Two-step motion compensation method for step-frequency UAV SAR imagery

Coherent range-dependent map-drift algorithm for improving SAR motion compensation

Forward velocity extraction is a very important process for obtaining a high-quality unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) image. Because of the constraints of low flying altitude and small platform size, the flight path of the UAV is easily disturbed by the atmospheric turbulence. The complex motion error of the UAV's flight path makes the forward velocity difficult to be extracted from raw SAR data. To address this problem, an adaptive notch filtering (ANF)-based approach for forward velocity extraction is proposed. Based on the kinetic characteristics of the UAV, the variation of Doppler centroid frequency is analyzed and exploited to remove most components of the cross-track acceleration in the low-frequency range. Then, by regarding the forward velocity component as a narrow-band component, ANF processing is employed to extract it from the estimated Doppler rate. Comparing with the methods reported in the literature, the ANF method can achieve higher accuracy and efficiency due to its excellent notching performance and strong suppression for narrow-band signals. Promising results from raw data experiments are presented to demonstrate the validity and superiority of the proposed method.

Unmanned aerial vehicle (UAV) which carries a synthetic aperture radar (SAR) to obtain high-resolution images are used by more and more scenes. However, due to the light weight and small size UAV platform, the UAV SAR system is very sensitive to turbulence, and the resulting motion impact will give the UAV an unknown force to change the acceleration in the azimuth direction, resulting in the UAV non-uniform motion in the azimuth direction. This makes the azimuth sampling position of UAV's non-uniform, resulting in azimuth ambiguity in the imaging results. In this paper, we use an equivalent alternative method, in which the azimuthal nonuniform motion of UAV caused by turbulence is equivalent to azimuthal nonuniform sampling. In this paper, the non-uniform fast Fourier transform (NUFFT) and omega-k algorithm is used to simulate the model.

High-resolution airborne synthetic aperture radar (SAR) systems are very sensible to deviations of the aircraft from the reference track. In high-resolution imagery, the improvement of range resolution increases the difficulty of implementing range cell migration correction (RCMC), while a wider synthetic aperture increases the cumulative time of motion errors which will affect the image quality. To enable accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, due to the limit of accuracy of such systems, motion errors are hard to be compensated correctly, causing mainly the resolution decrease in final image. Moreover, in large swath mode, the range-dependent phase errors are difficult to be compensated by using the conventional autofocus algorithm only. In this paper, we propose a robust motion error estimation method based on raw SAR data. To apply this estimation method, we first estimate the double phase gradients in subaperture. Second, a filtering method based on curve fitting was proposed to reduce the phase estimation errors caused by low signal-to-clutter ratio (SCR). Finally, we propose a weighted total least square method to calculate the motion errors using the filtered phase gradients. Because the proposed algorithm is nonparametric, it can estimate high-order motion errors. This is very important for the airborne SAR, particularly the light aircraft SAR platform, due to their more complicated movement in air turbulence. The versatility that the proposed method can be used in any imaging algorithms is another advantage. The processing of large number of raw SAR data shows that the algorithm is as robust and practical as phase gradient autofocus and can generate better focused images.

Airborne synthetic aperture radar (SAR) system is an essential tool for modern remote sensing applications. The aircraft is easily affected by the atmospheric turbulence, leading to deviations from the ideal track. To enable high-resolution imagery, a navigation system is usually mounted on the aircraft. Due to the limitation of the navigation system's accuracy, motion errors estimated from the SAR raw data are needed. In this paper, a novel motion compensation algorithm, which is based on the instrumental variables (IV) method, is proposed. We call this IV-based algorithm the IVA algorithm. In this algorithm, double-derivative motion errors are estimated without modeling the random disturbances to be a zero-mean Gaussian distribution and to be created from mutually independent noise, which makes it more robust and accurate in focusing SAR images. Before the motion error estimation, a Savitzky–Golay filter is performed to reduce the phase estimation errors, in which the phase is obtained by the phase gradient autofocus algorithm. Finally, the estimated motion errors are used to compensate the received signal with the range-dependent model. The IVA algorithm is validated by using real airborne SAR data, and experimental results show that the proposed algorithm achieve an excellent performance in airborne SAR systems.

Oil spill detection by imaging radars: Challenges and pitfalls

Motion error is one of the most serious problems in airborne synthetic aperture radar (SAR) data processing. For a smoothly distributing backscatter scene or a seriously speed-varying velocity platform, the autofocusing performances of conventional algorithms, e.g., map-drift (MD) or phase gradient autofocus (PGA) are limited by their estimators. In this paper, combining the trajectories measured by global position system (GPS) and inertial navigation system (INS), we propose a novel error compensation method for varying accelerated airborne SAR based on the best linear unbiased estimation (BLUE). The proposed compensating method is particularly intended for varying acceleration SAR or homogeneous backscatter scenes, the processing procedures and computational cost of which are much simpler and lower than those of MD and PGA algorithms.

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.

This paper concerns the compensation of track deviations in high-resolution automobile synthetic aperture radar (SAR). We establish the geometry in which the automobile SAR raw data is acquired. Based on the derived model, the research investigates the impact of track deviations on the final focused image quality and we propose motion compensation (MOCO) strategy which can be applied to obtain a well-focused SAR image. This paper presents how to obtain the accurate positions of the antenna phase center (APC) using the velocity and attitude angles measured by inertial navigation system (INS)/global positioning system (GPS), and proposes a new method to compensate the forward velocity errors which are decomposed into two components and compensated individually, that is, high-order and low order phase compensation. Finally, the results from real data are presented to validate the whole analysis and the proposed procedure.

As miniaturization technology has progressed, Synthetic Aperture Radar (SAR) can now be mounted on Unmanned Aerial Vehicles (UAVs) to carry out observational tasks. Influenced by airflow, UAVs inevitably experience deviations or vibrations during flight. In the context of cost constraints, the precision of the measurement equipment onboard UAVs may be relatively low. Nonetheless, high-resolution imaging demands more accurate track information. It is therefore of great importance to estimate high-precision tracks in the presence of both motion and measurement errors. This paper presents a robust track estimation method for airborne SAR that makes use of both envelope and phase errors. Firstly, weak navigation information is employed for motion compensation, which reduces a significant portion of the motion error. Subsequently, the track is initially estimated using additional envelope errors introduced by the Extended Omega-K (EOK) algorithm. The track is then refined using a phase-based approach. Furthermore, this paper presents the calculation method of the compensated component for each target and provides an analysis of accuracy from both theoretical and simulation perspectives. The track estimation and imaging results in the simulations and real data experiments validate the effectiveness of the proposed method, with an estimation accuracy of real data experiments within 5 cm.

Bistatic synthetic aperture radar (SAR) processing requires precise knowledge about geometrical parameters of the flown bistatic constellation, whereas estimation of these parameters is even more important than in the monostatic case. As it is impossible to separate the individual semimonostatic parameters from the bistatic raw data, the searched parameters are derived from the tracks of the moving platforms. For this reason, global positioning system (GPS) and inertial navigation system (INS) position and velocity data of transmitter and receiver are combined in an optimal way by a Kalman filter approach. As a consequence of model-based interpolation, we can easily determine varying parameters like Doppler centroid frequency, azimuth velocity, and the bistatic parameters a2 and a0 referring to the illuminated scene at each time instant. Accurately determined position and velocity of the transmitter and receiver enable a first-order bistatic motion compensation (MC), which is also described in this paper. In verifying our approach, GPS/INS tracks and raw data of a bistatic airborne SAR experiment flown in 2003 were used, where the data were provided by Forschungsgesellschaft fu umlr Angewandte Naturwissenschaften e.V., Wachtberg, Germany. The focusing was done by a scaled inverse Fourier transformation processor for nearly parallel trajectories for transmitter and receiver. This paper focuses on analyzing the trajectories and antenna beams of bistatic missions to obtain accurate processing parameters and MC functions.

Synthetic Aperture Radar (SAR) systems deployed on Unmanned Aerial Vehicles (UAVs) are emerging as a highly promising solution for a wide range of future remote sensing and monitoring applications. The integration of SAR’s well-established capabilities for high-resolution imaging and change detection with the flexibility, maneuverability, and cost-effectiveness of modern UAV platforms unlocks significant potential for innovative sensing missions. However, the practical implementation of SAR on UAVs presents considerable challenges, primarily due to the inherent limitations in UAV flight stability and precise navigation, which degrade image quality. As a result, robust autofocusing techniques are essential to correct motion-induced phase errors and ensure coherent image reconstruction. While numerous autofocusing methods have been proposed in the literature, most are designed under assumptions that do not hold for low-flying UAV platforms. Specifically, conventional algorithms often overlook the strong dependence of the SAR imaging geometry on both azimuth and range positions of the targets, which becomes pronounced at low altitudes. In this study, we introduce a novel autofocusing approach termed Geometry-Aware Entropy Minimization (GAME). The proposed algorithm reconstructs three-dimensional positioning errors by explicitly modeling the spatially variant acquisition geometry along the UAV trajectory. This geometry-aware formulation enables accurate autofocus correction under the challenging conditions associated with low-altitude UAV SAR acquisitions. The effectiveness of the GAME algorithm is demonstrated using real X-band SAR data collected by an octacopter platform, confirming its applicability and performance in practical scenarios.

This paper presents the relationship between energy and signal integrity of Unmanned Aerial Vehicles (UAVs) with Synthetic Aperture Radar (SAR) capabilities for a more informed off-the-shelf selection design. The energy is expressed in the UAV and SAR power consumption, whereas signal integrity is expressed in Bit-Error Rate (BER). Low-end, mid-end, and highend—categorized in terms of their market cost—commercial UAVs and SAR modules were considered. The energy relations are based on the UAV flight path and power ratings. The signal processing and decomposition of the reconstructed objects for the model simulations are based on a transmit channel-receive chain of 16-quadrature-amplitude, low-density parity check, Rayleigh response, and single-antenna directivity done through Nvidia Sionna. The results indicate that far-reaching flight spatial path scanning is achieved with high-end UAVs and low-cost SARs, but the accuracy is relatively low. Higher accuracy is achieved with low-end UAVs with high-cost SARs. Further, the results point out that selecting UAV-SAR is critical in terms of energy and accuracy for a specific target application. The outcomes show tradeoffs in the selection design.

Aperture-dependent motion compensation is important for wide beam Synthetic Aperture Radar (SAR) data processing. This paper studies a wide beam motion compensation algorithm based on frequency division. It takes blocks along azimuth dimension in frequency domain and applies angle-variant motion compensation in time domain. With this frequency division based motion compensation approach, the effects of aperture-dependent residual phase errors are corrected precisely. The rationale and procedure of this algorithm are introduced in detail. Point targets and images of a P-band airborne SAR with motion errors are simulated to validate this algorithm. Compared with the wide beam motion compensation algorithms based on time division, the proposed algorithm has better performance, especially in terms of high-frequency motion errors.

Estimation of forest aboveground biomass (AGB) has become one of the main challenges of remote sensing science for global observation of carbon storage and changes in the past few decades. We examine the impact of plot size at different spatial resolutions, incidence angles, and polarizations on the forest biomass estimation using L-band polarimetric Synthetic Aperture Radar data acquired by NASA’s Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne system. Field inventory data from 32 1.0 ha plots (AGB < 200 Mg ha−1) in approximately even-aged forests in a temperate to boreal transitional region in the state of Maine were divided into subplots at four different spatial scales (0.0625 ha, 0.25 ha, 0.5 ha, and 1.0 ha) to quantify aboveground biomass variations. The results showed a large variability in aboveground biomass at smaller plot size (0.0625 ha). The variability decreased substantially at larger plot sizes (>0.5 ha), suggesting a stability of field-estimated biomass at scales of about 1.0 ha. UAVSAR backscatter was linked to the field estimates of aboveground biomass to develop parametric equations based on polarized returns to accurately map biomass over the entire radar image. Radar backscatter values at all three polarizations (HH, VV, HV) were positively correlated with field aboveground biomass at all four spatial scales, with the highest correlation at the 1.0 ha scale. Among polarizations, the cross-polarized HV had the highest sensitivity to field estimated aboveground biomass (R2 = 0.68). Algorithms were developed that combined three radar backscatter polarizations (HH, HV, and VV) to estimate aboveground biomass at the four spatial scales. The predicted aboveground biomass from these algorithms resulted in decreasing estimation error as the pixel size increased, with the best results at the 1 ha scale with an R2 of 0.67 (p < 0.0001), and an overall RMSE of 44 Mg·ha−1. For AGB < 150 Mg·ha−1, the error reduced to 23 Mg·ha−1 (±15%), suggesting an improved AGB prediction below the L-band sensitivity range to biomass. Results also showed larger bias in aboveground biomass estimation from radar at smaller scales that improved at larger spatial scales of 1.0 ha with underestimation of −3.62 Mg·ha−1 over the entire biomass range.