Cognitive radar network design and applications

PhD ThesisIn recent years, several emerging technologies in modern radar system design are attracting the attention of radar researchers and practitioners alike, noteworthy among which are multiple-input multiple-output (MIMO), ultra wideband (UWB) and joint communication-radar technologies. This thesis, in particular focuses upon a cognitive approach to design these modern radars. In the existing literature, these technologies have been implemented on a traditional platform in which the transmitter and receiver subsystems are discrete and do not exchange vital radar scene information. Although such radar architectures benefit from these mentioned technological advances, their performance remains sub-optimal due to the lack of exchange of dynamic radar scene information between the subsystems. Consequently, such systems are not capable to adapt their operational parameters “on the fly”, which is in accordance with the dynamic radar environment. This thesis explores the research gap of evaluating cognitive mechanisms, which could enable modern radars to adapt their operational parameters like waveform, power and spectrum by continually learning about the radar scene through constant interactions with the environment and exchanging this information between the radar transmitter and receiver. The cognitive feedback between the receiver and transmitter subsystems is the facilitator of intelligence for this type of architecture. In this thesis, the cognitive architecture is fused together with modern radar systems like MIMO, UWB and joint communication-radar designs to achieve significant performance improvement in terms of target parameter extraction. Specifically, in the context of MIMO radar, a novel cognitive waveform optimization approach has been developed which facilitates enhanced target signature extraction. In terms of UWB radar system design, a novel cognitive illumination and target tracking algorithm for target parameter extraction in indoor scenarios has been developed. A cognitive system architecture and waveform design algorithm has been proposed for joint communication-radar systems. This thesis also explores the development of cognitive dynamic systems that allows the fusion of cognitive radar and cognitive radio paradigms for optimal resources allocation in wireless networks. In summary, the thesis provides a theoretical framework for implementing cognitive mechanisms in modern radar system design. Through such a novel approach, intelligent illumination strategies could be devised, which enable the adaptation of radar operational modes in accordance with the target scene variations in real time. This leads to the development of radar systems which are better aware of their surroundings and are able to quickly adapt to the target scene variations in real time.Newcastle University, Newcastle upon Tyne: University of Greenwich

Random Phase Center Motion Technique for Enhanced Angle-Doppler Discrimination Using MIMO Radars

A random Phase Center Motion (PCM) technique is presented in this paper, based on Frequency Modulated Continuous Wave (FMCW) radar, in order to suppress the angle- Doppler coupling in Time Division Multiplex (TDM) Multiple- Input-Multiple-Output (MIMO) radar when employing sparse array structures. The presented approach exploits an apparently moving transmit platform or PCM due to spatio-temporal transmit array modulation. In particular, the work considers a framework utilizing a random PCM trajectory. The statistical characterization of the random PCM trajectory is devised, such that the PCM and the target motion coupling is minimal, while the angular resolution is increased by enabling the virtual MIMO concept. In more details, this paper discusses sidelobe suppression approaches within the angle-Doppler Ambiguity Function (AF) by introducing a phase center probability density function within the array. This allows for enhanced discrimination of multiple targets. Simulation results demonstrate the suppression angle- Doppler coupling by more than 30 dB, even though spatiotemporal transmit array modulation is done across chirps which leads usually to strong angle-Doppler coupling

Discrimination of Angle-Doppler Signatures using Arbitrary Phase Center Motion for MIMO Radars

A novel Phase Center Motion (PCM) based technique for discriminating angle-Doppler signatures within Multiple-Input-Multiple-Output (MIMO) radars using Frequency Modulated Continuous Wave (FMCW) has been explored in this work. The PCM technique induces angle dependent Doppler shifts in the back-scattered signal, wherein a modified Doppler post processing for FMCW leads to joint angle-Doppler processing. Specifically, we intend to design unique spatialtemporal motion of the phase center on each individual MIMO radar channel in an effort to synthesize nearly orthogonal angle-Doppler signatures. Subsequently, we also develop a MIMO radar receiver design, which would be capable of discriminating between these induced angle-Doppler signatures. The asymptotic investigation provides a Bessel function characteristic. Simulation results demonstrate a significant side-lobe suppression of 8:5 dB for an individual PCM trajectory and 7 dB over distinct PCM trajectories, in an attempt towards realization of nearly orthogonal MIMO radar channels

A Bayesian nonparametric approach to tumor detection using UWB imaging

We develop a tumor detection and discrimination algorithm for Ultra-Wideband (UWB) microwave imaging of breast cancer based on a Bayesian nonparametric approach. We model the UWB backscattered signal as a mixture of distinct scatterer contributions, and use a Dirichlet Process mixture model (DPMM) to describe the amplitudes and delays of the backscattered returns. Because of the unbounded complexity afforded by the DPMM, model under-fitting is avoided and parameters like the clutter covariance matrix in other commonly used approaches, need not be estimated. The DPMM allows us to perform discrimination when there are multiple tumor and clutter sources that present as extended radar targets. After performing discrimination, we distinguish the tumor sources from other clutter sources using a generalized likelihood ratio test (GLRT). We perform experiments on a breast phantom with realistic dielectric contrast ratios, and compare the performance of our algorithm with a direct GLRT approach. Our numerical results show performance improvement in terms of tumor detection probability and Signal to Interference and Noise Ratio (SINR) gain of approximately 2.2 dB at a probability of detection of 0.9 over the GLRT method

An impulse radio ultrawideband system for contactless noninvasive respiratory monitoring

We design a impulse radio ultrawideband radar monitoring system to track the chest wall movement of a human subject during respiration. Multiple sensors are placed at different locations to ensure that the backscattered signal could be detected by at least one sensor no matter which direction the human subject faces. We design a hidden Markov model to infer the subject facing direction and his or her chest movement. We compare the performance of our proposed scheme on 15 human volunteers with the medical gold standard using respiratory inductive plethysmography (RIP) belts, and show that on average, our estimation is over 81% correlated with the measurements of a RIP belt system. Furthermore, in order to automatically differentiate between periods of normal and abnormal breathing patterns, we develop a change point detection algorithm based on perfect simulation techniques to detect changes in the subject's breathing. The feasibility of our proposed system is verified by both the simulation and experiment results.Accepted versio