A comparison of processing approaches for distributed radar sensing

Radar networks received increasing attention in recent years as they can outperform single monostatic or bistatic systems. Further attention is being dedicated to these systems as an application of the MIMO concept, well know in communications for increasing the capacity of the channel and improving the overall quality of the connection. However, it is here shown that radar network can take advantage not only from the angular diversity in observing the target, but also from a variety of ways of processing the received signals. The number of devices comprising the network has also been taken into the analysis. Detection and false alarm are evaluated in noise only and clutter from a theoretical and simulated point of view. Particular attention is dedicated to the statistics behind the processing. Experiments have been performed to evaluate practical applications of the proposed processing approaches and to validate assumptions made in the theoretical analysis. In particular, the radar network used for gathering real data is made up of two transmitters and three receivers. More than two transmitters are well known to generate mutual interference and therefore require additional e�fforts to mitigate the system self-interference. However, this allowed studying aspects of multistatic clutter, such as correlation, which represent a first and novel insight in this topic. Moreover, two approaches for localizing targets have been developed. Whilst the first is a graphic approach, the second is hybrid numerical (partially decentralized, partially centralized) which is clearly shown to improve dramatically the single radar accuracy. Finally the e�ects of exchanging angular with frequency diversity are shown as well in some particular cases. This led to develop the Frequency MIMO and the Frequency Diverse Array, according to the separation of two consecutive frequencies. The latter is a brand new topic in technical literature, which is attracting the interest of the technical community because of its potential to generate range-dependant patterns. Both the latter systems can be used in radar-designing to improve the agility and the effciency of the radar

Passive MIMO Radar Detection

Passive multiple-input multiple-output (MIMO) radar is a sensor network comprised of multiple distributed receivers that detects and localizes targets using the emissions from multiple non-cooperative radio frequency transmitters. This dissertation advances the theory of centralized passive MIMO radar (PMR) detection by proposing two novel generalized likelihood ratio test (GLRT) detectors. The first addresses detection in PMR networks without direct-path signals. The second addresses detection in PMR networks with direct-path signals. The probability distributions of both test statistics are investigated using recent results from random matrix theory. Equivalence is established between PMR networks without direct-path signals and passive source localization (PSL) networks. Comparison of both detectors with a centralized GLRT for active MIMO radar (AMR) detection reveals that PMR may be interpreted as the link between AMR and PSL sensor networks. In particular, under high direct-path-to-noise ratio (DNR) conditions, PMR sensitivity and ambiguity approaches that of AMR. Under low-DNR conditions, PMR sensitivity and ambiguity approaches that of PSL. At intermediate DNRs, PMR sensitivity and ambiguity smoothly varies between that of AMR and PSL. In this way, PMR unifies PSL and AMR within a common theoretical framework. This result provides insight into the fundamental natures of active and passive distributed sensing

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

Single data set detection for multistatic doppler radar

The aim of this thesis is to develop and analyse single data set (SDS) detection algorithms that can utilise the advantages of widely-spaced (statistical) multiple-input multiple-output (MIMO) radar to increase their accuracy and performance. The algorithms make use of the observations obtained from multiple space-time adaptive processing (STAP) receivers and focus on covariance estimation and inversion to perform target detection. One of the main interferers for a Doppler radar has always been the radar’s own signal being reflected off the surroundings. The reflections of the transmitted waveforms from the ground and other stationary or slowly-moving objects in the background generate observations that can potentially raise false alarms. This creates the problem of searching for a target in both additive white Gaussian noise (AWGN) and highly-correlated (coloured) interference. Traditional STAP deals with the problem by using target-free training data to study this environment and build its characteristic covariance matrix. The data usually comes from range gates neighbouring the cell under test (CUT). In non-homogeneous or non-stationary environments, however, this training data may not reflect the statistics of the CUT accurately, which justifies the need to develop SDS methods for radar detection. The maximum likelihood estimation detector (MLED) and the generalised maximum likelihood estimation detector (GMLED) are two reduced-rank STAP algorithms that eliminate the need for training data when mapping the statistics of the background interference. The work in this thesis is largely based on these two algorithms. The first work derives the optimal maximum likelihood (ML) solution to the target detection problem when the MLED and GMLED are used in a multistatic radar scenario. This application assumes that the spatio-temporal Doppler frequencies produces in the individual bistatic STAP pairs of the MIMO system are ideally synchronised. Therefore the focus is on providing the multistatic outcome to the target detection problem. It is shown that the derived MIMO detectors possess the desirable constant false alarm rate (CFAR) property. Gaussian approximations to the statistics of the multistatic MLED and GMLED are derived in order to provide a more in-depth analysis of the algorithms. The viability of the theoretical models and their approximations are tested against a numerical simulation of the systems. The second work focuses on the synchronisation of the spatio-temporal Doppler frequency data from the individual bistatic STAP pairs in the multistatic MLED scenario. It expands the idea to a form that could be implemented in a practical radar scenario. To reduce the information shared between the bistatic STAP channels, a data compression method is proposed that extracts the significant contributions of the MLED likelihood function before transmission. To perform the inter-channel synchronisation, the Doppler frequency data is projected into the space of potential target velocities where the multistatic likelihood is formed. Based on the expected structure of the velocity likelihood in the presence of a target, a modification to the multistatic MLED is proposed. It is demonstrated through numerical simulations that the proposed modified algorithm performs better than the basic multistatic MLED while having the benefit of reducing the data exchange in the MIMO radar system

Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet

Robust Multi-target Tracking with Bootstrapped-GLMB Filter

This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters

Target Detection Architecture for Resource Constrained Wireless Sensor Networks within Internet of Things

Wireless sensor networks (WSN) within Internet of Things (IoT) have the potential to address the growing detection and classi�cation requirements among many surveillance applications. RF sensing techniques are the next generation technologies which o�er distinct advantages over traditional passive means of sensing such as acoustic and seismic which are used for surveillance and target detection applications of WSN. RF sensing based WSN within IoT detect the presence of designated targets by transmitting RF signals into the sensing environment and observing the re ected echoes. In this thesis, an RF sensing based target detection architecture for surveillance applications of WSN has been proposed to detect the presence of stationary targets within the sensing environment. With multiple sensing nodes operating simultaneously within the sensing region, diversity among the sensing nodes in the choice of transmit waveforms is required. Existing multiple access techniques to accommodate multiple sensing nodes within the sensing environment are not suitable for RF sensing based WSN. In this thesis, a diversity in the choice of the transmit waveforms has been proposed and transmit waveforms which are suitable for RF sensing based WSN have been discussed. A criterion have been de�ned to quantify the ease of detecting the signal and energy e�ciency of the signal based on which ease of detection index and energy e�ciency index respectively have been generated. The waveform selection criterion proposed in this thesis takes the WSN sensing conditions into account and identi�es the optimum transmit waveform within the available choices of transmit waveforms based on their respective ease of detection and energy e�ciency indexes. A target detector analyses the received RF signals to make a decision regarding the existence or absence of targets within the sensing region. Existing target detectors which are discussed in the context of WSN do not take the factors such as interference and nature of the sensing environment into account. Depending on the nature of the sensing environment, in this thesis the sensing environments are classi�ed as homogeneous and heterogeneous sensing environments. Within homogeneous sensing environments the presence of interference from the neighbouring sensing nodes is assumed. A target detector has been proposed for WSN within homogeneous sensing environments which can reliably detect the presence of targets. Within heterogeneous sensing environments the presence of clutter and interfering waveforms is assumed. A target detector has been proposed for WSN within heterogeneous sensing environments to detect targets in the presence of clutter and interfering waveforms. A clutter estimation technique has been proposed to assist the proposed target detector to achieve increased target detection reliability in the presence of clutter. A combination of compressive and two-step target detection architectures has been proposed to reduce the transmission costs. Finally, a 2-stage target detection architecture has been proposed to reduce the computational complexity of the proposed target detection architecture

Adaptive waveform design for SAR in a crowded spectrum

This thesis concerns the development of an adaptive waveform design scheme for synthetic aperture radar (SAR) to support its operation in the increasingly crowded radio frequency (RF) spectrum, focusing on mitigating the effects of external RF interference. The RF spectrum is a finite resource and the rapid expansion of the telecommunications industry has seen radar users face a significant restriction in the range of available operational frequencies. This crowded spectrum scenario leads to increased likelihood of RF interference either due to energy leakage from neighbouring spectral users or from unlicensed transmitters. SAR is a wide bandwidth radar imaging mode which exploits the motion of the radar platform to form an image using multiple one dimensional profiles of the scene of interest known as the range profile. Due to its wideband nature, SAR is particularly vulnerable to RF interference which causes image impairments and overall reduction in quality. Altering the approach for radar energy transmission across the RF spectrum is now imperative to continue effective operation. Adaptive waveforms have recently become feasible for implementation and offer the much needed flexibility in the choice and control over radar transmission. However, there is a critically small processing time frame between waveform reception and transmission, which necessitates the use of computationally efficient processing algorithms to use adaptivity effectively. This simulation-based study provides a first look at adaptive waveform design for SAR to mitigate the detrimental effects of RF interference on a pulse-to-pulse basis. Standard SAR systems rely on a fixed waveform processing format on reception which restricts its potential to reap the benefits of adaptive waveform design. Firstly, to support waveform design for SAR, system identification techniques are applied to construct an alternative receive processing method which allows flexibility in waveform type. This leads to the main contribution of the thesis which is the formation of an adaptive spectral waveform design scheme. A computationally efficient closed-form expression for the waveform spectrum that minimizes the error in the estimate of the SAR range profile on a pulse to pulse basis is derived. The range profile and the spectrum of the interference are estimated at each pulse. The interference estimate is then used to redesign the proceeding waveform for estimation of the range profile at the next radar platform position. The solution necessitates that the energy is spread across the spectrum such that it competes with the interferer. The scenario where the waveform admits gaps in the spectrum in order to mitigate the effects of the interference is also detailed and is the secondary major thesis contribution. A series of test SAR images demonstrate the efficacy of these techniques and yield reduced interference effects compared to the standard SAR waveform

Optimization methods for active and passive localization

Active and passive localization employing widely distributed sensors is a problem of interest in various fields. In active localization, such as in MIMO radar, transmitters emit signals that are reflected by the targets and collected by the receive sensors, whereas, in passive localization the sensors collect the signals emitted by the sources themselves. This dissertation studies optimization methods for high precision active and passive localization. In the case of active localization, multiple transmit elements illuminate the targets from different directions. The signals emitted by the transmitters may differ in power and bandwidth. Such resources are often limited and distributed uniformly among the transmitters. However, previous studies based on the well known Cramer-Rao lower bound have shown that the localization accuracy depends on the locations of the transmitters as well as the individual channel gains between different transmitters, targets and receivers. Thus, it is natural to ask whether localization accuracy may be improved by judiciously allocating such limited resources among the transmitters. Using the Cr´amer-Rao lower bound for target localization of multiple targets as a figure of merit, approximate solutions are proposed to the problems of optimal power, optimal bandwidth and optimal joint power and bandwidth allocation. These solutions are computed by minimizing a sequence of convex problems. The quality of these solutions is assessed through extensive numerical simulations and with the help of a lower-bound that certifies their optimality. Simulation results reveal that bandwidth allocation policies have a stronger impact on performance than power. Passive localization of radio frequency sources over multipath channels is a difficult problem arising in applications such as outdoor or indoor geolocation. Common approaches that combine ad-hoc methods for multipath mitigation with indirect localization relying on intermediary parameters such as time-of-arrivals, time difference of arrivals or received signal strengths, are unsatisfactory. This dissertation models the localization of known waveforms over unknown multipath channels in a sparse framework, and develops a direct approach in which multiple sources are localized jointly, directly from observations obtained at distributed sources. The proposed approach exploits channel properties that enable to distinguish line-of-sight (LOS) from non-LOS signal paths. Theoretical guarantees are established for correct recovery of the sources’ locations by atomic norm minimization. A second-order-cone-based algorithm is developed to produce the optimal atomic decomposition, and it is shown to produce high accuracy location estimates over complex scenes, in which sources are subject to diverse multipath conditions, including lack of LOS