Дисертації з теми "Radar Recognition"
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1
Cole, Zachary K. "Radar target recognition using bispectrum correlation." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FCole.pdf.
Повний текст джерелаАнотація:
Thesis (M.S. in Physics)--Naval Postgraduate School, June 2007.Thesis Advisor(s): Brett Borden. "June 2007." Description based on title screen as viewed on July 31, 2007. Includes bibliographical references (p. 79-80). Also available in print.
2
Kothe, Martin. "Object Recognition with Surveillance Radar Systems." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:kon4-opus-1161.
Повний текст джерела
3
Yeo, Jiunn Wah. "Bi-spectral method for radar target recognition." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FYeo_Jiunn.pdf.
Повний текст джерелаАнотація:
Thesis (M.S. in Combat Systems Science and Technology))--Naval Postgraduate School, December 2006.Thesis Advisor(s): Brett Borden, Donald L. Walters. "December 2006." Includes bibliographical references (p. 71-72). Also available in print.
4
Green, Thomas Joseph. "Three-dimensional object recognition using laser radar." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/13073.
Повний текст джерелаАнотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.Includes bibliographical references (leaves 217-220).
by Thomas Joseph Green, Jr.
Ph.D.
5
French, A. "Target recognition techniques for multifunction phased array radar." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/19675/.
Повний текст джерелаАнотація:
This thesis, submitted for the degree of Doctor of Philosophy at University College London, is a discussion and analysis of combined stepped-frequency and pulse-Doppler target recognition methods which enable a multifunction phased array radar designed for automatic surveillance and multi-target tracking to offer a Non Cooperative Target Recognition (NCTR) capability. The primary challenge is to investigate the feasibility of NCTR via the use of high range resolution profiles. Given stepped frequency waveforms effectively trade time for enhanced bandwidth, and thus resolution, attention is paid to the design of a compromise between resolution and dwell time. A secondary challenge is to investigate the additional benefits to overall target classification when the number of coherent pulses within an NCTR wavefrom is expanded to enable the extraction of spectral features which can help to differentiate particular classes of target. As with increased range resolution, the price for this extra information is a further increase in dwell time. The response to the primary and secondary challenges described above has involved the development of a number of novel techniques, which are summarized below: • Design and execution of a series of experiments to further the understanding of multifunction phased array Radar NCTR techniques • Development of a ‘Hybrid’ stepped frequency technique which enables a significant extension of range profiles without the proportional trade in resolution as experienced with ‘Classical’ techniques • Development of an ‘end to end’ NCTR processing and visualization pipeline • Use of ‘Doppler fraction’ spectral features to enable aircraft target classification via propulsion mechanism. Combination of Doppler fraction and physical length features to enable broad aircraft type classification. • Optimization of NCTR method classification performance as a function of feature and waveform parameters. • Generic waveform design tools to enable delivery of time costly NCTR waveforms within operational constraints. The thesis is largely based upon an analysis of experimental results obtained using the multifunction phased array radar MESAR2, based at BAE Systems on the Isle of Wight. The NCTR mode of MESAR2 consists of the transmission and reception of successive multi-pulse coherent bursts upon each target being tracked. Each burst is stepped in frequency resulting in an overall bandwidth sufficient to provide sub-metre range resolution. A sequence of experiments, (static trials, moving point target trials and full aircraft trials) are described and an analysis of the robustness of target length and Doppler spectra feature measurements from NCTR mode data recordings is presented. A recorded data archive of 1498 NCTR looks upon 17 different trials aircraft using five different varieties of stepped frequency waveform is used to determine classification performance as a function of various signal processing parameters and extent (numbers of pulses) of the data used. From analysis of the trials data, recommendations are made with regards to the design of an NCTR mode for an operational system that uses stepped frequency techniques by design choice.
6
Pisane, Jonathan. "Automatic target recognition using passive bistatic radar signals." Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-00963601.
Повний текст джерелаАнотація:
We present the design, development, and test of three novel, distinct automatic target recognition (ATR) systems for the recognition of airplanes and, more specifically, non-cooperative airplanes, i.e. airplanes that do not provide information when interrogated, in the framework of passive bistatic radar systems. Passive bistatic radar systems use one or more illuminators of opportunity (already present in the field), with frequencies up to 1 GHz for the transmitter part of the systems considered here, and one or more receivers, deployed by the persons managing the system, and not co-located with the transmitters. The sole source of information are the signal scattered on the airplane and the direct-path signal that are collected by the receiver, some basic knowledge about the transmitter, and the geometrical bistatic radar configuration. The three distinct ATR systems that we built respectively use the radar images, the bistatic complex radar cross-section (BS-RCS), and the bistatic radar cross-section (BS-RCS) of the targets. We use data acquired either on scale models of airplanes placed in an anechoic, electromagnetic chamber or on real-size airplanes using a bistatic testbed consisting of a VOR transmitter and a software-defined radio (SDR) receiver, located near Orly airport, France. We describe the radar phenomenology pertinent for the problem at hand, as well as the mathematical underpinnings of the derivation of the bistatic RCS values and of the construction of the radar images.For the classification of the observed targets into pre-defined classes, we use either extremely randomized trees or subspace methods. A key feature of our approach is that we break the recognition problem into a set of sub-problems by decomposing the parameter space, which consists of the frequency, the polarization, the aspect angle, and the bistatic angle, into regions. We build one recognizer for each region. We first validate the extra-trees method on the radar images of the MSTAR dataset, featuring ground vehicles. We then test the method on the images of the airplanes constructed from data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.99.We test the subspace methods on the BS-CRCS and on the BS-RCS of the airplanes extracted from the data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.98, with variations according to the frequency band, the polarization, the sector of aspect angle, the sector of bistatic angle, and the number of (Tx,Rx) pairs used. The ATR system deployed in the field gives a probability of correct recognition of $0.82$, with variations according to the sector of aspect angle and the sector of bistatic angle.
7
Ehrman, Lisa M. "Automatic target recognition using passive radar and a coordinated flight model." Thesis, Available online, Georgia Institute of Technology, 2004:, 2004. http://etd.gatech.edu/theses/available/etd-06072004-131128/unrestricted/ehrman%5Flisa%5Fm%5F200405%5Fms.pdf.
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8
Olsson, Andreas. "Target recognition by vibrometry with a coherent laser radar." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1730.
Повний текст джерелаАнотація:
Laser vibration sensing can be used to classify military targets by its unique vibration signature. A coherent laser radar receives the target´s rapidly oscillating surface vibrations and by using proper demodulation and Doppler technique, stationary, radially moving and even accelerating targets can be taken care of.
A frequency demodulation method developed at the former FOA, is for the first time validated against real data with turbulence, scattering, rain etc. The issue is to find a robust and reliable system for target recognition and its performance is therefore compared with some frequency distribution methods. The time frequency distributions have got a crucial drawback, they are affected by interference between the frequency and amplitude modulated multicomponent signals. The system requirements are believed to be fulfilled by combining the FOA method with the new statistical method proposed here, the combination being suggested as aimpoint for future investigations.
9
Lane, R. O. "Bayesian super-resolution with application to radar target recognition." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/10593/.
Повний текст джерелаАнотація:
This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target recognition algorithms require access to a database of previously recorded or synthesized radar images for the targets of interest, or a database of features based on those images. However, the resolution of a new image acquired under non-ideal conditions may not be as good as that of the images used to generate the database. Therefore it is proposed to use super-resolution techniques to match the resolution of new images with the resolution of database images. A comprehensive review of the literature is given for super-resolution when used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It is shown that the Bayesian approach improves the probability of correct target classification over standard super-resolution techniques. The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the super-resolution algorithm is then tested as part of a Bayesian target recognition framework using measured radar data.
10
Yen, Brent J. 1977. "Target recognition performance for FLIR and laser radar systems." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86854.
Повний текст джерелаАнотація:
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 69-70).
by Brent J. Yen.
M.Eng.
11
Patel, Kandarp. "Analysis of Human Echolocation Waveform for Radar Target Recognition." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1369160477.
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12
Moore, Linda Jennifer. "Impact of Phase Information on Radar Automatic Target Recognition." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480434103611127.
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13
Seeger, Mauritius. "3-D imaging using optical coherence radar." Thesis, University of Kent, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263698.
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14
Pham, Quoc H. "Automatic target recognition for infrared imagery." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/16687.
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15
Wang, Yuanxun. "Radar signature prediction and feature extraction using advanced signal processing techniques /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.
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16
Wilcher, John S. "Algorithms and performance optimization for distributed radar automatic target recognition." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53533.
Повний текст джерелаАнотація:
This thesis focuses upon automatic target recognition (ATR) with radar sensors. Recent advancements in ATR have included the processing of target signatures from multiple, spatially-diverse perspectives. The advantage of multiple perspectives in target classification results from the angular sensitivity of reflected radar transmissions. By viewing the target at different angles, the classifier has a better opportunity to distinguish between target classes. This dissertation extends recent advances in multi-perspective target classification by: 1) leveraging bistatic target reflectivity signatures observed from multiple, spatially-diverse radar sensors; and, 2) employing a statistical distance measure to identify radar sensor locations yielding improved classification rates.
The algorithms provided in this thesis use high resolution range (HRR) profiles – formed by each participating radar sensor – as input to a multi-sensor classification algorithm derived using the fundamentals of statistical signal processing. Improvements to target classification rates are demonstrated for multiple configurations of transmitter, receiver, and target locations. These improvements are shown to emanate from the multi-static characteristics of a target class’ range profile and not merely from non-coherent gain. The significance of dominant scatterer reflections is revealed in both classification performance and the “statistical distance” between target classes. Numerous simulations have been performed to interrogate the robustness of the derived classifier. Errors in target pose angle and the inclusion of camouflage, concealment, and deception (CCD) effects are considered in assessing the validity of the classifier. Consideration of different transmitter and receiver combinations and low signal-to-noise ratios are analyzed in the context of deterministic, Gaussian, and uniform target pose uncertainty models. Performance metrics demonstrate increases in classification rates of up to 30% for multiple-transmit, multiple-receive platform configurations when compared to multi-sensor monostatic configurations.
A distance measure between probable target classes is derived using information theoretic techniques pioneered by Kullback and Leibler. The derived measure is shown to suggest radar sensor placements yielding better target classification rates. The predicted placements consider two-platform and three-platform configurations in a single-transmit, multiple-receive environment. Significant improvements in classification rates are observed when compared to ad-hoc sensor placement. In one study, platform placements identified by the distance measure algorithm are shown to produce classification rates exceeding 98.8% of all possible platform placements.
17
Borrion, H. "Study of processing techniques for radar non-cooperative target recognition." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1444030/.
Повний текст джерелаАнотація:
Radar is a powerful tool for detecting and tracking airborne targets such as aircraft and missiles by day and night. Nowadays, it is seen as a genuine solution to the problem of target recognition. Recent events showed that cooperative means of identification such as the IFF transponders carried by most aircraft are not entirely reliable and can be switched off by terrorists. For this reason, it is important that target identification be obtained through measurements and reconnaissance based on non-cooperative techniques. In practice, recognition is achieved by comparing the electromagnetic sig nature of a target to a set of others previously collected and stored in a library. Such signatures generally represent the targets reflectivity as a function of space. A common representation is known as one-dimensional high-resolution range-profile (HRRP) and can be described as the projection of the reflectivity along the direction of propagation of the wave. When the measured signature matches a template, the target is identified. The main drawback of this technique is that signatures greatly vary with aspect-angle so that measurements must be made for many angles and in three dimensions. This implies a potentially large cost as large datasets must be created, stored and processed. Besides, any modification of the target structure may yield incorrect classification results. Instead, other processing techniques exist that rely on recent mathematical algorithms. These techniques can be used to extract target features directly from the radar data. Because of the direct relation with target geometry, these feature-based methods seem to be suitable candidates for reducing the need of large databases. However, their performances and their domains of validity are not known. This is especially true when it comes to real targets for at least three reasons. First, the performance of the methods varies with the signal-to-noise ratio. Second, man-made targets arc often more complex than just a set of independent theoretical point-like scatterers. Third, these targets are made up of a large number of scattering elements so that mathematical assumptions are not met. In conclusion, the physical correctness of the computational models are questionable. This thesis investigates the processing techniques that can be used for non-cooperative target recognition. It demonstrates that the scattering-centre extraction is not suitable for the model-based approach. In contrast, it shows that the technique can be used with the feature-based approach. In particular, it investigates the recognition when achieved directly in the z-domain and proposes a novel algorithm that exploits the information al ready in the database for identifying the signal features that corresponds to physical scatterers on the target. Experiments involving real targets show that the technique can enhance the classification performance and therefore could be used for non-cooperative target recognition.
18
Clark, Christine. "Geocoding and stereoscopy of synthetic aperture radar imagery." Thesis, University College London (University of London), 1991. http://discovery.ucl.ac.uk/1349607/.
Повний текст джерелаАнотація:
This thesis is concerned with the geocoding of Synthetic Aperture Radar (SAR) images and the use of stereo SAR images. The work was carried out as part of the preparation for the launch of the ERS-1 sateffite, due in July 1991, which will carry a SAR sensor. There are two basic approaches to geocoding: image-to-object and object-to-image. Both of these methods have been analysed and assessed on experimental data, namely SIR-B imagery of Mount Shasta. Each type of geocoding requires the solution of nonlinear equations. It has been shown that if the parameters which control the geocoding process are given to a good degree of accuracy, each method can give good results. The effect of inaccuracies in the estimation of these parameters has also been analysed. It was found that there was a predominantly linear response to parameter error in both types of geocoding. Experimental investigations into the effects of the resampling, inherent in operational geocoding, showed that the statistical properties of the resulting image may be severely corrupted with pixel values of less than zero being obtained. This discovery has subsequently been given theoretical support. Height can be determined from stereo pairs of images and digital elevation models can thus be produced, aiding both geocoding and topographic mapping. Existing approaches to SAR/SAR stereo all appear to be based on photograinmetric methods. An alternative, analytic approach, believed to be novel, is described and applied to the same Mount Shasta imagery. Using this method, with accurately-known controlling parameters, correspondence with ground data is excellent. However, an analysis of the sensitivity of the approach to inaccuracies in the controlling parameters shows that the method is extremely sensitive to error. The possibility of combining SAR and optical/infrared imagery for stereometric purposes is also discussed from a theoretical viewpoint.
19
Vyas, Sarweshwar Prasad. "Radar remote sensing for monitoring sugar beet production." Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363556.
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20
Pham, Quoc Henry. "Hierarchical processing algorithms for object recognition." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13562.
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21
Fowler, E. "Interpretation of Synthetic Aperture Radar images using fractal geometry." Thesis, Cranfield University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385750.
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22
Li, Kai Chee. "Object identification from a low resolution laser radar system." Thesis, University of Surrey, 1992. http://epubs.surrey.ac.uk/844536/.
Повний текст джерелаАнотація:
Range is a very important and useful physical property. We can extract most of the physical features of an object from a 3-D image. This thesis is about analysing range images taken from a low resolution laser radar system. The objective of this research is to locate and attempt to identify obstacles in the surroundings for an unmanned small tracked vehicle to find its way. A short range (less than 30 metres) laser radar range finder, provided by the Ministry of Defense, gathered range images around the vehicle. Trees, rocks and walls are classified as obstacles. Roads, grassland and bushes are classified as passable objects. In the cases where the objects cannot be identified, we use the steepness as a guideline to classify the object as obstacles or not. Simple image processing techniques are applied to analyse the range image and satisfactory results are obtained. Obstacles can be located in the range images. The images are first segmented by three methods. Firstly, the range gating method is applied which segments the images- according to the information in their range histograms. Secondly, the gradient thresholding method is applied which distinguishes the steep obstacles from the non-steep objects. Thirdly, the spatial isolation is applied which isolates each individual object. The only information contained in a range image is the three dimensions of the object, so we concentrated on the analysis of the physical properties. Besides the size and shape, the texture of an object can also be extracted. Texture reflects what type of objects we are looking at. Walls, plains and other flat objects have fine textures while trees and bushes have rough textures. We have investigated various textural properties derived from the co-occurrence matrix. Another important physical property is the gradient because high gradient always implies obstacles, and these are things which an un-manned vehicle must avoid. The classification method uses the distance function to classify objects. Finally, the algorithm is implemented on an array of transputers. Promising results were observed. By implementing the algorithm onto an array of transputers, the processing time was reduced, and the obstacles can be identified from the range images.
23
Steen, Preston S. T. "The application of connectionist models to radar signal recognition and fusion." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240109.
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Grancharova, Mila. "Representation Learning for Modulation Recognition of LPI Radar Signals Through Clustering." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283194.
Повний текст джерелаАнотація:
Today, there is a demand for reliable ways to perform automatic modulation recognition of Low Probability of Intercept (LPI) radar signals, not least in the defense industry. This study explores the possibility of performing automatic modulation recognition on these signals through clustering and more specifically how to learn representations of input signals for this task. A semi-supervised approach using a bootstrapped convolutional neural network classifier for representation learning is proposed. A comparison is made between training the representation learner on raw time-series and on spectral representations of the input signals. It is concluded that, overall, the system trained on spectral representations performs better, though both approaches show promise and should be explored further. The proposed system is tested both on known modulation types and on previously unseen modulation types in the task of novelty detection. The results show that the system can successfully identify known modulation types with adjusted mutual information of 0.86 for signal-to-noise ratios ranging from -10 dB to 10 dB. When introducing previously unseen modulations, up to six modulations can be identified with adjusted mutual information above 0.85. Furthermore, it is shown that the system can learn to separate LPI radar signals from telecom signals which are present in most signal environments.Idag finns ett behov av pålitlig automatiserad modulationsigenkänning (AMR) av Low Probability of Inercept (LPI)-radarsignaler, inte minst hos försvarsindustrin. Denna studie utforskar möjligheten att utföra AMR av dessa signaler genom klustring och mer specifikt hur man bör lära in representationer av signalerna i detta syfte. En halvövervakad inlärningsmetod som använder en klassificerare baserad på faltningsnätverk föreslås. En jämförelse görs mellan ett system som tränar för representationsinlärning på råa tidsserier och ett system som tränar på spektrala representationer av signalerna. Resultaten visar att systemet tränat på spektrala representationer på det stora hela presterar bättre, men båda metoderna visar lovande resultat och bör utforskas vidare. Systemet testas på signaler från både kända och för systemet tidigare okända modulationer i syfte att pröva förmågan att upptäcka nya typer av modulationer. Systemet identifierar kända modulationer med adjusted mutual information på 0.86 i brusnivåer från -10 dB till 10 dB. När tidigare okända modulationer introduceras till systemet ligger adjusted mutual information över 0.85 för upp till sex modulationer. Studien visar dessutom att systemet kan lära sig skilja LPI-radarsignaler från telekommunikationssignaler som är vanliga i de flesta signalmiljöer.
25
Dixon, Jason Herbert. "Pattern-theoretic automatic target recognition for infrared and laser radar data." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54404.
Повний текст джерелаАнотація:
Pattern theory, a mathematical framework for representing knowledge of complex patterns developed by applied mathematician Ulf Grenander, has been shown to have potential uses in automatic target recognition (ATR). Prior research performed in the mid-1990s at Washington University in St. Louis resulted in ATR algorithms based on concepts in pattern theory for forward-looking infrared (FLIR) and laser radar (LADAR) imagery, but additional work was needed to create algorithms that could be implemented in real ATR systems. This was due to performance barriers and a lack of calibration between target models and real data. This work addresses some of these issues by exploring techniques that can be used to create practical pattern-theoretic ATR algorithms. This dissertation starts by reviewing the previous pattern-theoretic ATR research described above and discussing new results involving the unification of two previously separate outcomes of that research: multi-target detection/recognition and thermal state estimation in FLIR imagery. To improve the overall utility of pattern-theoretic ATR, the following areas are re-examined: 1) generalized diffusion processes to update target pose estimates and 2) the calibration of thermal models with FLIR target data. The final section of this dissertation analyzes the fundamental accuracy limits of target pose estimation under different sensor conditions, independent of the target detection/recognition algorithm employed. The Cramér-Rao lower bound (CRLB) is used to determine these accuracy limits.
26
Yiu, Siu Fung. "Recursive state-space approach to Ground Probing Radar signal processing." Thesis, Lancaster University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278379.
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Kizhakkel, Vinit Rajan. "PULSED RADAR TARGET RECOGNITION BASED ON MICRO-DOPPLER SIGNATURES USING WAVELET ANALYSIS." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366033578.
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28
Wilkinson, Andrew John. "Techniques for 3-D surface reconstruction using synthetic aperture radar interferometry." Thesis, University College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299243.
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Cui, Jingjing. "Recognition of stationary and moving targets from high range resolution radar profiles." Thesis, Imperial College London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440552.
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30
Loza, Artur. "Image processing and time-frequency transform methods for radar characterisation and recognition." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500445.
Повний текст джерелаАнотація:
This thesis covers research into radar target characterisation and recognition with the use of joint time-frequency representations and image processing techniques. Specifically, the recognition of the scatterers of ballistic missiles is of particular interest. The complex motion and composite structure of the targets result in highly nonstationary signal, and therefore its characteristics are investigated both in time and frequency. A novel method for time-frequency analysis of nonstationary radar signals is proposed facilitating formulation of a feature space suited to the problem at hand. This signal-adaptive procedure allows unsupervised computation of the Smoothed Wigner-Ville Distribution based on time and frequency moments of the time-segmented signal.
31
Jiang, Hao Burdick Joel Wakeman Burdick Joel Wakeman. "Adaptive feature selection in pattern recognition and ultra-wideband radar signal analysis /." Diss., Pasadena, Calif. : California Institute of Technology, 2008. http://resolver.caltech.edu/CaltechETD:etd-05302008-134607.
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Ono, Sashi, and Hua Lee. "OBJECT RECOGNITION BY GROUND-PENETRATING RADAR IMAGING SYSTEMS WITH TEMPORAL SPECTRAL STATISTICS." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/604925.
Повний текст джерелаАнотація:
International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, CaliforniaThis paper describes a new approach to object recognition by using ground-penetrating radar (GPR) imaging systems. The recognition procedure utilizes the spectral content instead of the object shape in traditional methods. To produce the identification feature of an object, the most common spectral component is obtained by singular value decomposition (SVD) of the training sets. The identification process is then integrated into the backward propagation image reconstruction algorithm, which is implemented on the FMCW GPR imaging systems.
33
Da, Silveira Reinaldo Bomfim. "Recognition of clutter in weather radars using polarization diversity information and artificial neural networks." Thesis, University of Essex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265022.
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34
Betancourt, Benjamin. "A fuzzy approach to automatic target recognition applied to bare and camouflaged synthetic aperture targets." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2007. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Ehrman, Lisa M. "An Algorithm for Automatic Target Recognition Using Passive Radar and an EKF for Estimating Aircraft Orientation." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7510.
Повний текст джерелаАнотація:
Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition.
The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, further scaling the RCS. A Rician likelihood model compares the scaled RCS of the illuminated aircraft with those of the potential targets. To improve the robustness of the result, the algorithm jointly optimizes over feasible orientation profiles and target types via dynamic programming.
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Pope, Glenn William. "Application of shape-from-shading to synthetic aperture radar." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29755.
Повний текст джерелаАнотація:
This thesis investigates the viability of applying a shape-from-shading technique to SAR imagery. A shape-from-shading algorithm is derived and tested on a single site for which both a Seasat SAR image and Digitial Elevation Model (DEM) were available.
The shape-from-shading technique used in this thesis follows an approach proposed by Frankot and Chellappa for processing slant range SAR imagery. The algorithm incorporates
a one-step technique for projecting non-integrable surface orientation estimates onto an integrable set in the frequency domain along with the iterative convergent shape-from-shading algorithm of Brooks and Horn. The significant issues and choices made in implementing the shape-from-shading algorithm and in preparing the SAR data and DEM are discussed.
The shape-from-shading algorithm was applied to both the test site SAR image and images synthesized from the DEM. Reflectance models were derived from the SAR image and DEM. By quantitatively comparing the shape-from-shading results with the initial conditions used for the experiments, it was found that the algorithm produced substantially
better results when applied to the synthesized images; however, when applied to the SAR image, there was no significant improvement over the initial conditions.Science, Faculty of
Computer Science, Department of
Graduate
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Grönwall, Christina. "Ground object recognition using laser radar data : geometric fitting, performance analysis, and applications /." Linköping : Department of Electrical Engineering, Linköping University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7582.
Повний текст джерела
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Grönwall, Christna. "Ground Object Recognition using Laser Radar Data : Geometric Fitting, Performance Analysis, and Applications." Doctoral thesis, Linköpings universitet, Institutionen för systemteknik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7685.
Повний текст джерелаАнотація:
This thesis concerns detection and recognition of ground object using data from laser radar systems. Typical ground objects are vehicles and land mines. For these objects, the orientation and articulation are unknown. The objects are placed in natural or urban areas where the background is unstructured and complex. The performance of laser radar systems is analyzed, to achieve models of the uncertainties in laser radar data. A ground object recognition method is presented. It handles general, noisy 3D point cloud data. The approach is based on the fact that man-made objects on a large scale can be considered be of rectangular shape or can be decomposed to a set of rectangles. Several approaches to rectangle fitting are presented and evaluated in Monte Carlo simulations. There are error-in-variables present and thus, geometric fitting is used. The objects can have parts that are subject to articulation. A modular least squares method with outlier rejection, that can handle articulated objects, is proposed. This method falls within the iterative closest point framework. Recognition when several similar models are available is discussed. The recognition method is applied in a query-based multi-sensor system. The system covers the process from sensor data to the user interface, i.e., from low level image processing to high level situation analysis. In object detection and recognition based on laser radar data, the range value’s accuracy is important. A general direct-detection laser radar system applicable for hard-target measurements is modeled. Three time-of-flight estimation algorithms are analyzed; peak detection, constant fraction detection, and matched filter. The statistical distribution of uncertainties in time-of-flight range estimations is determined. The detection performance for various shape conditions and signal-tonoise ratios are analyzed. Those results are used to model the properties of the range estimation error. The detector’s performances are compared with the Cramér-Rao lower bound. The performance of a tool for synthetic generation of scanning laser radar data is evaluated. In the measurement system model, it is possible to add several design parameters, which makes it possible to test an estimation scheme under different types of system design. A parametric method, based on measurement error regression, that estimates an object’s size and orientation is described. Validations of both the measurement system model and the measurement error model, with respect to the Cramér-Rao lower bound, are presented.
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Vineet, Vibhav. "Recognition, reorganisation, reconstruction and reinteraction for scene understanding." Thesis, Oxford Brookes University, 2014. https://radar.brookes.ac.uk/radar/items/e8923034-085b-4735-80ae-1c741e55ab99/1.
Повний текст джерелаАнотація:
Perceiving 3D structure and recognizing objects and their properties around us is central to our understanding of the world. For example, when we drive a car from our home to the workplace, we constantly perceive 3D structure, recognise objects and their properties, and understand their functional attributes so as to interact with the environment. Such capabilities permit free and accurate movement in unknown environments and may seem like an easy task for humans. However, for computer systems using artificial vision, it is not. Thus, researchers from philosophy to neuroscience, from mathematics to computer science, have devoted ample time to understand the underlying principles for developing a vision system which would be able to see as well as we do. Such understanding of (sequences of) images is commonly known as Scene Understanding. It consists of solving three classical computer vision problems: recognition, reorganisation and reconstruction. In this dissertation, I focus on some of these problems and propose methods for solving them. The work can be divided into three main parts. In the first part, I show how the problem of recognition and reorganisation can be improved by incorporating some prior information such as context. Specifically I propose novel algorithms to incorporate higher order information, such as context and label consistency over large regions efficiently in the MRF model with only unary and/or pairwise terms. Inference in a MRF is performed using a filter-based mean-field approach. I demonstrate this techniques on joint object and stereo labelling problems, as well as on object class segmentation, showing in addition for joint object-stereo labelling how the method provides an efficient approach for inference in product label spaces. In the second part I propose methods that encapsulate the benefits of reconstruction,recognition and reorganisation so as to solve scene understanding problems. First I propose robust real-time systems that reconstruct dense 3D models of environments on-the-fly and associates them with object labels. This approach works for both indoor and outdoor scenes and scale to any size environments. Next I propose an algorithm to solve the problems of recovering intrinsic scene properties such as shape, reflectance and illumination from a single image, along with estimating the object and attribute segmentation separately. I formulate this joint estimation problem in an energy minimization framework which is able to capture the correlations between intrinsic properties (reflectance, shape, illumination), objects (table, tv-monitor), and materials (wooden, plastic) in a given scene. Finally I design an efficient filter-based mean-field algorithm that jointly estimates human segmentation, pose and depth given a pair of stereo images so as to capture the relationships between these three problems. In the third part I show how human interaction can help in improving the visual recognition task. I propose an interactive 3D labelling and segmentation system that aims to make acquiring segmented 3D models fast, simple, and userfriendly. Carrying a body-worn depth camera, the environment is reconstructed using standard techniques. The user is able to reach out and touch surfaces in the world, and provide object category labels through voice commands. These user provided data are used to learn random forest based object models on-the-fly. Now when the user encounters a previously unobserved and unlabelled region of space, the forest predicts object labels for each voxel, and the volumetric mean-field based inference smooths the final output. I demonstrate compelling results on several sequences that generalizes to unseen regions of the world.
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Laubie, Ellen. "Aspect Diversity for Bistatic Synthetic Aperture Radar." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1492420649395159.
Повний текст джерела
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Djouadi, Abdelhamid. "Analysis of the performance of a parametric and nonparametric classification system : an application to feature selection and extraction in radar target identification /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487324944214317.
Повний текст джерела
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Roos, Jason Daniel. "Probabilistic SVM for Open Set Automatic Target Recognition on High Range Resolution Radar Data." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1472248754.
Повний текст джерела
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Ghinelli, Barbara Maria Gigliola. "The application of artificial neural networks to the interpretation of synthetic aperture radar imagery." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268282.
Повний текст джерела
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Cilliers, Jacques Étienne. "Information theoretic limits on non-cooperative airborne target recognition by means of radar sensors." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10049414/.
Повний текст джерелаАнотація:
The main objective of this research is to demonstrate that information theory, and specifically the concept of mutual information (MI) can be used to predict the maximum target recognition performance for a given radar concept in combination with a given set of targets of interest. This approach also allows for the direct comparison of disparate approaches to designing a radar concept which is capable of target recognition without resorting to choosing specific feature extraction and classification algorithms. The main application area of the study is the recognition of fighter type aircraft using surface based radar systems, although the results are also applicable to airborne radars. Information theoretic concepts are developed mathematically for the analysis of the radar target recognition problem. The various forms of MI required for this application are derived in detail and are tested rigorously against results from digital communication theory. The results are also compared to Shannon’s channel capacity bound, which is the fundamental limit on the amount of information which can be transmitted over a channel. Several sets of simulation based experiments were conducted to demonstrate the insights achievable by applying MI concepts to quantitatively predict the maximum achievable performance of disparate approaches to the radar target recognition problem. Asymptotic computational electromagnetic code was applied to calculate the target’s response to the radar signal for freely available geometrical models of fighter aircraft. The calculated target responses were then used to quantify the amount of information which is transmitted back to the radar about the target as a function of signal to noise ratio (SNR). The information content of the F-14, F-15 and F-16 were evaluated for a 480 MHz bandwidth waveform at 10 GHz as a baseline. Several ultra-wideband (UWB) waveforms, spanning 2-10 GHz, 10- 18 GHz and 2-18 GHz, but which were highly range ambiguous, were evaluated and showed SNR gains of 0.5-2 dB relative to the baseline. The effect of sensing the full polarimetric response of an F-18 and F-35 was evaluated and SNR gains of 5-7 dB over a single linear polarisation were measured. A Boeing 707 scale model (1:25) was measured in the University of Pretoria’s compact range spanning 2-18 GHz and gains of 2 dB were observed between single and dual linear polarisations. This required numerical integration in 8004 dimensions, demonstrating the stability of the MI estimation algorithm in high dimensional signal spaces. The information gained by including the difference channel signal of an X-band monopulse radar for the F-14 data set was approximately 3 dB at 50 km and increased to 4.5 dB at 2 km due to the increased target extent relative to the antenna pattern. This experiment necessitated the use of target profiles which were matched to the range of the target to achieve maximum information transfer. Experiments were conducted to evaluate the loss in information due to envelope processing. For the baseline data set, SNR losses in the region of 7 dB were measured. Linear pre-processing using the fast Fourier transform (FFT) and principal component analysis (PCA), before envelope processing, were compared and the PCA algorithm outperformed the FFT by approximately 1 dB at high MI values. Finally, the expression for multi-target MI was applied in conjunction with Fano’s inequality to predict the probability of incorrectly classifying a target. Probability of error is a critical parameter for a radar user. For the baseline data set, at P(error) = 0.001, maximum losses in the region of 0.6 to 0.9 dB were measured. This result shows that these targets are easily separable in the signal space. This study was only the proverbial “tip of the iceberg” and future research could extend the results and applications of the techniques developed. The types of targets and configurations of the individual targets could be increased and analysed. The analysis should also be extended to describe effects internal to the radar such as phase noise, spurious signals and analogue to digital converters and external effects such as clutter and multipath. The techniques could also be applied to quantify the gains in target recognition performance achievable for multistatic radar, multiple input multiple output (MIMO) radar and more exotic concepts, such as the fusion of data from multiple monostatic microwave radars with multi-receiver multi-band passive bistatic radar (PBR) data.
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Johansson, Tim, and Leo Wikström. "An Exploratory Study of Simple Fall and Activity Recognition Using mmWave." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21794.
Повний текст джерелаАнотація:
Background. As smart appliances become more attractive, the demand from public consumers grows, and producers are in search of innovative technologies that may aid in the creation of smart homes. Current products may use screens and buttons, voice commands and motion detection to create an interactive experience for consumers. A rather new technology that has gathered attention in recent years is millimetre wave radar sensors (mmWave). This technology uses electromagnetic waves to detect objects in the vicinity of the physical sensor; it may detect both the range, velocity and orientation of an object in relation to itself. The current research has had a main focus in automotive and industrial industries, and the technology has thus far been applied to areas such as vital signs monitoring, people counting, motion control, object detection and collision aversion among others. An attractive feature for use in smart homes that the sensor provides, or rather lacks, is its inability to identify different people. As the information gathered is a point cloud -- in low resolution -- any monitored people retain their privacy under normal circumstances. Objectives. The aim of this thesis is to verify the usability of mmWave sensors in smart homes, as well as reaching an initial understanding of people's opinions regarding the mmWave technology. Method. Experiments are performed to test how well the mmWave sensors can determine if a person is standing, sitting, lying or if they have fallen. The approach for the developed program to make these predictions are done through simple algorithms. Experiments were performed in an environment that was meant to mimic the conditions of a home. Participants were also asked about their opinion of potentially using the technology in their home, both regarding imagined usage and whether the sensor would cause them any discomfort. Results. The results show that while the implemented software in this thesis helps validate the proof of concept for the intended purpose, the technology shows a lot of promise for the future. Further algorithmic efforts will however be required to reach the desired maturity. The opinions of the participant show a generally positive response in using the sensor, however, they also note that if the sensor is to be used in their home, any data gathered should be both available and in control of the consumer to ease suspicions of misuse. Conclusions. The authors conclude that while not yet quite ready, the sensor is indeed a probable candidate to be integrated into smart homes of the future.Bakgrund. Då smarta apparater blir mer attraktiva växer efterfrågan för dessa produkter. Tillverkare söker därmed efter innovativa teknologier som kan bistå i skapandet av smarta hem. De produkter som finns idag använder sig av skärmar och knappar, röstkommandon och rörelsedetektering för att skapa en interaktiv upplevelse för användarna. En relativt ny tekonologi som har fått uppmärksamhet de senaste åren är radarsensorer med millimetervågor (mmWave). Denna teknologi använder elektromagnetiska vågor för att upptäcka föremål i sin närhet; sensorn kan känna av både avstånd, hastighet och orientering av ett objekt i relation till sig själv. Existerande forskning har framförallt fokuserat på bil- och tillverkningsindustrierna, och teknologin har hittills applicerats på områden som bland annat övervakning av vitala tecken, räkning av människor, rörelsestyrning, detektion av föremål och kollisionsundvikande system. En attraktiv funktionalitet för användande i smarta hem som den här sortens sensor tillhandahåller, eller snarare saknar, är dess oförmåga att identifiera olika människor. Eftersom datan sensorn samlar in består av ett punktmoln -- i låg upplösning -- kommer den under normala förhållanden inte inkräkta på privatliv och integritet hos användarna. Syfte. Målet med detta projekt är att undersöka användbarheten av mmWave-sensorer i smarta hem, samt att komma till en initial insikt om folks åsikter angående mmWave-teknologin. Metod. Experiment har utförts för att verifiera hur väl sensorerna kan avgöra om en person står upp, sitter ner, ligger ner eller har fallit. Mjukvaran som utvecklades för att avgöra vilken handling en person utför tar sig an detta med hjälp av enkla algoritmer. Experimenten utfördes i en miljö som var tänkt att efterlikna förhållandena i ett hem. Deltagarna fick också frågor angående sina åsikter om att potentiellt använda teknologin i sina hem, både vad gäller möjliga användningsområden samt huruvida varandet av sensorn i hemmet skulle orsaka dem något obehag. Resultat. Resultaten visar att även om den skapade mjukvaran är otillförlitlig för det tänkta användandet så visar teknologin lovande tecken för framtiden. Deltagarnas åsikter visar också på ett generellt sett positivt gensvar gentemot användandet av sensorn, men de påpekar också att om sensorn ska användas i deras hem bör all data vara tillgänglig för och kontrollerad av användaren, allt för att lindra möjliga misstankar om missbruk av datan. Slutsatser. Författarna kommer fram till att även om de inte än är riktigt redo, så är mmWave-sensorerna en sannolik kandidat till att användas i framtidens smarta hem.
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Erdem, Erem. "Digital Modulation Recognition." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611281/index.pdf.
Повний текст джерелаАнотація:
In this thesis work, automatic recognition algorithms for digital modulated signals are surveyed.
Feature extraction and classification algorithm stages are the main parts of a modulation recognition system. Performance of the modulation recognition system mainly depends on the prior knowledge of some of the signal parameters, selection of the key features and classification algorithm selection.
Unfortunately, most of the features require some of the signal parameters such as carrier frequency, pulse shape, time of arrival, initial phase, symbol rate, signal to noise ratio, to be known or to be extracted. Thus, in this thesis, features which do not require prior knowledge of the signal parameters, such as the number of the peaks in the envelope histogram and the locations of these peaks, the number of peaks in the frequency histogram, higher order moments of the signal are considered. Particularly, symbol rate and signal to noise ratio estimation methods are surveyed. A method based on the cyclostationarity analysis is used for symbol rate estimation and a method based on the eigenvector decomposition is used for the estimation of signal to noise ratio. Also, estimated signal to noise ratio is used to improve the performance of the classification algorithm.
Two methods are proposed for modulation recognition:
1) Decision tree based method
2) Bayesian based classification method
A method to estimate the symbol rate and carrier frequency offset of minimum-shift keying (MSK) signal is also investigated.
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Scott, Michael L. "Automated Characterization of Bridge Deck Distress Using Pattern Recognition Analysis of Ground Penetrating Radar Data." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/28624.
Повний текст джерелаАнотація:
Many problems are involved with intspecting and evaluating the condition of
bridges in the United States. Concrete bridge deck inspection and evaluation
presents one of the largest problems. The deterioration of these concrete
decks progresses more rapidly than any other bridge component, which leads to
early concrete deck replacements that must be done before the bridge
superstructure needs to be replaced. The primary cause of deterioration in
these concrete bridge decks is corrosion-induced concrete cracking, which
frequently results in delaminations. Delamination distress increases the life
cycle cost of maintaining a concrete bridge deck, particularly when it is not
detected early on. Early detection of delamination distress can facilitate
economical repair and rehabilitation work, but bridge engineers must recommend
deck replacement if repairs are delayed too long or inspection tools cannot detect
delaminations early enough.
The Federal Highway Administration has responded to the need for a better
bridge deck inspection tool by contracting Lawrence Livermore National Laboratory
to develop two new prototype ground penetrating radar systems. These two systems
generate three-dimensional data that provide a representation of features that lie
below the bridge deck surface. Both of these systems produce large amounts of
data for an individual bridge deck, which makes automated data processing very
desirable. The primary goal of the automated processing is to characterize bridge
deck distress represented in the data. This study presents data collected from
sample bridge deck sections using one of the prototype systems. It also describes
the development and implementation of appropriate methods for automating data
processing. The automated data processing is accomplished using image processing
and pattern recognition algorithms developed in the study.Ph. D.
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Mat, Lela Mohamed Said bin. "The integration of remotely sensed data using Landsat and radar imagery with ancillary information for forest management." Thesis, University of Nottingham, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314550.
Повний текст джерела
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Chamberlain, Neil Frederick. "Recognition and analysis of aircraft targets by radar, using structural pattern representations derived from polarimetric signatures /." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487599963593822.
Повний текст джерела
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Tallon, Doris. "The under-recognition of trauma in the diagnosis of Borderline Personality Disorder (BPD)." Thesis, Oxford Brookes University, 2015. https://radar.brookes.ac.uk/radar/items/fa410a82-9abe-4069-b57f-3dea322f98fa/1/.
Повний текст джерелаАнотація:
BPD is a complex condition presenting with a wide array of features, making it difficult to diagnose and treat. Controversially, there is also concern about BPD misdiagnosis due to under-recognition of trauma and PTSD/CPTSD (Complex PTSD) because of common aetiology. PTSD/CPTSD has a better track record of successful treatment; as typically BPD treatment focuses more on symptoms, while PTSD/CPTSD treats underlying traumatic causes. Aim: The research objective is to assess if early screening for traumatic exposure and PTSD/CPTSD symptoms will enhance BPD diagnosis, and lead to improved treatment. Methodology: Following clinical and academic reviews, two stages were completed. Stage 1: Initially medical records of BPD (N=60) patients in three UK Mental Health Hospitals were examined for evidence of BPD, trauma, PTSD and CPTSD. Stage 2: Separate BPD outpatients (N=40) were screened for trauma, PTSD/CPTSD using a new simple ‘BPD Trauma Exposure and Reactions Screen’ (BTERS). Reliability and validity was then assessed using recommended reference instruments (CAPS and SIDES). Results: Trauma was recorded in 47% of the stage 1 medical records, 100% in stage 2, 92.5% trauma in childhood. Sixty percent of stage 2 patients suffered distressing non-life-threatening trauma consistent with Adjustment Disorder. High trauma percentages in BPD are explained by a combination of life-threatening trauma, requiring specialist PTSD/CPTSD treatment, and non-life-threatening, which is treatable using similar techniques by BPD clinicians without specialist training. Conclusions: Although insufficient evidence for BPD misdiagnosis was found, an under-diagnosis of comorbid PTSD/CPTSD was confirmed. Without initial screening (BTERS) of BPD patients, clinicians are missing PTSD/CPTSD diagnoses, and hence are losing the opportunity for early treatment for a significant percentage of BPD patients, which could be critical to improved recovery and reduced suicide rates.