Ultra Wide Band Signal Modeling for Radar Receiver Characterization

Results for modeling, simulation, and analysis of interference effects that modern wideband signals have on existing narrowband radar system performance are presented. In particular, radar detection performance is characterized using a basic radar receiver model and operational parameters consistent with those of the ARSR-4 air route surveillance radar. Two modern wideband signals (interferers) are addressed in this work, including the GPS military signal (M-Code signal) and a direct sequence ultra wideband (DS-UWB) waveform meeting outdoor emission restrictions imposed by the Federal Communications Commission (FCC). Interference effects are characterized for an unmodulated sinusoidal pulse as well as linear frequency modulated (LFM) and bi-phase Barker coded pulse compression waveforms. Finally, coherent pulse integration is addressed and interference mitigation demonstrated via improved detection performance. Worst case detection scenarios from the radar\u27s perspective are considered for all cases. M-Code interference results indicate that at proposed received power levels of -160 to -130 dBW, radar detection performance is severely degraded with expected improvement occurring when pulse integration is employed. DS-UWB interference results indicate that at maximum transmit power levels specified by the FCC, the DS-UWB waveform has minimal impact on detection performance for radar receiver/UWB transmitter separation distances beyond 0.5 meters. This separation distance is reduced further when pulse integration is employed. (8 tables, 42 figures, 25 refs.

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Multitones’ Performance for Ultra Wideband Software Defined Radar

This chapter proposes and tests an approach for an unbiased study of radar waveforms’ performances. Through an empirical performance analysis, the performances of Chirp and Multitones are compared with both simulations and measurements. An ultra wideband software defined radar prototype was designed and the prototype has performances comparable to the state of the art in software defined radar. The study looks at peak-to-mean-envelope power ratio, spectrum efficiency, and pulse compression as independent waveform criteria. The experimental results are consistent with the simulations. The study shows that a minimum of 10 bits resolution for the AD/DA converters is required to obtain near-optimum performances

Mathematical optimization techniques for cognitive radar networks

This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from a bistatic model (i.e. two transmitters and a single receiver) and ending with a cognitive network (i.e. multiple transmitting and multiple receiving radars). The environment under investigation always features strong signal-dependent clutter and noise. All algorithms are based on an iterative waveform-filter optimization. The waveform optimization is based on convex optimization techniques and the exploitation of initial radar waveforms characterized by desired auto and cross-correlation properties. Finally, robust optimization techniques are introduced to account for the assumptions made by cognitive radars on certain second order statistics such as the covariance matrix of the clutter. More specifically, initial optimization techniques were proposed for the case of bistatic radars. By maximizing the signal to interference and noise ratio (SINR) under certain constraints on the transmitted signals, it was possible to iteratively optimize both the orthogonal transmission waveforms and the receiver filter. Subsequently, the above work was extended to a convex optimization framework for a waveform design technique for bistatic radars where both radars transmit and receive to detect targets. The method exploited prior knowledge of the environment to maximize the accumulated target return signal power while keeping the disturbance power to unity at both radar receivers. The thesis further proposes convex optimization based waveform designs for multiple input multiple output (MIMO) based cognitive radars. All radars within the system are able to both transmit and receive signals for detecting targets. The proposed model investigated two complementary optimization techniques. The first one aims at optimizing the signal to interference and noise ratio (SINR) of a specific radar while keeping the SINR of the remaining radars at desired levels. The second approach optimizes the SINR of all radars using a max-min optimization criterion. To account for possible mismatches between actual parameters and estimated ones, this thesis includes robust optimization techniques. Initially, the multistatic, signal-dependent model was tested against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered signal-dependent clutter scenario. Therefore a new approach was derived where uncertainty was assumed directly on the radar cross-section and Doppler parameters of the clutters. Approximations based on Taylor series were invoked to make the optimization problem convex and {subsequently} determine robust waveforms with specific SINR outage constraints. Finally, this thesis introduces robust optimization techniques for through-the-wall radars. These are also cognitive but rely on different optimization techniques than the ones previously discussed. By noticing the similarities between the minimum variance distortionless response (MVDR) problem and the matched-illumination one, this thesis introduces robust optimization techniques that consider uncertainty on environment-related parameters. Various performance analyses demonstrate the effectiveness of all the above algorithms in providing a significant increase in SINR in an environment affected by very strong clutter and noise

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

Multiple moving target detection with ultra wideband radar using super-resolution algorithms

The improvements in microwave electronics opened the way to build microwave components such as low noise amplifiers, samplers and pulse generators that are broadband. As these building blocks are being developed, new applications become subject of research. Ultra wideband radar is one of these subjects. Major applications of ultra wideband radars are behind the wall imaging, biomedical imaging and buried land mine detection. In this study we aimed to locate multiple scatterers that are moving. Even though there are many scatterers in an environment, detection of moving targets is possible using differences of successive radar snapshots. This is generally the case when behind the wall human targets are to be detected. We investigated the effectiveness of various types Multiple Signal Classification (MUSIC) algorithms on the data acquired by our ultra wideband radar prototype. In ideal computer simulations, Time Reversal MUSIC (TRM) algorithm provides successful estimations of both directions and distances of multiple targets. However in practice where non-ideal effects are existent, the performance of TRM algorithm is estimating the target distances degrades. On the other hand, Delay Estimation MUSIC algorithm provides better estimates for the distances of the targets since it is less sensitive to phase noise. Combining the output of TRM algorithm for target directions and the output of Delay Estimation MUSIC method for target distances resulted in successful localization of targets. Experiments are performed using two moving targets in order to test the effectiveness the proposed processing scheme. The problem of detection ambiguities is also considered and several methods to resolve actual targets are presented

REAL-TIME ADAPTIVE PULSE COMPRESSION ON RECONFIGURABLE, SYSTEM-ON-CHIP (SOC) PLATFORMS

New radar applications need to perform complex algorithms and process a large quantity of data to generate useful information for the users. This situation has motivated the search for better processing solutions that include low-power high-performance processors, efficient algorithms, and high-speed interfaces. In this work, hardware implementation of adaptive pulse compression algorithms for real-time transceiver optimization is presented, and is based on a System-on-Chip architecture for reconfigurable hardware devices. This study also evaluates the performance of dedicated coprocessors as hardware accelerator units to speed up and improve the computation of computing-intensive tasks such matrix multiplication and matrix inversion, which are essential units to solve the covariance matrix. The tradeoffs between latency and hardware utilization are also presented. Moreover, the system architecture takes advantage of the embedded processor, which is interconnected with the logic resources through high-performance buses, to perform floating-point operations, control the processing blocks, and communicate with an external PC through a customized software interface. The overall system functionality is demonstrated and tested for real-time operations using a Ku-band testbed together with a low-cost channel emulator for different types of waveforms