Frequency Diverse Array Radar: Signal Characterization and Measurement Accuracy

Radar systems provide an important remote sensing capability, and are crucial to the layered sensing vision; a concept of operation that aims to apply the right number of the right types of sensors, in the right places, at the right times for superior battle space situational awareness. The layered sensing vision poses a range of technical challenges, including radar, that are yet to be addressed. To address the radar-specific design challenges, the research community responded with waveform diversity; a relatively new field of study which aims reduce the cost of remote sensing while improving performance. Early work suggests that the frequency diverse array radar may be able to perform several remote sensing missions simultaneously without sacrificing performance. With few techniques available for modeling and characterizing the frequency diverse array, this research aims to specify, validate and characterize a waveform diverse signal model that can be used to model a variety of traditional and contemporary radar configurations, including frequency diverse array radars. To meet the aim of the research, a generalized radar array signal model is specified. A representative hardware system is built to generate the arbitrary radar signals, then the measured and simulated signals are compared to validate the model. Using the generalized model, expressions for the average transmit signal power, angular resolution, and the ambiguity function are also derived. The range, velocity and direction-of-arrival measurement accuracies for a set of signal configurations are evaluated to determine whether the configuration improves fundamental measurement accuracy

SUB-NYQUIST SENSING AND SPARSE RECOVERY OF WIDE-BAND INTENSITY MODULATED OPTICAL SIGNALS

Intensity modulated optical transmitters, wide-bandwidth electro-optical receivers, high-speed digitizers, and digital matched-filters are being used in hybrid lidar-radar systems to measure the range and reflectivity of objects located within degraded visual underwater environments. These methods have been shown to mitigate the adverse effects of the turbid underwater channel due to the de-correlation of the modulated optical signal after undergoing multiple scattering events. The observed frequency-dependent nature of the underwater channel has driven the desire for wider bandwidth waveforms modulated at higher frequencies in order to improve range accuracy and resolution. While the described system has shown promise, the matched filter processing scheme, which is also widely used in the fields of radar and sonar, suffers from inherent limitations. One limitation is based on the achievable range resolution as dictated by the classical time-frequency uncertainty principle, where the bandwidth dictates the measurable resolution. The side-lobes generated during the matched filtering process also present a challenge when trying to detect multiple targets. These limitations are further constrained by currently-available analog-to-digital conversion technologies which restrict the ability to directly sample the wide-band modulated signals. Even in cases where the technology exists that can operate at sufficient rates, often it is prohibitively expensive for many applications and high data rates can pose processing challenges. This research effort addresses both the restrictions imposed by the available analog-to-digital conversion technologies and the limited resolution of the existing time-frequency methods for wide-band signal processing. The approach is based on concepts found within the fields of compressive sensing and sparse signal recovery and will be applied to the detection of objects illuminated with wide-band intensity modulated optical signals. The underlying assumption is that given the directive nature of laser propagation, the illuminated scene is inherently sparse and the limited number of reflecting objects can be treated as point sources. The main objective of this research is to provide results that show, when sampling at rates below those dictated by the traditional Shannon-Nyquist sampling theorem, it is possible to make more efficient use of the samples collected and detect a limited number of reflecting targets using specialized recovery algorithms without reducing system resolution. Through theoretical derivations, empirical simulations, and experimental investigation, it will be shown under what conditions the sub-Nyquist sampling and sparse recovery techniques are applicable, and how the described methods influence resolution, accuracy, and overall performance in the presence of noise

Array Signal Processing for Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a kind of imaging radar that can produce high resolution images of targets and terrain on the ground. At present, most of SAR processing algorithms are based on matched filtering. This method is easy to implement and can produce stable results. However, It also has some limitations. This approach must obey the Nyquist sampling theorem and the resolution depends on bandwidth of pulses. This means that the matched filter approach must be based on a large amount of raw data but the performance is limited. With the development of radar imaging, it is difficult for the matched filtering approach to meet the requirement of high resolution SAR images. In this thesis, a new processing method based on the least squares (LS) beamforming is utilized in the processing of SAR raw data. The model of SAR simulates a virtual linear array. The processing of SAR data can also be seen as a process of beamforming. The 1- D azimuth direction echo data is processed using the beamforming method. Simulation results based on the least squares design method are compared with the matched filtering method and the conventional beamforming method with different windows

Addressing Spectrum Congestion by Spectrally-Cooperative Radar design

This dissertation attempts to address a significant challenge that is encountered by the users of the Radio Frequency (RF) Spectrum in recent years. The challenge arises due to the need for greater RF spectrum by wireless communication industries such as mobile telephony, cable/satellite and wireless internet as a result of growing con-sumer base and demands. As such, it has led to the issue of spectrum congestion as radar systems have traditionally maintain the largest share of the RF spectrum. To resolve the spectrum congestion problem, it has become even necessary for users from both radar and communication systems to coexist within a finite spectrum allocation. However, this then leads to other problems such as the increased likelihood of mutual interference experienced by all systems that are coexisting within the finite spectrum.. In order to address this challenge, the dissertation will seek to resolve it via a two-step approach that are described as follows. For the first step of this approach, it will present a structured and meticulous approach to design a sparse spectrum allocation optimization scheme that will lead to the release of valuable spectrum previously allocated to radar applications for reallocation to other players such as the wireless video-on-demand and telecommunication industries while maintaining the range resolution performance of these radar applications. This sparse bandwidth allocation scheme is implemented using an optimization process utilizing the Marginal Fisher information (MFI) measure as the main metric for optimization. Although the MFI approach belongs to the class of greedy optimization methods that cannot guarantee global convergence, the results obtained indicated that this approach is able to produce a locally optimal solution. For the second step of this approach, it will present on the design of a spectral efficient waveform that can be used to ensure that the allocated spectrum limits will not be violated due to poor spectral emission containment. The design concept of this waveform is based on the joint implementation of the first and higher orders of the Poly-phase coded Frequency Modulated (PCFM) waveform that expands previous research on first order PCFM waveform. As any waveform generated using the PCFM framework possesses good spectral containment and is amenable to high power transmit operations such as radar due to its constant modulus property, thus the combined-orders of PCFM waveform is a very suitable candidate that can be used in conjunction with the sparse bandwidth allocation scheme in the first step for any radar application such that the waveform will further mitigate the issue of interference experienced by other users coexisting within the same band

Compressive Sensing Applied to MIMO Radar and Sparse Disjoint Scenes

The purpose of remote sensing is to acquire information about an object through the propagation of electromagnetic waves, specifically radio waves for radar systems. However, these systems are constrained by the costly Nyquist sampling rate required to guarantee efficient recovery of the signal. The recent advancements of compressive sensing offer a means of efficiently recovering such signals with fewer measurements. This thesis investigates the feasibility of employing techniques from compressive sensing in on-grid MIMO radar in order to identify targets and estimate their locations and velocities. We develop a mathematical framework to model this problem then devise numerical simulations to assess how various parameters, such as the choice of recovery algorithm, antenna positioning, signal to noise ratio, etc., impact performance. The experimental formulation of this project leads to further theoretical questions concerning the benefits of incorporating an underlying signal structure within the compressive sensing framework. We pursue these concerns for the case of sparse and disjoint vectors. Our computational and analytical treatments illustrate that knowledge of the simultaneity of these structures within a signal provides no benefit in reducing the minimal number of measurements needed to robustly recover such vectors from noninflating measurements, regardless of the reconstruction algorithm.Ph.D., Mathematics -- Drexel University, 201