Joseph Tabrikian Underwater Acoustics Symposium Tel-Aviv University

Slides:

Advertisements

Similar presentations

Feedback Reliability Calculation for an Iterative Block Decision Feedback Equalizer (IB-DFE) Gillian Huang, Andrew Nix and Simon Armour Centre for Communications.

Advertisements

Capacity of MIMO Channels: Asymptotic Evaluation Under Correlated Fading Presented by: Zhou Yuan University of Houston 10/22/2009.

Underwater Acoustic MIMO Channel Capacity

Minimum Redundancy MIMO Radar Chun-Yang Chen and P. P. Vaidyanathan California Institute of Technology Electrical Engineering/DSP Lab ISCAS 2008.

General Classes of Lower Bounds on Outage Error Probability and MSE in Bayesian Parameter Estimation Tirza Routtenberg Dept. of ECE, Ben-Gurion University.

2005/12/06OPLAB, Dept. of IM, NTU1 Optimizing the ARQ Performance in Downlink Packet Data Systems With Scheduling Haitao Zheng, Member, IEEE Harish Viswanathan,

NEWCOM – SWP2 MEETING 1 Impact of the CSI on the Design of a Multi-Antenna Transmitter with ML Detection Antonio Pascual Iserte Dpt.

Compressed Sensing in MIMO Radar Chun-Yang Chen and P. P. Vaidyanathan California Institute of Technology Electrical Engineering/DSP Lab Asilomar 2008.

1 Vertically Integrated Seismic Analysis Stuart Russell Computer Science Division, UC Berkeley Nimar Arora, Erik Sudderth, Nick Hay.

Joint MIMO Radar Waveform and Receiving Filter Optimization Chun-Yang Chen and P. P. Vaidyanathan California Institute of Technology Electrical Engineering/DSP.

Volkan Cevher, Marco F. Duarte, and Richard G. Baraniuk European Signal Processing Conference 2008.

An Overview of Delay-and-sum Beamforming

Focused Beam Routing protocol for Underwater Acoustic Networks Josep Miquel Jornet Montana, Milica Stojanovic, Michele Zorzi, Proc. WuWNet 2008.

1 OUTLINE Motivation Distributed Measurements Importance Sampling Results Conclusions.

A Multipath Sparse Beamforming Method

Sarah Middleton Supervised by: Anton van Wyk, Jacques Cilliers, Pascale Jardin and Florence Nadal 3 December 2010.

1 Assessment of Imprecise Reliability Using Efficient Probabilistic Reanalysis Farizal Efstratios Nikolaidis SAE 2007 World Congress.

Roee Diamant, Lutz Lampe, Emmett Gamroth Low Probability of Detection for Underwater Acoustic Communication Networks.

Doc.: IEEE /1399r0 Submission November 2014 Multi-Carrier Training Field for OFDM Transmission in aj (45GHz) Authors/contributors: Date:

International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences 1 Cooperative Wireless.

1 Secure Cooperative MIMO Communications Under Active Compromised Nodes Liang Hong, McKenzie McNeal III, Wei Chen College of Engineering, Technology, and.

1 Waveform Design For Active Sensing Systems – A Computational Approach.

ECE 8443 – Pattern Recognition ECE 8423 – Adaptive Signal Processing Objectives: Introduction SNR Gain Patterns Beam Steering Shading Resources: Wiki:

A Sweeper Scheme for Localization and Mobility Prediction in Underwater Acoustic Sensor Networks K. T. DharanC. Srimathi*Soo-Hyun Park VIT University Vellore,

Bayesian parameter estimation in cosmology with Population Monte Carlo By Darell Moodley (UKZN) Supervisor: Prof. K Moodley (UKZN) SKA Postgraduate conference,

EFFECTS OF MUTUAL COUPLING AND DIRECTIVITY ON DOA ESTIMATION USING MUSIC LOPAMUDRA KUNDU & ZHE ZHANG.

Multiuser Detection (MUD) Combined with array signal processing in current wireless communication environments Wed. 박사 3학기 구 정 회.

Transmit beam-pattern synthesis Waveform design for active sensing Chapters 13 – 14.

Transmit Diversity with Channel Feedback Krishna K. Mukkavilli, Ashutosh Sabharwal, Michael Orchard and Behnaam Aazhang Department of Electrical and Computer.

CHANNEL ESTIMATION FOR MIMO- OFDM COMMUNICATION SYSTEM PRESENTER: OYERINDE, OLUTAYO OYEYEMI SUPERVISOR: PROFESSOR S. H. MNENEY AFFILIATION:SCHOOL OF ELECTRICAL,

Ali Al-Saihati ID# Ghassan Linjawi

Forward-Scan Sonar Tomographic Reconstruction PHD Filter Multiple Target Tracking Bayesian Multiple Target Tracking in Forward Scan Sonar.

MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 1 Dynamic Sensor Resource Management for ATE MURI.

-Arnaud Doucet, Nando de Freitas et al, UAI

Full auto rate MAC protocol for wireless ad hoc networks Z. Li, A. Das, A.K. Gupta and S. Nandi School of Computer Engineering Nanyang Technological University.

Junfeng Xu, Keqiu Li, and Geyong Min IEEE Globecom 2010 Speak: Huei-Rung, Tsai Layered Multi-path Power Control in Underwater Sensor Networks.

Submission doc.: IEEE /1088r0 September 2015 Daewon Lee, NewracomSlide 1 LTF Design for Uplink MU-MIMO Date: Authors:

Asaf Barel Eli Ovits Supervisor: Debby Cohen June 2013 High speed digital systems laboratory Technion - Israel institute of technology department of Electrical.

PARALLEL FREQUENCY RADAR VIA COMPRESSIVE SENSING

1 Value of information – SITEX Data analysis Shubha Kadambe (310) Information Sciences Laboratory HRL Labs 3011 Malibu Canyon.

A Semi-Blind Technique for MIMO Channel Matrix Estimation Aditya Jagannatham and Bhaskar D. Rao The proposed algorithm performs well compared to its training.

Zaid A. Shafeeq Mohammed N. Al-Damluji Al-Ahliyya Amman University Amman - Jordan September

1 ICC 2013, 9-13 June, Budapest, Hungary Localization packet scheduling for an underwater acoustic sensor network By Hamid Ramezani & Geert Leus.

Background 2 Outline 3 Scopus publications 4 Goal and a signal model 5Harmonic signal parameters estimation.

Friday 23 rd February 2007 Alex 1/70 Alexandre Renaux Washington University in St. Louis Minimal bounds on the Mean Square Error: A Tutorial.

Chance Constrained Robust Energy Efficiency in Cognitive Radio Networks with Channel Uncertainty Yongjun Xu and Xiaohui Zhao College of Communication Engineering,

Overcoming the Sensing-Throughput Tradeoff in Cognitive Radio Networks ICC 2010.

Multiple Antennas.

- A Maximum Likelihood Approach Vinod Kumar Ramachandran ID:

Zhaoxia Fu, Yan Han Measurement Volume 45, Issue 4, May 2012, Pages 650–655 Reporter: Jing-Siang, Chen.

Optimisation of Radio Spectrum Usage

Mohsen Riahi Manesh and Dr. Naima Kaabouch

EE359 – Lecture 14 Outline Practical Issues in Adaptive Modulation

SENSOR FUSION LAB RESEARCH ACTIVITIES PART I : DATA FUSION AND DISTRIBUTED SIGNAL PROCESSING IN SENSOR NETWORKS Sensor Fusion Lab, Department of Electrical.

Project Assignment Chris Allen

Tirza Routtenberg Dept. of ECE, Ben-Gurion University of the Negev

Optimum Passive Beamforming in Relation to Active-Passive Data Fusion

Adaptive Beamforming for Target Tracking in Cognitive MIMO Sonar

Radar Micro-Doppler Analysis and Rotation Parameter Estimation for Rigid Targets with Complicated Micro-Motions Peng Lei, Jun Wang, Jinping Sun Beijing.

PERFORMANCE ANALYSIS OF SPECTRUM SENSING USING COGNITIVE RADIO

Yinsheng Liu, Beijing Jiaotong University, China

Introduction to Key LTE TDD Features: Beamforming

Presenter: Xudong Zhu Authors: Xudong Zhu, etc.

Advisor : Tzi-Dar Chiueh Student : Jui-Ping Lien

Master Thesis Presentation

Whitening-Rotation Based MIMO Channel Estimation

Optimal Combining of STBC and Spatial Multiplexing for MIMO-OFDM

Real-time Uncertainty Output for MBES Systems

Baofeng Ji,Bingbing Xing,Huahong Ma Chunguo Li,Hong Wen,Luxi Yang

PHY Signaling for Adaptive Repetition of 11p PPDU

Presentation transcript:

Adaptive Waveform Design for Target Localization and Tracking for Cognitive MIMO Sonar Joseph Tabrikian Underwater Acoustics Symposium Tel-Aviv University June 17, 2013 Research students: W. Huleihel and N. Shraga

Outline Introduction Cognitive MIMO radar/sonar configuration Adaptive waveform for target localization Adaptive waveform for MIMO sonar – static scenario Adaptive waveform design for MIMO sonar – dynamic scenario Conclusions and future work

Introduction - Cognitive Radar/Sonar Adaptive Waveform Design Environment Adaptive Receiver Detection/ Localization/ Tracking/ Classification Transmit Signal Receive Signal Key point: Transmit waveform is designed at very low SNR’s before the target is detected.

Cognitive MIMO Radar/Sonar Configuration

Cognitive MIMO Radar/Sonar Configuration

Cognitive MIMO Radar/Sonar Configuration Target dynamic model Detection/ Estimation/ Tracking Optimal Adaptive Waveform Design Optimal Receiver noise

Waveform Design for Optimal Target Localization Considered criteria: Bayesian Cramér-Rao bound (BCRB) Simple, analytic expressions Ignores large-errors/threshold phenomenon Reuven-Messer bound (RMB) Higher complexity Takes into account large-errors/threshold phenomenon and therefore is able to control the sidelobes

Simulations – Cognitive MIMO Radar

Simulations – Cognitive MIMO Radar BCRB-based waveform design Posterior pdf’s and transmit beampatterns . Auto-focusing effect: Automatic beamforming before detection/estimation.

Simulations – Cognitive MIMO Radar RMB-based waveform design Posterior pdf’s and transmit beampatterns . Auto-focusing effect: Automatic beamforming before detection/estimation.

Simulations – Cognitive MIMO Radar Single target – direction estimation accuracy: ASNR=-6dB k=6

Cognitive MIMO Sonar

Simulations – Cognitive MIMO Sonar

Simulations – Cognitive MIMO Sonar Single target – posterior pdf:

Simulations – Cognitive MIMO Sonar Single target – beampattern:

Waveform Design for Optimal Target Tracking Dynamic model: What is the optimal transmit (spatial) waveform for tracking?

Simulations – Cognitive MIMO Sonar Target Tracking

Simulations – Cognitive MIMO Sonar Single target – posterior pdf (via Monte-Carlo):

Simulations – Cognitive MIMO Sonar Single target – posterior pdf (via Monte-Carlo):

Conclusions and Future Work A new optimal waveform design approach for cognitive MIMO radar/sonar is proposed based on minimizing the BCRB and RMB at each step using the measurements from previous steps. The RMB-based algorithm was shown to provide better results, since it is able to control the sidelobes. This approach provides an automatic focusing array: beamforming before detection or estimation. The method was adapted to consider dynamic targets, which can be interpreted as track-before-detect in transmission. Further research will cover the following issues: Taking into account environmental uncertainties, Wideband signal model, Realistic shallow water channel simulations, Considering other optimization criteria, such as probability of detection.

Thank you!

Simulations – Cognitive MIMO Radar