Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, a novel databased motion compensation (MOCO) approach is proposed for the UAV SAR imagery. The approach is implemented by a three-step process: 1) The range-invariant motion error is estimated by the weighted phase gradient autofocus (WPGA), and the nonsystematic range cell migration function is calculated from the estimate for each subaperture SAR data; 2) the retrieval of the range-dependent phase error is executed by a local maximum-likelihood WPGA algorithm; and 3) the subaperture phase errors are coherently combined to perform the MOCO for the full-aperture data. Both simulated and real-data experiments show that the proposed approach is appropriate for highly precise imaging for UAV SAR equipped with only low-accuracy inertial navigation system.

A Robust Motion Compensation Approach for UAV SAR Imagery

In this paper, an extended nonlinear chirp scaling (ENLCS) algorithm for focusing synthetic aperture radar data acquired at high resolution and highly squint angle is proposed. The whole processing of the ENLCS consists of the following three steps. First, a linear range walk correction is used to remove the linear component of target range cell migration (RCM) and to mitigate the range-azimuth coupling of the 2-D spectrum. Second, a bulk second range compression (SRC) is performed in the 2-D frequency domain for compensating the residual RCM, SRC term, and higher order range-azimuth coupling terms. Third, a modified azimuth NLCS (ANLCS) operation is applied to equalize the azimuth frequency modulation rate for azimuth compression. By adopting higher order approximation processing and by properly selecting the scaling coefficients, the proposed modified ANLCS operation has better accuracy and little image misregistration. The overall focusing procedure of the ENLCS algorithm only involves fast Fourier transform and complex multiplication, which means easier implementation and higher efficiency. The experimental results with simulated data prove the effectiveness of the proposed algorithm.

Extended Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Data Focusing

The present work is a review of unmanned aerial systems technology and their subsystems (frame, propellers, motors and batteries, payloads, and data processing). Different applications are evaluated, related to remote sensing, spraying of liquids, and logistics. An overview of the regulatory framework is also developed.

Unmanned Aerial Systems for Civil Applications: A Review

Since synthetic aperture technology was employed in radar signal processing, the information capability of radar has greatly been enhanced. A lot of imaging algorithms have also been developed. However, the high-resolution imaging for highly squinted synthetic aperture radar data is still a difficult issue due to large range migration and strong range dependence on the secondary range compression term that is relatively large and cubic with high focusing sensibilities for high resolution. To accommodate for this problem, the "squint-minimization" operation and azimuth nonlinear chirp scaling (CS) (ANCS) operation are studied in this paper. On the basis of these operations, we propose new imaging algorithms and analyze the characteristic of highly squinted data and the difficulty in focusing these data as well as discussing the principle of ANCS. We also introduce a new CS algorithm, and numerical examples show that the proposed algorithm is able to achieve 0.1 m of resolution under a squint angle as large as 70°s.

Focus Improvement of Highly Squinted Data Based on Azimuth Nonlinear Scaling

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is very important for battlefield awareness. For SAR systems mounted on a UAV, the motion errors can be considerably high due to atmospheric turbulence and aircraft properties, such as its small size, which makes motion compensation (MOCO) in UAV SAR more urgent than other SAR systems. In this paper, based on 3-D motion error analysis, a novel 3-D MOCO method is proposed. The main idea is to extract necessary motion parameters, i.e., forward velocity and displacement in line-of-sight direction, from radar raw data, based on an instantaneous Doppler rate estimate. Experimental results show that the proposed method is suitable for low- or medium-altitude UAV SAR systems equipped with a low-accuracy inertial navigation system.

Xiuwei Jiang

Papers

Motion Compensation for UAV SAR Based on Raw Radar Data

Focus Improvement of Highly Squinted Data Based on Azimuth Nonlinear Scaling

Research for bridge deformation monitoring based on high frequency and multi-system GNSS