Precise Aperture-Dependent Motion Compensation with Frequency Domain Fast Back-Projection Algorithm

In the area of airborne synthetic aperture radar (SAR), motion compensation (MOCO) is a crucial technique employed to correct the SAR data affected by nonlinear platform trajectory during data acquisition. Due to range-azimuth coupling and computational burden consideration, some approximations, which are valid for SAR systems of moderate aperture length, are usually adopted in commonly used MOCO approaches. However, a much more accurate SAR data processing approach is appealing to process the low-frequency SAR systems with large aperture length, such as P-band. In this paper, a new MOCO approach with high precision and high efficiency is proposed. After the ω - κ processing and the range-dependent MOCO, the analytical expression of a 2-D spectrum of a partially focused SAR image is given. Afterward, aperture reduction is achieved by a chirp modulation technique. Finally, with high precision and less computation cost, back projection along the new built short apertures (affected by the residual motion errors) is employed to yield a fairly well-focused SAR image. Experimental results on simulated and actual P-band SAR data are presented to verify the performance of the proposed approach.

When using motion compensation approaches based on the measurement of motion sensors, the residual uncompensated motion errors due to measurement instrument inaccuracies contribute to phase errors and hence degrade Synthetic Aperture Radar (SAR) images. This paper presents a model to compute the phase error caused by Inertial Measurement Unit (IMU) measurement inaccuracies. By analyzing SAR motion compensation method and the effect of lever arm, this model derives the contribution of each term of IMU inaccuracies towards the residual uncompensated motion errors and provides a method to calculate each order of the residual phase error. According to the model, computed results of the airborne X-band SAR system with POS AV510 accord closely with the actual image quality.

In this work we focus on the Precise Topography- and Aperture-dependent algorithm, briefly PTA, proposed in the recent literature to remove the residual errors still affecting airborne SAR data focused with standard two-step Motion Compensation algorithms. More specifically, we first show that the azimuth-to-frequency mapping used by the PTA procedure is not fully appropriate, because it does not account for the presence of the residual motion errors themselves. Then, we show how to calculate the correct azimuth-to-frequency mapping able to fully account for the presence of the residual motion errors. Finally, we show how to modify the original PTA in such a way to properly exploit the correct azimuth-to-frequency mapping previously discussed.

In this paper, an effective high-resolution imaging approach of moving targets is proposed for the space-borne multi-channel in azimuth high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, which is based on the multi-target tracking technique. Firstly, the echo of multi-channel in the azimuth multi-channel SAR system is utilized to deduce the sub-aperture focusing image of moving targets, where the sub-aperture division and imaging technology is involved. Then, the tracks of the moving targets in the sub-aperture focusing image can be obtained by using the multi-target tracking technology. After that, extract the corresponding data of each moving target in the sub-aperture focusing image, and stitch the data according to the tracks to obtain the secondary focus image of the moving target. Finally, some simulation experiments are implemented to validate the theoretical investigations and the proposed approach in this paper.

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.

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.

Airborne SAR imaging processing needs to estimate motion error to compensate non-ideal trajectory. In this paper, a subaperture imaging scheme for wide azimuth beam airborne SAR systems is proposed. First, the motion error is estimated from the subaperture data and the modified range migration algorithm is applied to obtain the coarse focused image, whose azimuth resolution corresponds to the subaperture Doppler bandwidth. The subaperture image is then projected into a fine grid image, whose coordinates is defined by the imaging geometry. As the subaperture data stream is coming, the azimuth resolution of the grid image will become higher and higher. Finally, the fine image with the azimuth resolution corresponding to the full aperture data can be obtained. Since the motion error estimation is based on the sub-aperture data, the imaging processing is suitable for real-time SAR imaging.

The high orbit height leads to the ultralow signal-to-noise ratio (ULSNR) of geosynchronous (GEO) targets, so it is necessary to increase the coherent processing interval (CPI) to improve the coherent accumulation gain. The ultra-long CPI (ULCPI) causes wide rotational angles and complex signal modulation, which poses a serious challenge to conventional inverse synthetic aperture radar (ISAR) imaging algorithms. Moreover, the high orbit height and high-speed motion of GEO targets make the “stop-and-go” model no longer applicable, and intra-pulse motion errors must be taken into consideration. To address the problems, this article proposes a high-resolution ISAR imaging algorithm with joint intra-pulse and inter-pulse high-order motion compensation (JIPHOMC) and sub-aperture images fusion for GEO targets at ULCPI. In this technique, ULCPI is divided into several sub-apertures. Then, with respect to the motion compensation in sub-apertures, we innovatively propose a JIPHOMC algorithm with particle swarm optimization (PSO), which can correct the complex spatial-time-variant (STV) motion errors caused by wide rotational angles and high-speed motion, and eliminate the 2-D high-order coupling. By utilizing image overall constraints, JIPHOMC avoids signal source decomposition processing, thereby boosting algorithm efficiency and robustness. For the sub-aperture images obtained from different perspectives, we develop a sub-aperture image fusion algorithm (SAIF) based on non-negative matrix factorization (NMF) of structured weighted sparse enhancement, which can ensure the integrity of the target structure and enhance the image signal-to-noise ratio (SNR), so as to achieve high-resolution ISAR imaging of GEO targets at ULCPI. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data corroborate that the proposed algorithm outperforms traditional ISAR imaging methods for GEO targets at ULCPI.

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

The wavenumber domain algorithm is an ideal solution for high-resolution and highly squinted (HRHS) synthetic aperture radar (SAR) imaging in the case of an ideal straight trajectory. However, for airborne HRHS SAR imaging, the Stolt mapping leads to nonsystematic range cell migration (NsRCM) and secondary range compression (SRC) for the HRHS SAR, which causes the HRHS SAR image to defocus severely. To obtain a well-focused HRHS SAR image, a new autofocusing algorithm for HRHS SAR imagery based on an equivalent broadside model is proposed in this article. After coarse motion compensation, linear range walk correction and azimuth resampling are employed to transform the HRHS SAR data into the equivalent broadside SAR data, which has been proved to greatly reduce NsRCM and SRC. After that, the motion error prior structure in the 2-D wavenumber domain is revealed. According to the structure, a novel 2-D wavenumber domain autofocusing algorithm is proposed by the relationship between the 1-D azimuth phase error and the 2-D wavenumber domain phase error correction. Finally, the well-focused HRHS SAR imagery is obtained. Experiments based on simulated and acquired data are carried out to verify the necessity and effectiveness of the proposed algorithm for HRHS SAR imaging.

To obtain a high-resolution and wide-swath image, the azimuth multichannel technique has been widely used in synthetic aperture radar (SAR) systems to overcome the contradiction between the wide swath and high pulse repetition frequency. For a high image quality, channel mismatch correction is an essential step in the multichannel SAR data imaging. However, in the case of airborne multichannel SAR, motion errors will severely degrade the performance of channel mismatch correction. To deal with this problem, this article proposes an improved airborne multichannel SAR imaging method with motion compensation, and range-variant channel mismatch correction. First, motion errors are compensated based on resampling and phase compensation. Then, the time-delay and constant gain-phase errors between channels are estimated and corrected, followed by the range-variant phase error correction based on a novel range-down-sampling method, which reduces the influence of motion errors on the channel mismatch correction significantly. Finally, simulated and real data processing results are used to demonstrate the effectiveness of the proposed method.

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.

In synthetic aperture radar (SAR) imaging, generation of fine resolution imagery requires measurement and compensation for the relative translational and rotational motions between the antenna phase center (APC) and the scene being imaged. A difficulty arises when motion compensating stripmap mode signal history data from a SAR requiring a wide angle azimuth beamwidth. In general, the required compensation varies with scatterer location within the azimuth beam. The usual approach in stripmap motion compensation is to ignore this requirement and to compensate only the component of sensor motion in the nominal antenna boresight direction (usually broadside). In the widebeam system, this procedure can lead to unacceptable phase errors even if sensor motion is measured without error. In this paper, we survey the motion compensation challenge to ultra-wideband/widebeam (UWB/WB) SAR image formation. The emphasis here is on formation of fine resolution digital imagery from low frequency (VHF/UHF) UWB/WB data collected in stripmap mode by an airborne SAR system . We examine differences in motion compensation requirements and approaches between a UWB/WB system and a conventional X-band SAR system. This paper presents an improved motion compensation algorithm applicable to widebeam stripmap SAR. We demonstrate this procedure with imagery from the fine resolution ARPA/ERIM/NAWC P3 ultra-wideband radar system.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper, a novel moving target detection and tracking approach is presented based on Gaussian mixture probability hypothesis density (GMPHD) filter in synthetic aperture radar (SAR). Based on the advantages of GMPHD filter and the characteristics of moving target in SAR imagery, GMPHD filter is employed on potential moving target candidates extracted from a sequence of temporal and spatial sub-aperture SAR images to detect and track the moving targets in heavy clutter environment. Utilizing the tracking algorithm, the states and the number of moving targets are obtained over time. Simultaneously, the strong ground stationary clutter lacking of the dynamic behavior is eliminated finally. Both simulation and real Gotcha data set processing results are provided to demonstrate the effectiveness of the proposed approach.

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.