doppler spread in the wide-angle scene, leading to severe aliasing problems. This research effort develops a new anti-aliasing technique using randomized Stepped-Frequency (SF) waveforms to form Doppler filter nulls coinciding with aliasing artifact locations. Both simulation and laboratory results demonstrate effective performance, eliminating more than 99% of the aliased energy

Orthogonal Frequency Division Multiplexed Waveform Effects on Passive Bistatic Radar

Communication waveforms act as signals of opportunity for passive radars. However, these signals of opportunity suffer from range-Doppler processing losses due to their high range sidelobes and pulse-diverse waveform aspects. Signals such as the long term evolution (LTE) encode information within the phase and amplitude of the waveform. This research explores aspects of the LTE, such as the encoding scheme and bandwidth modes on passive bistatic Doppler radar. Signal space-time adaptive processing (STAP) performance is evaluated and parameters are compared with the signal to interference-plus-noise ratio (SINR) metric

Hardware Development and Error Characterisation for the AFIT RAIL SAR System

This research is focussed on updating the Air Force Institute of Technology (AFIT) Radar Instrumentation Lab (RAIL) Synthetic Aperture Radar (SAR) experimental system. Firstly, this research assesses current hardware limitations and updates the system configuration and methodology to enable collections from a receiver in motion. Secondly, orthogonal frequency-division multiplexing (OFDM) signals are used to form (SAR) images in multiple experimental and simulation configurations. This research analyses, characterises and attempts compensation of relevant SAR image error sources, such as Doppler shift or motion measurement errors (MMEs). Error characterisation is conducted using theoretical, simulated and experimental methods. Final experimental results are presented to verify performance of the updated SAR collection system and show improvements to the final product through an updated methodology and various signal processing techniques

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Measurements of Sea Surface Currents in the Baltic Sea Region Using Spaceborne Along-Track InSAR

The main challenging problems in ocean current retrieval from along-track interferometric (ATI)-synthetic aperture radar (SAR) are phase calibration and wave bias removal. In this paper, a method based on differential InSAR (DInSAR) technique for correcting the phase offset and its variation is proposed. The wave bias removal is assessed using two different Doppler models and two different wind sources. In addition to the wind provided by an atmospheric model, the wind speed used for wave correction in this work is extracted from the calibrated SAR backscatter. This demonstrates that current retrieval from ATI-SAR can be completed independently of atmospheric models. The retrieved currents, from four TanDEM-X (TDX) acquisitions over the 6resund channel in the Baltic Sea, are compared to a regional ocean circulation model. It is shown that by applying the proposed phase correction and wave bias removal, a good agreement in spatial variation and current direction is achieved. The residual bias, between the ocean model and the current retrievals, varies between 0.013 and 0.3 m/s depending on the Doppler model and wind source used for wave correction. This paper shows that using SAR as a source of wind speed reduces the bias and root-mean-squared-error (RMSE) of the retrieved currents by 20% and 15%, respectively. Finally, the sensitivity of the sea current retrieval to Doppler model and wind errors are discussed

National Forest Biomass Mapping Using the Two-Level Model

This article uses the two-level model (TLM) to predict above-ground biomass (AGB) from TanDEM-X synthetic aperture radar (SAR) data for Sweden. The SAR data were acquired between October 2015 and January 2016 and consisted of 420 scenes. The AGB was estimated from forest height and canopy density estimates obtained from TLM inversion with a power law model. The model parameters were estimated separately for each satellite scene. The prediction accuracy at stand-level was evaluated using field inventoried references from entire Sweden 2017, provided by a forestry company. AGB estimation performance varied throughout the country, with smaller errors in the north and larger in the south, but when the errors were expressed in relative terms, this pattern vanished. The error in terms of root mean square error (RMSE) was 45.6 and 27.2 t/ha at the plot- and stand-level, respectively, and the corresponding biases were -8.80 and 11.2 t/ha. When the random errors related to using sampled field references were removed, the RMSE decreased about 24% to 20.7 t/ha at the stand-level. Overall, the RMSE was of similar order to that obtained in a previous study (27-30 t/ha), where one linear regression model was used for all scenes in Sweden. It is concluded that, using the power law model with parameters estimated for each scene, the scene-wise variations decreased