High-range Resolution Signatures of Complex Targets and Target Identification

High-range Resolution Signatures of Complex Targets and Target Identification PDF Author: Marvin Cohen
Publisher:
ISBN:
Category : Radar
Languages : en
Pages :

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High-range Resolution Signatures of Complex Targets and Target Identification

High-range Resolution Signatures of Complex Targets and Target Identification PDF Author: Marvin Cohen
Publisher:
ISBN:
Category : Radar
Languages : en
Pages :

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High Range Resolution Radar Target Identification Using the Prony Model and Hidden Markov Models

High Range Resolution Radar Target Identification Using the Prony Model and Hidden Markov Models PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 130

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Fully polarized Xpatch signatures are transformed to two left circularly polarized signals. These two signals are then filtered by a linear FM pulse compression ('chirp') transfer function, corrupted by AWGN, and filtered by a filter matched to the 'chirp' transfer function. The bandwidth of the 'chirp' radar is about 750 MHz. Range profile feature extraction is performed using the TLS Prony Model parameter estimation technique developed at Ohio State University. Using the Prony Model, each scattering center is described by a polarization ellipse, relative energy, frequency response, and range. This representation of the target is vector quantized using a K-means clustering algorithm. Sequences of vector quantized scattering centers as well as sequences of vector quantized range profiles are used to synthesize target specific Hidden Markov Models (HMM's). The identification decision is made by determining which HMM has the highest probability of generating the unknown sequence. The data consist of synthesized Xpatch signatures of two targets which have been difficult to separate with other RTI algorithms. The RTI algorithm developed for this thesis is clearly able to separate these two targets over a 10 by 10 degree (1 degree granularity) aspect angle window off the nose for SNRs as low as 0 dB. The classification rate is 100 % for SNRs of 5 - 20 dB, 95 % for a SNR of 0 dB and it drops rapidly for SNRs lower than 0 dB.

Robust High Range Resolution Radar Target Identification Using a Statistical Feature Based Classifier with Feature Level Fusion

Robust High Range Resolution Radar Target Identification Using a Statistical Feature Based Classifier with Feature Level Fusion PDF Author: Richard Alan Mitchell
Publisher:
ISBN:
Category : Optical pattern recognition
Languages : en
Pages : 242

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Targets and Backgrounds

Targets and Backgrounds PDF Author:
Publisher:
ISBN:
Category : Infrared imaging
Languages : en
Pages : 454

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High Range Resolution Radar Target Identification Using the Prony Model and Hidden Markov Models

High Range Resolution Radar Target Identification Using the Prony Model and Hidden Markov Models PDF Author: Mark R. DeWitt (CAPT, USAF.)
Publisher:
ISBN:
Category : Radar targets
Languages : en
Pages :

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Introduction to Radar Target Recognition

Introduction to Radar Target Recognition PDF Author: P. Tait
Publisher: IET
ISBN: 0863415016
Category : Technology & Engineering
Languages : en
Pages : 428

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Book Description
This book text provides an overview of the radar target recognition process and covers the key techniques being developed for operational systems. It is based on the fundamental scientific principles of high resolution radar, and explains how the underlying techniques can be used in real systems, taking into account the characteristics of practical radar system designs and component limitations. It also addresses operational aspects, such as how high resolution modes would fit in with other functions such as detection and tracking.

Target Recognition Using Linear Classification of High Range Resolution Radar Profiles

Target Recognition Using Linear Classification of High Range Resolution Radar Profiles PDF Author: Ricardo A. Diaz
Publisher:
ISBN: 9781423519942
Category : Pattern recognition systems
Languages : en
Pages : 111

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Book Description
High Range Resolution (HRR) radar profiles map three-dimensional target characteristics onto one-dimensional signals that represent reflected radar intensity along target extent. In this thesis, second through fourth statistical moments are extracted from HRR profiles and input to Fisher Linear Discriminant (FLD) classifiers. An iterative classification process is applied that gradually minimizes required a priori knowledge about the target data. It is found that the second through fourth statistical moments of HRR profiles are useful features in the FLD classification of dissimilar targets and they provide reasonable discrimination of similar targets. Greater than 69% correct classification for two-target scenarios and greater than 60% correct classification for three-target scenarios is obtained using a single HRR profile extracted from a full 360-degree aspect angle window. A key contribution of this thesis is the demonstration that simple statistical moment features and simple linear classifiers can be used to effectively classify HRR profiles.

Automatic Target Recognition Using High Range Resolution Profiles

Automatic Target Recognition Using High Range Resolution Profiles PDF Author: Vijay Bhatnagar
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 94

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Signal Processing, Sensor Fusion, and Target Recognition

Signal Processing, Sensor Fusion, and Target Recognition PDF Author:
Publisher:
ISBN:
Category : Automatic tracking
Languages : en
Pages : 516

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Non-cooperative Air Target Identification Using Radar

Non-cooperative Air Target Identification Using Radar PDF Author:
Publisher:
ISBN:
Category : Airplanes
Languages : en
Pages : 314

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Book Description
Contains the unclassified papers presented at the Symposium. Novel solutions to the Non-Cooperative Target Identification (NCTI) Problem, using radar are proposed. The papers are presented under the following headings: System requirements -- Target characterisation -- Radar measurements and feature extraction -- Target classification -- Scattering techniques, target modelling and validation.