Sensory Communication

Contains table of contents for Section 2 and reports on five research projects.National Institutes of Health Contract 2 R01 DC00117National Institutes of Health Contract 1 R01 DC02032National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Contract N01 DC22402National Institutes of Health Grant R01-DC001001National Institutes of Health Grant R01-DC00270National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Air Warfare Center Training Systems Division Contract N61339-94-C-0087U.S. Navy - Naval Air Warfare Center Training System Division Contract N61339-93-C-0055U.S. Navy - Office of Naval Research Grant N00014-93-1-1198National Aeronautics and Space Administration/Ames Research Center Grant NCC 2-77

Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

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

Autonomous, Collaborative, Unmanned Aerial Vehicles for Search and Rescue

Search and Rescue is a vitally important subject, and one which can be improved through the use of modern technology. This work presents a number of advances aimed towards the creation of a swarm of autonomous, collaborative, unmanned aerial vehicles for land-based search and rescue. The main advances are the development of a diffusion based search strategy for route planning, research into GPS (including the Durham Tracker Project and statistical research into altitude errors), and the creation of a relative positioning system (including discussion of the errors caused by fast-moving units). Overviews are also given of the current state of research into both UAVs and Search and Rescue

COMBAT SYSTEMS Volume 1. Sensor Elements Part I. Sensor Functional Characteristics

This document includes: CHAPTER 1. SIGNATURES, OBSERVABLES, & PROPAGATORS. CHAPTER 2. PROPAGATION OF ELECTROMAGNETIC RADIATION. I. – FUNDAMENTAL EFFECTS. CHAPTER 3. PROPAGATION OF ELECTROMAGNETIC RADIATION. II. – WEATHER EFFECTS. CHAPTER 4. PROPAGATION OF ELECTROMAGNETIC RADIATION. III. – REFRACTIVE EFFECTS. CHAPTER 5. PROPAGATION OF ELECTROMAGNETIC RADIATION IV. – OTHER ATMOSPHERIC AND UNDERWATER EFFECTS. CHAPTER 6. PROPAGATION OF ACOUSTIC RADIATION. CHAPTER 7. NUCLEAR RADIATION: ITS ORIGIN AND PROPAGATION. CHAPTER 8. RADIOMETRY, PHOTOMETRY, & RADIOMETRIC ANALYSIS. CHAPTER 9. SENSOR FUNCTIONS. CHAPTER 10. SEARCH. CHAPTER 11. DETECTION. CHAPTER 12. ESTIMATION. CHAPTER 13. MODULATION AND DEMODULATION. CHAPTER 14. IMAGING AND IMAGE-BASED PERCEPTION. CHAPTER 15. TRACKING. APPENDIX A. UNITS, PHYSICAL CONSTANTS, AND USEFUL CONVERSION FACTORS. APPENDIX B. FINITE DIFFERENCE AND FINITE ELEMENT TECHNIQUES. APPENDIX C. PROBABILITY AND STATISTICS. INDEX TO VOLUME 1. Note by author: Note: Boldface entries in the table of contents are not yet completed

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

Radar-based Application of Pedestrian and Cyclist Micro-Doppler Signatures for Automotive Safety Systems

Die sensorbasierte Erfassung des Nahfeldes im Kontext des hochautomatisierten Fahrens erfährt einen spürbaren Trend bei der Integration von Radarsensorik. Fortschritte in der Mikroelektronik erlauben den Einsatz von hochauflösenden Radarsensoren, die durch effiziente Verfahren sowohl im Winkel als auch in der Entfernung und im Doppler die Messgenauigkeit kontinuierlich ansteigen lassen. Dadurch ergeben sich neuartige Möglichkeiten bei der Bestimmung der geometrischen und kinematischen Beschaffenheit ausgedehnter Ziele im Fahrzeugumfeld, die zur gezielten Entwicklung von automotiven Sicherheitssystemen herangezogen werden können. Im Rahmen dieser Arbeit werden ungeschützte Verkehrsteilnehmer wie Fußgänger und Radfahrer mittels eines hochauflösenden Automotive-Radars analysiert. Dabei steht die Erscheinung des Mikro-Doppler-Effekts, hervorgerufen durch das hohe Maß an kinematischen Freiheitsgraden der Objekte, im Vordergrund der Betrachtung. Die durch den Mikro-Doppler-Effekt entstehenden charakteristischen Radar-Signaturen erlauben eine detailliertere Perzeption der Objekte und können in direkten Zusammenhang zu ihren aktuellen Bewegungszuständen gesetzt werden. Es werden neuartige Methoden vorgestellt, die die geometrischen und kinematischen Ausdehnungen der Objekte berücksichtigen und echtzeitfähige Ansätze zur Klassifikation und Verhaltensindikation realisieren. Wird ein ausgedehntes Ziel (z.B. Radfahrer) von einem Radarsensor detektiert, können aus dessen Mikro-Doppler-Signatur wesentliche Eigenschaften bezüglich seines Bewegungszustandes innerhalb eines Messzyklus erfasst werden. Die Geschwindigkeitsverteilungen der sich drehenden Räder erlauben eine adaptive Eingrenzung der Tretbewegung, deren Verhalten essentielle Merkmale im Hinblick auf eine vorausschauende Unfallprädiktion aufweist. Ferner unterliegen ausgedehnte Radarziele einer Orientierungsabhängigkeit, die deren geometrischen und kinematischen Profile direkt beeinflusst. Dies kann sich sowohl negativ auf die Klassifikations-Performance als auch auf die Verwertbarkeit von Parametern auswirken, die eine Absichtsbekundung des Radarziels konstituieren. Am Beispiel des Radfahrers wird hierzu ein Verfahren vorgestellt, das die orientierungsabhängigen Parameter in Entfernung und Doppler normalisiert und die gemessenen Mehrdeutigkeiten kompensiert. Ferner wird in dieser Arbeit eine Methodik vorgestellt, die auf Grundlage des Mikro- Doppler-Profils eines Fußgängers dessen Beinbewegungen über die Zeit schätzt (Tracking) und wertvolle Objektinformationen hinsichtlich seines Bewegungsverhaltens offenbart. Dazu wird ein Bewegungsmodell entwickelt, das die nichtlineare Fortbewegung des Beins approximiert und dessen hohes Maß an biomechanischer Variabilität abbildet. Durch die Einbeziehung einer wahrscheinlichkeitsbasierten Datenassoziation werden die Radar-Detektionen ihren jeweils hervorrufenden Quellen (linkes und rechtes Bein) zugeordnet und eine Trennung der Gliedmaßen realisiert. Im Gegensatz zu bisherigen Tracking-Verfahren weist die vorgestellte Methodik eine Steigerung in der Genauigkeit der Objektinformationen auf und stellt damit einen entscheidenden Vorteil für zukünftige Fahrerassistenzsysteme dar, um deutlich schneller auf kritische Verkehrssituationen reagieren zu können.:1 Introduction 1 1.1 Automotive environmental perception 2 1.2 Contributions of this work 4 1.3 Thesis overview 6 2 Automotive radar 9 2.1 Physical fundamentals 9 2.1.1 Radar cross section 9 2.1.2 Radar equation 10 2.1.3 Micro-Doppler effect 11 2.2 Radar measurement model 15 2.2.1 FMCW radar 15 2.2.2 Chirp sequence modulation 17 2.2.3 Direction-of-arrival estimation 22 2.3 Signal processing 25 2.3.1 Target properties 26 2.3.2 Target extraction 28 Power detection 28 Clustering 30 2.3.3 Real radar data example 31 2.4 Conclusion 33 3 Micro-Doppler applications of a cyclist 35 3.1 Physical fundamentals 35 3.1.1 Micro-Doppler signatures of a cyclist 35 3.1.2 Orientation dependence 36 3.2 Cyclist feature extraction 38 3.2.1 Adaptive pedaling extraction 38 Ellipticity constraints 38 Ellipse fitting algorithm 39 3.2.2 Experimental results 42 3.3 Normalization of the orientation dependence 44 3.3.1 Geometric correction 44 3.3.2 Kinematic correction 45 3.3.3 Experimental results 45 3.4 Conclusion 47 3.5 Discussion and outlook 47 4 Micro-Doppler applications of a pedestrian 49 4.1 Pedestrian detection 49 4.1.1 Human kinematics 49 4.1.2 Micro-Doppler signatures of a pedestrian 51 4.1.3 Experimental results 52 Radially moving pedestrian 52 Crossing pedestrian 54 4.2 Pedestrian feature extraction 57 4.2.1 Frequency-based limb separation 58 4.2.2 Extraction of body parts 60 4.2.3 Experimental results 62 4.3 Pedestrian tracking 64 4.3.1 Probabilistic state estimation 65 4.3.2 Gaussian filters 67 4.3.3 The Kalman filter 67 4.3.4 The extended Kalman filter 69 4.3.5 Multiple-object tracking 71 4.3.6 Data association 74 4.3.7 Joint probabilistic data association 80 4.4 Kinematic-based pedestrian tracking 84 4.4.1 Kinematic modeling 84 4.4.2 Tracking motion model 87 4.4.3 4-D radar point cloud 91 4.4.4 Tracking implementation 92 4.4.5 Experimental results 96 Longitudinal trajectory 96 Crossing trajectory with sudden turn 98 4.5 Conclusion 102 4.6 Discussion and outlook 103 5 Summary and outlook 105 5.1 Developed algorithms 105 5.1.1 Adaptive pedaling extraction 105 5.1.2 Normalization of the orientation dependence 105 5.1.3 Model-based pedestrian tracking 106 5.2 Outlook 106 Bibliography 109 List of Acronyms 119 List of Figures 124 List of Tables 125 Appendix 127 A Derivation of the rotation matrix 2.26 127 B Derivation of the mixed radar signal 2.52 129 C Calculation of the marginal association probabilities 4.51 131 Curriculum Vitae 135Sensor-based detection of the near field in the context of highly automated driving is experiencing a noticeable trend in the integration of radar sensor technology. Advances in microelectronics allow the use of high-resolution radar sensors that continuously increase measurement accuracy through efficient processes in angle as well as distance and Doppler. This opens up novel possibilities in determining the geometric and kinematic nature of extended targets in the vehicle environment, which can be used for the specific development of automotive safety systems. In this work, vulnerable road users such as pedestrians and cyclists are analyzed using a high-resolution automotive radar. The focus is on the appearance of the micro-Doppler effect, caused by the objects’ high kinematic degree of freedom. The characteristic radar signatures produced by the micro-Doppler effect allow a clearer perception of the objects and can be directly related to their current state of motion. Novel methods are presented that consider the geometric and kinematic extents of the objects and realize real-time approaches to classification and behavioral indication. When a radar sensor detects an extended target (e.g., bicyclist), its motion state’s fundamental properties can be captured from its micro-Doppler signature within a measurement cycle. The spinning wheels’ velocity distributions allow an adaptive containment of the pedaling motion, whose behavior exhibits essential characteristics concerning predictive accident prediction. Furthermore, extended radar targets are subject to orientation dependence, directly affecting their geometric and kinematic profiles. This can negatively affect both the classification performance and the usability of parameters constituting the radar target’s intention statement. For this purpose, using the cyclist as an example, a method is presented that normalizes the orientation-dependent parameters in range and Doppler and compensates for the measured ambiguities. Furthermore, this paper presents a methodology that estimates a pedestrian’s leg motion over time (tracking) based on the pedestrian’s micro-Doppler profile and reveals valuable object information regarding his motion behavior. To this end, a motion model is developed that approximates the leg’s nonlinear locomotion and represents its high degree of biomechanical variability. By incorporating likelihood-based data association, radar detections are assigned to their respective evoking sources (left and right leg), and limb separation is realized. In contrast to previous tracking methods, the presented methodology shows an increase in the object information’s accuracy. It thus represents a decisive advantage for future driver assistance systems in order to be able to react significantly faster to critical traffic situations.:1 Introduction 1 1.1 Automotive environmental perception 2 1.2 Contributions of this work 4 1.3 Thesis overview 6 2 Automotive radar 9 2.1 Physical fundamentals 9 2.1.1 Radar cross section 9 2.1.2 Radar equation 10 2.1.3 Micro-Doppler effect 11 2.2 Radar measurement model 15 2.2.1 FMCW radar 15 2.2.2 Chirp sequence modulation 17 2.2.3 Direction-of-arrival estimation 22 2.3 Signal processing 25 2.3.1 Target properties 26 2.3.2 Target extraction 28 Power detection 28 Clustering 30 2.3.3 Real radar data example 31 2.4 Conclusion 33 3 Micro-Doppler applications of a cyclist 35 3.1 Physical fundamentals 35 3.1.1 Micro-Doppler signatures of a cyclist 35 3.1.2 Orientation dependence 36 3.2 Cyclist feature extraction 38 3.2.1 Adaptive pedaling extraction 38 Ellipticity constraints 38 Ellipse fitting algorithm 39 3.2.2 Experimental results 42 3.3 Normalization of the orientation dependence 44 3.3.1 Geometric correction 44 3.3.2 Kinematic correction 45 3.3.3 Experimental results 45 3.4 Conclusion 47 3.5 Discussion and outlook 47 4 Micro-Doppler applications of a pedestrian 49 4.1 Pedestrian detection 49 4.1.1 Human kinematics 49 4.1.2 Micro-Doppler signatures of a pedestrian 51 4.1.3 Experimental results 52 Radially moving pedestrian 52 Crossing pedestrian 54 4.2 Pedestrian feature extraction 57 4.2.1 Frequency-based limb separation 58 4.2.2 Extraction of body parts 60 4.2.3 Experimental results 62 4.3 Pedestrian tracking 64 4.3.1 Probabilistic state estimation 65 4.3.2 Gaussian filters 67 4.3.3 The Kalman filter 67 4.3.4 The extended Kalman filter 69 4.3.5 Multiple-object tracking 71 4.3.6 Data association 74 4.3.7 Joint probabilistic data association 80 4.4 Kinematic-based pedestrian tracking 84 4.4.1 Kinematic modeling 84 4.4.2 Tracking motion model 87 4.4.3 4-D radar point cloud 91 4.4.4 Tracking implementation 92 4.4.5 Experimental results 96 Longitudinal trajectory 96 Crossing trajectory with sudden turn 98 4.5 Conclusion 102 4.6 Discussion and outlook 103 5 Summary and outlook 105 5.1 Developed algorithms 105 5.1.1 Adaptive pedaling extraction 105 5.1.2 Normalization of the orientation dependence 105 5.1.3 Model-based pedestrian tracking 106 5.2 Outlook 106 Bibliography 109 List of Acronyms 119 List of Figures 124 List of Tables 125 Appendix 127 A Derivation of the rotation matrix 2.26 127 B Derivation of the mixed radar signal 2.52 129 C Calculation of the marginal association probabilities 4.51 131 Curriculum Vitae 13

Sonar sensor interpretation for ectogeneous robots

We have developed four generations of sonar scanning systems to automatically interpret surrounding environment. The first two are stationary 3D air-coupled ultrasound scanning systems and the last two are packaged as sensor heads for mobile robots. Template matching analysis is applied to distinguish simple indoor objects. It is conducted by comparing the tested echo with the reference echoes. Important features are then extracted and drawn in the phase plane. The computer then analyzes them and gives the best choices of the tested echoes automatically. For cylindrical objects outside, an algorithm has been presented to distinguish trees from smooth circular poles based on analysis of backscattered sonar echoes. The echo data is acquired by a mobile robot which has a 3D air-coupled ultrasound scanning system packaged as the sensor head. Four major steps are conducted. The final Average Asymmetry-Average Squared Euclidean Distance phase plane is segmented to tell a tree from a pole by the location of the data points for the objects interested. For extended objects outside, we successfully distinguished seven objects in the campus by taking a sequence scans along each object, obtaining the corresponding backscatter vs. scan angle plots, forming deformable template matching, extracting interesting feature vectors and then categorizing them in a hyper-plane. We have also successfully taught the robot to distinguish three pairs of objects outside. Multiple scans are conducted at different distances. A two-step feature extraction is conducted based on the amplitude vs. scan angle plots. The final Slope1 vs. Slope2 phase plane not only separates the rectangular objects from the corresponding cylindrical