Jamming Effects on Hybrid Multistatic Radar Network Range and Velocity Estimation Errors

This research studies the effects of three noise jamming techniques on the performance of a hybrid multistatic radar network in a selection of different electronic warfare (EW) situations. The performance metrics investigated are the range and velocity estimation errors found using the Cramér-Rao lower bounds (CRLBs). The hybrid multistatic network simulated is comprised of a single active radar transmitter, three illuminators of opportunity (IO), a receiver co-located at the active transmitter site, and two separately located silent receivers. Each IO transmits at a unique frequency band commonly used for civilian applications, including Digital Video Broadcasting-Terrestrial (DVB-T), Digital Audio Broadcasting (DAB), and FM radio. Each receiver is capable of receiving signals at all three IO frequency bands as well as the operating frequency band of the active radar transmitter. The investigations included compare the performance of the network at detecting a single flying target under conditions where different combinations of jammer type, operating mode, directivity, and number of jammers operating are used. The performance degradation of the system compared to operation in a non-contested environment is determined and a comparison between the performance of the hybrid multistatic radar with that achievable by a monostatic radar and an active-only multistatic radar network within a selection of contested scenarios is made. Results show that the use of spatially distributed nodes and frequency diversity within the system enable greater theoretical functionality in the presence of jamming over conventional radar systems

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

Improvement of detection and tracking techniques in multistatic passive radar systems. (Mejora de técnicas de detección y seguimiento en sistemas radar pasivos multiestáticos)

Esta tesis doctoral es el resultado de una intensa actividad investigadora centrada en los sensores radar pasivos para la mejora de las capacidades de detección y seguimiento en escenarios complejos con blancos terrestres y pequeños drones. El trabajo de investigación se ha llevado a cabo en el grupo de investigación coordinado por la Dra. María Pilar Jarabo Amores, dentro del marco diferentes proyectos: IDEPAR (“Improved DEtection techniques for PAssive Radars”), MASTERSAT (“MultichAnnel paSsive radar receiver exploiting TERrestrial and SATellite Illuminators”) y KRIPTON (“A Knowledge based appRoach to passIve radar detection using wideband sPace adapTive prOcessiNg”) financiados por el Ministerio de Economía y Competitividad de España; MAPIS (Multichannel passive ISAR imaging for military applications) y JAMPAR (“JAMmer-based PAssive Radar”), financiados por la Agencia Europea de Defensa (EDA) . El objetivo principal es la mejora de las técnicas de detección y seguimiento en radares pasivos con configuraciones biestáticas y multiestaticas. En el documento se desarrollan algoritmos para el aprovechamiento de señales procedentes de distintos iluminadores de oportunidad (transmisores DVB-T, satélites DVB-S y señales GPS). Las soluciones propuestas han sido integradas en el demostrador tecnológico IDEPAR, desarrollado y actualizado bajo los proyectos mencionados, y validadas en escenarios reales declarados de interés por potenciales usuarios finales (Direccion general de armamento y material, instituto nacional de tecnología aeroespacial y la armada española). Para el desarrollo y evaluación de cadenas de las cadenas de procesado, se plantean dos casos de estudio: blancos terrestres en escenarios semiurbanos edificios y pequeños blancos aéreos en escenarios rurales y costeros. Las principales contribuciones se pueden resumir en los siguientes puntos: • Diseño de técnicas de seguimiento 2D en el espacio de trabajo rango biestático-frecuencia Doppler: se desarrollan técnicas de seguimiento para los dos casos de estudio, localización de blancos terrestres y pequeños drones. Para es último se implementan técnicas capaces de seguir tanto el movimiento del dron como su firma Doppler, lo que permite implementar técnicas de clasificación de blancos. • Diseño de técnicas de seguimiento de blancos capaces de integrar información en el espacio 3D (rango, Doppler y acimut): se diseñan técnicas basadas en procesado en dos etapas, una primera con seguimiento en 2D para el filtrado de falsas alarmas y la segunda para el seguimiento en 3D y la conversión de coordenadas a un plano local cartesiano. Se comparan soluciones basadas en filtros de Kalman para sistemas tanto lineales como no lineales. • Diseño de cadenas de procesado para sistemas multiestáticos: la información estimada del blanco sobre múltiples geometrías biestáticas es utilizada para incremento de las capacidades de localización del blanco en el plano cartesiano local. Se presentan soluciones basadas en filtros de Kalman para sistemas no lineales explotando diferentes medidas biestáticas en el proceso de transformación de coordenadas, analizando las mejoras de precisión en la localización del blanco. • Diseño de etapas de procesado para radares pasivos basados en señales satelitales de las constelaciones GPS DVB-S. Se estudian las características de las señales satelitales identificando sus inconvenientes y proponiendo cadenas de procesado que permitan su utilización para la detección y seguimiento de blancos terrestres. • Estudio del uso de señales DVB-T multicanal con gaps de transmisión entre los diferentes canales en sistemas radares pasivos. Con ello se incrementa la resolución del sistema, y las capacidades de detección, seguimiento y localización. Se estudia el modelo de señal multicanal, sus efectos sobre el procesado coherente y se proponen cadenas de procesado para paliar los efectos adversos de este tipo de señales

Wide-Angle Multistatic Synthetic Aperture Radar: Focused Image Formation and Aliasing Artifact Mitigation

Traditional monostatic Synthetic Aperture Radar (SAR) platforms force the user to choose between two image types: larger, low resolution images or smaller, high resolution images. Switching to a Wide-Angle Multistatic Synthetic Aperture Radar (WAM-SAR) approach allows formation of large high-resolution images. Unfortunately, WAM-SAR suffers from two significant implementation problems. First, wavefront curvature effects, non-linear flight paths, and warped ground planes lead to image defocusing with traditional SAR processing methods. A new 3-D monostatic/bistatic image formation routine solves the defocusing problem, correcting for all relevant wide-angle effects. Inverse SAR (ISAR) imagery from a Radar Cross Section (RCS) chamber validates this approach. The second implementation problem stems from the large Doppler spread in the wide-angle scene, leading to severe aliasing problems. This research effort develops a new anti-aliasing technique using randomized Stepped-Frequency (SF) waveforms to form Doppler filter nulls coinciding with aliasing artifact locations. Both simulation and laboratory results demonstrate effective performance, eliminating more than 99% of the aliased energy

On Detection and Ranking Methods for a Distributed Radio-Frequency Sensor Network: Theory and Algorithmic Implementation

A theoretical foundation for pre-detection fusion of sensors is needed if the United States Air Force is to ever field a system of distributed and layered sensors that can detect and perform parameter estimation of complex, extended targets in difficult interference environments, without human intervention, in near real-time. This research is relevant to the United States Air Force within its layered sensing and cognitive radar/sensor initiatives. The asymmetric threat of the twenty-first century introduces stressing sensing conditions that may exceed the ability of traditional monostatic sensing systems to perform their required intelligence, surveillance and reconnaissance missions. In particular, there is growing interest within the United States Air Force to move beyond single sensor sensing systems, and instead begin fielding and leveraging distributed sensing systems to overcome the inherent challenges imposed by the modern threat space. This thesis seeks to analyze the impact of integrating target echoes in the angular domain, to determine if better detection and ranking performance is achieved through the use of a distributed sensor network. Bespoke algorithms are introduced for detection and ranking ISR missions leveraging a distributed network of radio-frequency sensors: the first set of bespoke algorithms area based upon a depth-based nonparametric detection algorithm, which is to shown to enhance the recovery of targets under lower signal-to-noise ratios than an equivalent monostatic radar system; the second set of bespoke algorithms are based upon random matrix theoretic and concentration of measure mathematics, and demonstrated to outperform the depth-based nonparametric approach. This latter approach shall be shown to be effective across a broad range of signal-to-noise ratios, both positive and negative

Polarimetric passive radar. A practical approach to parametric adaptive detection

Polarimetric diversity has been recently shown to significantly improve the target detection performance in passive radar systems, if properly exploited to mitigate the competing interference. An adaptive processing scheme is presented in this work, leveraging the information conveyed using multi-polarized receiving antennas and modeling the disturbance as a multichannel autoregressive process. Despite this approach operates with a limited number of adaptive degrees of freedom, the long integration time exploited by passive radar typically requires a substantial computational cost and ad hoc expedients for its application. Therefore, a modified cost-effective implementation of the conceived solution is proposed in order to reduce the computational burden while controlling the resulting loss. The authors extensively demonstrate the effectiveness of the proposed solution against experimental data collected by a FM radio based passive coherent location system. The experimental results show that the proposed processing scheme yields a remarkable improvement with respect to both the conventional processing at the single polarimetric channel and the state-of-the-art strategies exploiting polarization diversity in PCL systems

Integrated perception, modeling, and control paradigm for bistatic sonar tracking by autonomous underwater vehicles

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 357-364).In this thesis, a fully autonomous and persistent bistatic anti-submarine warfare (ASW) surveillance solution is developed using the autonomous underwater vehicles (AUVs). The passive receivers are carried by these AUVs, and are physically separated from the cooperative active sources. These sources are assumed to be transmitting both the frequency-modulated (FM) and continuous wave (CW) sonar pulse signals. The thesis then focuses on providing novel methods for the AUVs/receivers to enhance the bistatic sonar tracking performance. Firstly, the surveillance procedure, called the Automated Perception, is developed to automatically abstract the sensed acoustical data from the passive receiver to the track report that represents the situation awareness. The procedure is executed sequentially by two algorithms: (i) the Sonar Signal Processing algorithm - built with a new dual-waveform fusion of the FM and CW signals to achieve reliable stream of contacts for improved tracking; and (ii) the Target Tracking algorithm - implemented by exploiting information and environmental adaptations to optimize tracking performance. Next, a vehicular control strategy, called the Perception-Driven Control, is devised to move the AUV in reaction to the track report provided by the Automated Perception. The thesis develops a new non-myopic and adaptive control for the vehicle. This is achieved by exploiting the predictive information and environmental rewards to optimize the future tracking performance. The formulation eventually leads to a new information-theoretic and environmental-based control. The main challenge of the surveillance solution then rests upon formulating a model that allows tracking performance to be enhanced via adaptive processing in the Automated Perception, and adaptive mobility by the Perception-Driven Control. A Unified Model is formulated in this thesis that amalgamates two models: (i) the Information-Theoretic Model - developed to define the manner at which the FM and CW acoustical, the navigational, and the environmental measurement uncertainties are propagated to the bistatic measurement uncertainties in the contacts; and (ii) the Environmental-Acoustic Model - built to predict the signal-to-noise power ratios (SNRs) of the FM and CW contacts. Explicit relationships are derived in this thesis using information theory to amalgamate these two models. Finally, an Integrated System is developed onboard each AUV that brings together all the above technologies to enhance the bistatic sonar tracking performance. The system is formulated as a closed-loop control system. This formulation provides a new Integrated Perception, Modeling, and Control Paradigm for an autonomous bistatic ASW surveillance solution using AUVs. The system is validated using the simulated data, and the real data collected from the Generic Littoral Interoperable Network Technology (GLINT) 2009 and 2010 experiments. The experiments were conducted jointly with the NATO Undersea Research Centre (NURC).by Raymond Hon Kit Lum.Sc.D

Multiple-Target Tracking in Complex Scenarios

In this dissertation, we develop computationally efficient algorithms for multiple-target tracking: MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm: and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter: MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound: PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter: HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection: SS), resource allocation: RA) and data fusion: DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently

Evaluation and Analysis of Array Antennas for Passive Coherent Location (PCL) Systems

Passive Coherent Location (PCL) systems use a special form of a radar receiver that exploits the ambient radiation in the environment to detect and track targets. Typical transmissions of opportunity that might be exploited include television and FM radiobroadcasts. PCL implies the use of a non-radar electromagnetic sources of illumination, such as commercial radio or television broadcasts also referred as transmitters of opportunity. The use of such illumination sources means that the receiver needs to process waveforms that are not designed for radar purposes. As a consequence, the receivers for PCL systems must be much more customized than traditional receivers, in order to obtain the most appropriate and best signal. Since antennas are the eyes of the receivers, processing of an incoming signal starts with the antennas. Yet, because PCL system is non-traditional, there has not been much work done in the evaluation of the antennas, even though PCL systems have some demanding constraints on the antenna system. During this research various array antenna designs will be studied by their radiation patterns, gain factors, input impedances, power efficiencies and other features by simulating these arrays in the computer environment. The goal is to show the better performance of the array antennas compared to traditional Yagi-Uda antennas that are currently used for PCL systems