Effect and Compensation of Timing Jitter in Through-Wall Human Indication via Impulse Through-Wall Radar

Impulse through-wall radar (TWR) is considered as one of preferred choices for through-wall human indication due to its good penetration and high range resolution. Large bandwidth available for impulse TWR results in high range resolution, but also brings an atypical adversity issue not substantial in narrowband radars — high timing jitter effect, caused by the non-ideal sampling clock at the receiver. The fact that impulse TWR employs very narrow pulses makes little jitter inaccuracy large enough to destroy the signal correlation property and then degrade clutter suppression performance. In this paper, we focus on the timing jitter impact on clutter suppression in through-wall human indication via impulse TWR. We setup a simple timing jitter model and propose a criterion namely average range profile (ARP) contrast is to evaluate the jitter level. To combat timing jitter, we also develop an effective compensation method based on local ARP contrast maximization. The proposed method can be implemented pulse by pulse followed by exponential average background subtraction algorithm to mitigate clutters. Through-wall experiments demonstrate that the proposed method can dramatically improve through-wall human indication performance

Computational Algorithms for Improved Synthetic Aperture Radar Image Focusing

High-resolution radar imaging is an area undergoing rapid technological and scientific development. Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) are imaging radars with an ever-increasing number of applications for both civilian and military users. The advancements in phased array radar and digital computing technologies move the trend of this technology towards higher spatial resolution and more advanced imaging modalities. Signal processing algorithm development plays a key role in making full use of these technological developments.In SAR and ISAR imaging, the image reconstruction process is based on using the relative motion between the radar and the scene. An important part of the signal processing chain is the estimation and compensation of this relative motion. The increased spatial resolution and number of receive channels cause the approximations used to derive conventional algorithms for image reconstruction and motion compensation to break down. This leads to limited applicability and performance limitations in non-ideal operating conditions.This thesis presents novel research in the areas of data-driven motion compensation and image reconstruction in non-cooperative ISAR and Multichannel Synthetic Aperture Radar (MSAR) imaging. To overcome the limitations of conventional algorithms, this thesis proposes novel algorithms leading to increased estimation performance and image quality. Because a real-time imaging capability is important in many applications, special emphasis is placed on the computational aspects of the algorithms.For non-cooperative ISAR imaging, the thesis proposes improvements to the range alignment, time window selection, autofocus, time-frequency-based image reconstruction and cross-range scaling procedures. These algorithms are combined into a computationally efficient non-cooperative ISAR imaging algorithm based on mathematical optimization. The improvements are experimentally validated to reduce the computational burden and significantly increase the image quality under complex target motion dynamics.Time domain algorithms offer a non-approximated and general way for image reconstruction in both ISAR and MSAR. Previously, their use has been limited by the available computing power. In this thesis, a contrast optimization approach for time domain ISAR imaging is proposed. The algorithm is demonstrated to produce improved imaging performance under the most challenging motion compensation scenarios. The thesis also presents fast time domain algorithms for MSAR. Numerical simulations confirm that the proposed algorithms offer a reasonable compromise between computational speed and image quality metrics

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Ground-based ISAR imaging of cooperative and non-cooperative sea vessels with 3-D rotational motion

Includes bibliographical references (leaves 175-188).Inverse Synthetic Aperture Radar (ISAR) images of sea vessels are a rich source of information for radar cross section (RCS) measurement and ship classification. However, ISAR imaging of sea vessels is a challenging task because the 3-D rotational motion of such vessels often gives rise to blurring. Blurry ISAR images are not desirable because they lead to inaccurate parameter estimation, which reduces the probability of correct classification. The objective of this thesis is to explain how 3-D rotational motion causes blurring in ISAR imagery and to develop effective techniques for imaging cooperative and non-cooperative sea vessels for RCS measurement and ship-classification purposes respectively. Much research has been done to investigate the effect of 3-D rotational motion on an ISAR image under the assumption that an object's axis of rotation is constant over the coherent processing interval (CPI). In this thesis, a new quaternion-based system model is proposed to characterise the amount of blurring in an ISAR image when a sea vessel possesses 3-D rotational motion over a CPI. Simulations were done to characterise the migration of a scatterer through Doppler cells due to the time-varying nature of the Doppler generating axis of rotation. Simulation results with realistic 3-D rotational motion show substantial blurring in the cross-range dimension of the resulting ISAR image, and this blurring is attributed to the time-varying nature of the angle of the Doppler generating axis of rotation and the object's rotation rate over the CPI

The Design and Implementation of a Mobile RFID Tag Sorting Robot

Libraries, manufacturing lines, and offices of the future all stand to benefit from knowing the exact spatial order of RFID-tagged books, components, and folders, respectively. To this end, radio- based localization has demonstrated the potential for high accuracy. Key enabling ideas include motion-based synthetic aperture radar, multipath detection, and the use of different frequencies (channels). But indoors in real-world situations, current systems often fall short of the mark, mainly because of the prevalence and strength of "multipath" reflections of the radio signal off nearby objects. In this paper we describe the design and implementation of MobiTagbot, an autonomous wheeled robot reader that conducts a roving survey of the above such areas to achieve an exact spatial order of RFID- tagged objects in very close (1–6 cm) spacings. Our approach leverages a serendipitous correlation between the changes in multipath reflections that occur with motion and the effect of changing the carrier frequency (channel) of the RFID query. By carefully observing the relationship between channel and phase, MobiTagbot detects if multipath is likely prevalent at a given robot reader location. If so, MobiTagbot excludes phase readings from that reader lo- cation, and generates a final location estimate using phase readings from other locations as the robot reader moves in space. Experimentally, we demonstrate that cutting-edge localization algorithms including Tagoram are not accurate enough to exactly order items in very close proximity, but MobiTagbot is, achieving nearly 100% ordering accuracy for items at low (3–6 cm) spacings and 86% accuracy for items at very low (1–3 cm) spacings

Active and Passive Multi-Sensor Radar Imaging Techniques Exploiting Spatial Diversity

The work here presented reports several innovative SAR and ISAR radar imaging techniques exploiting the spatial diversity offered by multi-sensor systems in order to improve the performance with respect to the conventional, single channel cases. Both the cases of dedicated transmitters and exploitation of opportunity transmitters are considered