The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation.
Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system. This is accomplished by comparing an online-generated SAR image with a reference image obtained a priori. The distortions relative to the reference image are learned and exploited with a convolutional neural network to recover the initial navigational errors, which can be used to recover the true flight trajectory throughout the synthetic aperture. The proposed neural network approach is able to learn to predict the initial errors on both simulated and real SAR image data.
The popularity of using light aircraft such as UAVs is growing in a variety of applications from military use to urban consumer use. Regardless of the application, precision navigation is necessary and usually dependent on a global navigation satellite system (GNSS). Unfortunately, such systems, such as GPS, are easily denied. GPS can, for example, be jammed or spoofed either accidentally or intentionally. It can also be obscured or subject to multipath in the presence of natural or manmade structures obscuring the sky. The ease at which GPS can be denied motivates the field of GPS-denied navigation.GPS-denied navigation can be accomplished in a variety of ways and typically involves an inertial navigation system supplemented by auxiliary sensors such as cameras, lidar, radar, etc. (Balamurugan et al., 2016). These supplementary sensors provide measurements that replace information lost by GPS denial. Some current research explores the feasibility of using synthetic aperture radar (SAR) as an
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