A Compensation Method for Airborne SAR with Varying Accelerated Motion Error

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.

In unmanned aerial vehicle (UAV) synthetic aperture radar (SAR), step-frequency mode is usually used due to the limitation of size and weight. Due to the small size and weight, the trajectory deviation is so serious that the motion compensation becomes very complex. In order to estimate and compensate the motion error in UAV SAR, a two-step motion compensation approach is proposed in this paper. Firstly, the global positioning system (GPS) and inertial navigation system (INS) data is used for the coarse compensation to eliminate the major motion error. Secondly, fine phase error compensation based on raw SAR data is processed to circumvent the residual phase error. The fine phase error is estimated by weighted phase gradient autofocus (WPGA) in each sub-pulse of step-frequency mode. Then the weight least square (WLS) method is used to obtain the final focusing phase from the estimated phase of sub-pulse. Real data experiments show that the proposed method is suitable for UAV SAR motion compensation.

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.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

Current sonar autofocus techniques for blur removal originate in the radar community but have not provided a complete solution for Synthetic Aperture Sonar (SAS) imagery. The wide-beam, wide-band nature of SAS imagery makes implementation of Synthetic Aperture Radar (SAR) autofocus techniques difficult. This paper describes a generalisation of the standard Phase Gradient Autofocus (PGA) algorithm used in spotlight SAR that allows operation with stripmap SAS geometries. PGA uses prominent points within the target scene to estimate image blurring and phase errors. We show how PGA can be generalised to work with wide-band, wide-beam stripmap geometries. The SPGA method works by employing wave number domain 2D phase estimation techniques. The 2D phase errors are related to aperture position errors using the wave number transform. Robust sway estimates are obtained by using redundancy over a number of target points. We also present an improved Phase Curvature Autofocus (PCA) algorithm using the wave number transform. Preliminary results from the two algorithms (both on field-collected and simulated data sets) are presented and related to those obtained using previous methods. A discussion of SPGA's benefits over traditional algorithms and the limitations of the SPGA algorithm is presented. The SPGA algorithm was found to perform better than 2-D PCA on both simulated and field-collected data sets. Further testing on a variety of target scenes and imagery is required to investigate avenues of autofocus improvement.

Global Positioning System and Inertial Navigation System can be used to determine position and velocity. A Global Positioning System module is able to accurately determine position without sensor drift, but its usage is limited in heavily urbanized environments and heavy vegetation. While high-cost tactical-grade Inertial Navigation System can determine position accurately, low-cost micro-electro-mechanical system Inertial Navigation System sensors are plagued by significant errors. Global Positioning System is coupled with Inertial Navigation System to correct the errors, while Inertial Navigation System itself can be used to provide navigation solution during a Global Positioning System outage. Data from Global Positioning System and Inertial Navigation System can be integrated by extensive Kalman filtering, using loosely coupled integration architecture to provide navigation solutions. In this study, real-time low-cost loosely coupled micro-electro-mechanical system Inertial Navigation System/Global Positioning System sensors have been used for pedestrian navigation. Trial runs of Global Positioning System outages have been conducted to determine the accuracy of the system described. The micro-electro-mechanical system Inertial Navigation System/Global Positioning System can successfully project a trajectory during a Global Positioning System outage and produces a root mean square error of 9.35 m in latitude direction and 10.8 m in longitude direction. This technology is very suitable for visually impaired pedestrians.

Synthetic Aperture Radar (SAR) is a remote sensing technique that allows the generation of microwave images of the Earth surface, independently of weather condition and of sun illumination. SAR sensors can be either mounted on spacecrafts (spaceborne systems) or placed on airplanes (airborne systems). In this thesis the study of problems related to the use of airborne SAR is addressed. The thesis is organized in five chapters and Chapter 1 presents the basic rationale of SAR technique. The assumption that the sensor flight path is a straight line is adopted in the first chapter, which is preliminary to the subsequent analysis, and allows introducing the formalism adopted in Chapters 2-5, where the assumption of straight flight path, not realistic for airborne systems, is relaxed, and the original results of this thesis are presented. Airborne SAR acquisition model is deeply analyzed, and realization of an efficient airborne raw data simulator, which represents a useful tool for mission planning, SAR system design and inversion algorithm testing, is presented (Chapter 2). The analysis of the airborne acquisition model allows also highlighting problems related to airborne SAR focusing. In particular, a quantitative analysis of the accuracy achievable by airborne SAR images is performed; extension to the repeat pass airborne interferometry is also included (Chapter 3). Such an analysis allows, on one side, quantifying the airborne repeat pass interferogram phase accuracy in terms of the expected motion measurement system accuracy (Chapter 3); on the other side, designing a new efficient airborne SAR processor (Chapter 4). Theoretical results presented in Chapter 3 are, finally, validated by an airborne differential interferometry experiment carried out by using real data (Chapter 5).

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

Two methods of focusing synthetic aperture radar (SAR) images are compared. Both a conventional subaperture cross-correlation method and a new phase gradient autofocus (PGA) algorithm developed at Sandia National Laboratories are shown to perform well if high-order phase errors are not present. With the introduction of significant high-order phase errors [e.g., due to uncompensated platform motion], both algorithms suffer a loss in performance. However, relative performance degradation is less for PGA than for the subaperture focusing technique. An explanation is presented for the observed behavior of the two autofocus techniques.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A method of motion status estimation of airborne synthetic aperture radar (SAR) platform in short subapertures via parametric sparse representation is proposed for high-resolution SAR image autofocusing. The SAR echo is formulated as a jointly sparse signal through a parametric dictionary matrix, which converts the problem of SAR motion status estimation into a problem of dynamic representation of jointly sparse signals. A full synthetic aperture is decomposed into several subapertures to estimate the dynamic motion parameters of a platform, and SAR motion compensation is achieved by refining the estimation of the equivalent platform motion parameters, i.e., the azimuth velocity and the radial acceleration of the radar platform, at each subaperture in an iterative fashion. Experimental results based on both simulated and real data demonstrate that: 1) the proposed algorithm outperforms the map-drift algorithm and the phase gradient autofocus algorithm in terms of the imaging quality and 2) compared to the iterative minimum-entropy autofocus, the proposed algorithm produces the comparative imaging quality with less computational complexity in complex motion environment.

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.

UAVs have the potential for autonomous airborne remote sensing applications that require rapid response to natural hazards (e.g.. volcano eruptions, earthquakes). As these applications require very accurate positioning, such as airborne Synthetic Aperture Radar, tightly coupled Global Positioning System (GPS) Precise Point Positioning (PPP) Inertial Navigation Systems (INS) are an attractive method to perform real-time aircraft positioning. In particular, PPP can achieve a level of positioning accuracy that is similar to Real-Time Kinematic (RTK) GPS, without the need of a relatively close GPS reference station. However, the PPP method is known to converge to accurate positioning more slowly when compared to RTK, a drawback of PPP that is amplified whenever the receiver platform is faced with GPS challenged environments, such as poor satellite visibility and frequent phase breaks. Unfortunately, these challenging conditions occur more often when the platform being positioned is an aircraft that experiences abrupt changes in attitude. In this paper we present the use of a simulation environment to characterize the position estimation performance sensitivity of PPP/INS through a Monte Carlo analysis that is considered under various conditions, such as: the intensity of multipath errors, the number of phase breaks that occur in a flight, satellite geometry, atmospheric conditions, noise characteristics and grade of the inertial sensor, and accuracy of GPS orbit products.

With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

Ice shelves and other ice features along the northern coast of Ellesmere Island are a complex mixture of ice originating from marine and meteoric sources. This is reflected in the considerable variability in Synthetic Aperture Radar (SAR) backscatter in remotely sensed imagery. This study analyzed the properties of three different ice types across the Milne and Petersen ice shelves and associated them with SAR backscatter. The results indicated that the grain diameter of each ice type was unique in horizontal thin sections and that bubble shape and size were associated with SAR variables. Furthermore, cores extracted from areas originally thought to be glacially-fed were identified as being marine in origin, suggesting that considerable surface ablation has taken place over the past three decades. This research offers the potential to improve our ability to assess long-term changes (past and future) of the ice cover along the northern coast of Ellesmere Island.

This paper presents the design and analysis of integrating global positioning system (GPS), inertial navigation system (INS), and synthetic aperture radar (SAR). The intent of this research is to predict the performance of the proposed targeting system. The analysis presented here is based on a well-tuned, full-order extended Kalman filter. The targeting aspects of the integrated INS, GPS, and SAR are investigated for both relative and targeting. The simulation results indicate a theoretical possibility of obtaining absolute target position with a CEP of 10 ft. >