Neural Networks for improved signal source enumeration and localization with unsteered antenna arrays

Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process

Users guide Advanced Reactive Electronic Simulation (ARES) version 1.12

This Users Guide is for the Advanced Reactive Electronic Warfare Simulation (ARES) Version 1.12, created at the Naval Research Laboratory (NRL) under a project sponsored by the Office of Naval Research (ONR) titled Distributed and Networked C2W Technology (FY98-FYOO). The simulation is used to determine the optimum distributed C2W/EA configuration of assets including placement of sensors and system selection (jammer or receiver or both) for important mission scenarios leading to a better understanding of the minimum requirements for suppression of enemy air defense operations. ARES is a pulse level simulation that models the complex interaction of multiple radar systems being acted upon by multiple AEA aircraft, considering target aircraft radar cross section (RCS) and altitude, terrain masking effects, both standoff jamming and self protection jamming effects, and network connection effect. Its features include an object%-oriented scenario workbook allowing the users to build a battlefield scenario and a search procedure based on a genetic algorithm (GA) for optimizing configurations of what the core and peripheral components of the AEA architecture should be. ARES is available in two forms: Graphical User Interface (GUI) and parallel. ARES' GUI runs on a personal computer (PC) and its primary application is for setting up scenarios and post processing. ARES' parallel version runs over a cluster of Intel based Linux machines with Message- Passing Interface (MPI) and provides capability to execute multipleThis report was sponsored by the Applied Physics Laboratory, Johns Hopkins University

URANUS: Radio Frequency Tracking, Classification and Identification of Unmanned Aircraft Vehicles

Safety and security issues for Critical Infrastructures are growing as attackers adopt drones as an attack vector flying in sensitive airspaces, such as airports, military bases, city centers, and crowded places. Despite the use of UAVs for logistics, shipping recreation activities, and commercial applications, their usage poses severe concerns to operators due to the violations and the invasions of the restricted airspaces. A cost-effective and real-time framework is needed to detect the presence of drones in such cases. In this contribution, we propose an efficient radio frequency-based detection framework called URANUS. We leverage real-time data provided by the Radio Frequency/Direction Finding system, and radars in order to detect, classify and identify drones (multi-copter and fixed-wings) invading no-drone zones. We adopt a Multilayer Perceptron neural network to identify and classify UAVs in real-time, with $90$% accuracy. For the tracking task, we use a Random Forest model to predict the position of a drone with an MSE $\approx0.29$, MAE $\approx0.04$, and $R^2\approx 0.93$. Furthermore, coordinate regression is performed using Universal Transverse Mercator coordinates to ensure high accuracy. Our analysis shows that URANUS is an ideal framework for identifying, classifying, and tracking UAVs that most Critical Infrastructure operators can adopt

The application of digital techniques to an automatic radar track extraction system

'Modern' radar systems have come in for much criticism in recent years, particularly in the aftermath of the Falklands campaign. There have also been notable failures in commercial designs, including the well-publicised 'Nimrod' project which was abandoned due to persistent inability to meet signal processing requirements. There is clearly a need for improvement in radar signal processing techniques as many designs rely on technology dating from the late 1970's, much of which is obsolete by today’s standards. The Durham Radar Automatic Track Extraction System (RATES) is a practical implementation of current microprocessor technology, applied to plot extraction of surveillance radar data. In addition to suggestions for the design of such a system, results are quoted for the predicted performance when compared with a similar product using 1970's design methodology. Suggestions are given for the use of other VLSI techniques in plot extraction, including logic arrays and digital signal processors. In conclusion, there is an illustrated discussion concerning the use of systolic arrays in RATES and a prediction that this will represent the optimum architecture for future high-speed radar signal processors

Radar tracking system development

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaf 65).The Airborne Seeker Test Bed (ASTB) is an airborne sensor testing platform operated by the Tactical Defense Systems group at MIT Lincoln Laboratory. The Instrumentation Head (IH) is a primary sensor on the ASTB. It is a passive X-band radar receiver located on the nose of the plane. The IH serves as a truth sensor for other RF systems on the test bed and is controlled by an onboard tracking system, the Seeker Computer. The Seeker Computer processes IH data in real-time to track targets in Doppler, angle, and range. From these tracks it then produces angle-error feedback signals that command the IH gimbals, keeping targets centered along the antenna boresight. Over three years, a new Seeker Computer was built to replace an old system constrained by obsolete hardware. The redevelopment project was a team effort and this thesis presents a systems-level analysis of the design process, the new Seeker Computer system, and the related team and individual contributions to software and digital signal processing research that took place during development.by Yue Hann Chin.M.Eng

Radar Signal Processing for Interference Mitigation

It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter. For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems. The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically

De-interleaving of Radar Pulses for EW Receivers with an ELINT Application

De-interleaving is a critical function in Electronic Warfare (EW) that has not received much attention in the literature regarding on-line Electronic Intelligence (ELINT) application. In ELINT, on-line analysis is important in order to allow for efficient data collection and for support of operational decisions. This dissertation proposed a de-interleaving solution for use with ELINT/Electronic-Support-Measures (ESM) receivers for purposes of ELINT with on-line application. The proposed solution does not require complex integration with existing EW systems or modifications to their sub-systems. Before proposing the solution, on-line de-interleaving algorithms were surveyed. Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm that has not been used before in de-interleaving; in this dissertation, it has proved to be effective. DBSCAN was thus selected as a component of the proposed de-interleaving solution due to its advantages over other surveyed algorithms. The proposed solution relies primarily on the parameters of Angle of Arrival (AOA), Radio Frequency (RF), and Time of Arrival (TOA). The time parameter was utilized in resolving RF agility. The solution is a system that is composed of different building blocks. The solution handles complex radar environments that include agility in RF, Pulse Width (PW), and Pulse Repetition Interval (PRI)