Development and Evaluation of a Multistatic Ultrawideband Random Noise Radar

This research studies the AFIT noise network (NoNET) radar node design and the feasibility in processing the bistatic channel information of a cluster of widely distributed noise radar nodes. A system characterization is used to predict theoretical localization performance metrics. Design and integration of a distributed and central signal and data processing architecture enables the Matlab®-driven signal data acquisition, digital processing and multi-sensor image fusion. Experimental evaluation of the monostatic localization performance reveals its range measurement error standard deviation is 4.8 cm with a range resolution of 87.2(±5.9) cm. The 16-channel multistatic solution results in a 2-dimensional localization error of 7.7(±3.1) cm and a comparative analysis is performed against the netted monostatic solution. Results show that active sensing with a low probability of intercept (LPI) multistatic radar, like the NoNET, is capable of producing sub-meter accuracy and near meter-resolution imagery

System design of the MeerKAT L - band 3D radar for monitoring near earth objects

This thesis investigates the current knowledge of small space debris (diameter less than 10 cm) and potentially hazardous asteroids (PHA) by the use of radar systems. It clearly identifies the challenges involved in detecting and tracking of small space debris and PHAs. The most significant challenges include: difficulty in tracking small space debris due to orbital instability and reduced radar cross-section (RCS), errors in some existing data sets, the lack of dedicated or contributing instruments in the Southern Hemisphere, and the large cost involved in building a high-performance radar for this purpose. This thesis investigates the cooperative use of the KAT-7 (7 antennas) and MeerKAT (64 antennas) radio telescope receivers in a radar system to improve monitoring of small debris and PHAs was investigated using theory and simulations, as a cost-effective solution. Parameters for a low cost and high-performance radar were chosen, based on the receiver digital back-end. Data from such radars will be used to add to existing catalogues thereby creating a constantly updated database of near Earth objects and bridging the data gap that is currently being filled by mathematical models. Based on literature and system requirements, quasi-monostatic, bistatic, multistatic, single input multiple output (SIMO) radar configurations were proposed for radio telescope arrays in detecting, tracking and imaging small space debris in the low Earth orbit (LEO) and PHAs. The maximum dwell time possible for the radar geometry was found to be 30 seconds, with coherent integration limitations of 2 ms and 121 ms for accelerating and non-accelerating targets, respectively. The multistatic and SIMO radar configurations showed sufficient detection (SNR 13 dB) for small debris and quasi-monostatic configuration for PHAs. Radar detection, tracking and imaging (ISAR) simulations were compared to theory and ambiguities in range and Doppler were compensated for. The main contribution made by this work is a system design for a high performance, cost effective 3D radar that uses the KAT-7 and MeerKAT radio telescope receivers in a commensal manner. Comparing theory and simulations, the SNR improvement, dwell time increase, tracking and imaging capabilities, for small debris and PHAs compared to existing assets, was illustrated. Since the MeerKAT radio telescope is a precursor for the SKA Africa, extrapolating the capabilities of the MeerKAT radar to the SKA radar implies that it would be the most sensitive and high performing contributor to space situational awareness, upon its completion. From this feasibility study, the MeerKAT 3D distributed radar will be able to detect debris of diameter less than 10 cm at altitudes between 700 km to 900 km, and PHAs, with a range resolution of 15 m, a minimum SNR of 14 dB for 152 pulses for a coherent integration time of 2.02 ms. The target range (derived from the two way delay), velocity (from Doppler frequency) and direction will be measured within an accuracy of: 2.116 m, 15.519 m/s, 0.083° (single antenna), respectively. The range, velocity accuracies and SNR affect orbit prediction accuracy by 0.021 minutes for orbit period and 0.0057° for orbit inclination. The multistatic radar was found to be the most suitable and computationally efficient configuration compared to the bistatic and SIMO configurations, and beamforming should be implemented as required by specific target geometry

Multistatic Specular Meteor Radar Network in Peru: System Description and Initial Results

The mesosphere and lower thermosphere (MLT) region is dominated globally by dynamics at various scales: planetary waves, tides, gravity waves, and stratified turbulence. The latter two can coexist and be significant at horizontal scales less than 500 km, scales that are difficult to measure. This study presents a recently deployed multistatic specular meteor radar system, SIMONe Peru, which can be used to observe these scales. The radars are positioned at and around the Jicamarca Radio Observatory, which is located at the magnetic equator. Besides presenting preliminary results of typically reported large‐scale features, like the dominant diurnal tide at low latitudes, we show results on selected days of spatially and temporally resolved winds obtained with two methods based on: (a) estimation of mean wind and their gradients (gradient method), and (b) an inverse theory with Tikhonov regularization (regularized wind field inversion method). The gradient method allows improved MLT vertical velocities and, for the first time, low‐latitude wind field parameters such as horizontal divergence and relative vorticity. The regularized wind field inversion method allows the estimation of spatial structure within the observed area and has the potential to outperform the gradient method, in particular when more detections are available or when fine adaptive tuning of the regularization factor is done. SIMONe Peru adds important information at low latitudes to currently scarce MLT continuous observing capabilities. Results contribute to studies of the MLT dynamics at different scales inherently connected to lower atmospheric forcing and E‐region dynamo related ionospheric variability

Multistatic Specular Meteor Radar Network in Peru: System Description and Initial Results

The mesosphere and lower thermosphere (MLT) region is dominated globally by dynamics at various scales: planetary waves, tides, gravity waves, and stratified turbulence. The latter two can coexist and be significant at horizontal scales less than 500 km, scales that are difficult to measure. This study presents a recently deployed multistatic specular meteor radar system, SIMONe Peru, which can be used to observe these scales. The radars are positioned at and around the Jicamarca Radio Observatory, which is located at the magnetic equator. Besides presenting preliminary results of typically reported large-scale features, like the dominant diurnal tide at low latitudes, we show results on selected days of spatially and temporally resolved winds obtained with two methods based on: (a) estimation of mean wind and their gradients (gradient method), and (b) an inverse theory with Tikhonov regularization (regularized wind field inversion method). The gradient method allows improved MLT vertical velocities and, for the first time, low-latitude wind field parameters such as horizontal divergence and relative vorticity. The regularized wind field inversion method allows the estimation of spatial structure within the observed area and has the potential to outperform the gradient method, in particular when more detections are available or when fine adaptive tuning of the regularization factor is done. SIMONe Peru adds important information at low latitudes to currently scarce MLT continuous observing capabilities. Results contribute to studies of the MLT dynamics at different scales inherently connected to lower atmospheric forcing and E-region dynamo related ionospheric variability

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Detection and Localisation Using Light

Visible light communication (VLC) systems have become promising candidates to complement conventional radio frequency (RF) systems due to the increasingly saturated RF spectrum and the potentially high data rates that can be achieved by VLC systems. Furthermore, people detection and counting in an indoor environment has become an emerging and attractive area in the past decade. Many techniques and systems have been developed for counting in public places such as subways, bus stations and supermarkets. The outcome of these techniques can be used for public security, resource allocation and marketing decisions. This thesis presents the first indoor light-based detection and localisation system that builds on concepts from radio detection and ranging (radar) making use of the expected growth in the use and adoption of visible light communication (VLC), which can provide the infrastructure for our light detection and localisation (LiDAL) system. Our system enables active detection, counting and localisation of people, in addition to being fully compatible with existing VLC systems. In order to detect human (targets), LiDAL uses the visible light spectrum. It sends pulses using a VLC transmitter and analyses the reflected signal collected by an optical receiver. Although we examine the use of the visible spectrum here, LiDAL can be used in the infrared spectrum and other parts of the light spectrum. We introduce LiDAL with different transmitter-receiver configurations and optimum detectors considering the fluctuation of the received reflected signal from the target in the presence of Gaussian noise. We design an efficient multiple input multiple output (MIMO) LiDAL system with wide field of view (FOV) single photodetector receiver, and also design a multiple input single output (MISO) LiDAL system with an imaging receiver to eliminate ambiguity in target detection and localisation. We develop models for the human body and its reflections and consider the impact of the colour and texture of the cloth used as well as the impact of target mobility. A number of detection and localisation methods are developed iii for our LiDAL system including cross correlation, a background subtraction method and a background estimation method. These methods are considered to distinguish a mobile target from the ambient reflections due to background obstacles (furniture) in a realistic indoor environment

Advanced Ground-Based Real and Synthetic Aperture Radar

Ground-based/terrestrial radar interferometry (GBRI) is a scientific topic of increasing interest in recent years. The GBRI is used in several field as remote sensing technique for monitoring natural environment (landslides, glacier, and mines) or infrastructures (bridges, towers). These sensors provide the displacement of targets by measuring the phase difference between sending and receiving radar signal. If the acquisition rate is enough the GBRI can provide the natural frequency, e.g. by calculating the Fourier transform of displacement. The research activity, presented in this work, concerns design and development of some advanced GBRI systems. These systems are related to the following issue: detection of displacement vector, Multiple Input Multiple Output (MIMO) and radars with 3D capability

Development and performance evaluation of a multistatic radar system

Multistatic radar systems are of emerging interest as they can exploit spatial diversity, enabling improved performance and new applications. Their development is being fuelled by advances in enabling technologies in such fields as communications and Digital Signal Processing (DSP). Such systems differ from typical modern active radar systems through consisting of multiple spatially diverse transmitter and receiver sites. Due to this spatial diversity, these systems present challenges in managing their operation as well as in usefully combining the multiple sources of information to give an output to the radar operator. In this work, a novel digital Commercial Off-The-Shelf (COTS) based coherent multistatic radar system designed at University College London, named ‘NetRad’, has been developed to produce some of the first published experimental results, investigating the challenges of operating such a system, and determining what level of performance might be achievable. Full detail of the various stages involved in the combination of data from the component transmitter-receiver pairs within a multistatic system is investigated, and many of the practical issues inherent are discussed. Simulation and subsequent experimental verification of several centralised and decentralised detection algorithms in terms of localisation (resolution and parameter estimation) of targets was undertaken. The computational cost of the DSP involved in multistatic data fusion is also considered. This gave a clear demonstration of several of the benefits of multistatic radar. Resolution of multiple targets that would have been unresolvable in a conventional monostatic system was shown. Targets were also shown to be plotted as two-dimensional vector position and velocities from use of time delay and Doppler shift information only. A range of targets were used including some such as walking people which were particularly challenging due to the variability of Radar Cross Section (RCS). Performance improvements were found to be dependant on the type of multistatic radar, method of data fusion and target characteristics in question. It is likely that future work will look to further explore the optimisation of multistatic radar for the various measures of performance identified and discussed in this work

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

MIMO Radar Waveform Design and Sparse Reconstruction for Extended Target Detection in Clutter

This dissertation explores the detection and false alarm rate performance of a novel transmit-waveform and receiver filter design algorithm as part of a larger Compressed Sensing (CS) based Multiple Input Multiple Output (MIMO) bistatic radar system amidst clutter. Transmit-waveforms and receiver filters were jointly designed using an algorithm that minimizes the mutual coherence of the combined transmit-waveform, target frequency response, and receiver filter matrix product as a design criterion. This work considered the Probability of Detection (P D) and Probability of False Alarm (P FA) curves relative to a detection threshold, τ th, Receiver Operating Characteristic (ROC), reconstruction error and mutual coherence measures for performance characterization of the design algorithm to detect both known and fluctuating targets and amidst realistic clutter and noise. Furthermore, this work paired the joint waveform-receiver filter design algorithm with multiple sparse reconstruction algorithms, including: Regularized Orthogonal Matching Pursuit (ROMP), Compressive Sampling Matching Pursuit (CoSaMP) and Complex Approximate Message Passing (CAMP) algorithms. It was found that the transmit-waveform and receiver filter design algorithm significantly outperforms statically designed, benchmark waveforms for the detection of both known and fluctuating extended targets across all tested sparse reconstruction algorithms. In particular, CoSaMP was specified to minimize the maximum allowable P FA of the CS radar system as compared to the baseline ROMP sparse reconstruction algorithm of previous work. However, while the designed waveforms do provide performance gains and CoSaMP affords a reduced peak false alarm rate as compared to the previous work, fluctuating target impulse responses and clutter severely hampered CS radar performance when either of these sparse reconstruction techniques were implemented. To improve detection rate and, by extension, ROC performance of the CS radar system under non-ideal conditions, this work implemented the CAMP sparse reconstruction algorithm in the CS radar system. It was found that detection rates vastly improve with the implementation of CAMP, especially in the case of fluctuating target impulse responses amidst clutter or at low receive signal to noise ratios (β n). Furthermore, where previous work considered a τ th=0, the implementation of a variable τ th in this work offered novel trade off between P D and P FA in radar design to the CS radar system. In the simulated radar scene it was found that τ th could be moderately increased retaining the same or similar P D while drastically improving P FA. This suggests that the selection and specification of the sparse reconstruction algorithm and corresponding τ th for this radar system is not trivial. Rather, a tradeoff was noted between P D and P FA based on the choice and parameters of the sparse reconstruction technique and detection threshold, highlighting an engineering trade-space in CS radar system design. Thus, in CS radar system design, the radar designer must carefully choose and specify the sparse reconstruction technique and appropriate detection threshold in addition to transmit-waveforms, receiver filters and building the dictionary of target impulse responses for detection in the radar scene