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检索条件"任意字段=8th Sensor Signal Processing for Defence Conference, SSPD 2019"

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2019 sensor signal processing for defence conference, sspd 2019

2019 Sensor Signal Processing for Defence Conference, SSPD 2...

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8th sensor signal processing for defence conference, sspd 2019

ISBN: (纸本)9781728105031

the proceedings contain 21 papers. the topics discussed include: tradeoffs in detection and localization performance for mobile sensor scanning strategies;detection of incumbent radar in the 3.5 GHz CBRS band using support vector machines;measuring smoothness of real-valued functions defined by sample points on the unit circle;accuracy of Adcock Watson-Watt DF in the presence of channel errors;dual-functional radar-communication waveform design under constant-modulus and orthogonality constraints;message passing for joint registration and tracking in multistatic radar;prediction of sensor performance required for reliable aircraft target discrimination;and designing linear FM active sonar waveforms for continuous line source transducers to maximize the fisher information at a desired bearing.

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How Noise Radar Technology Brings Together Active Sensing and Modern Electronic Warfare Techniques in a Combined sensor Concept 8

How Noise Radar Technology Brings Together Active Sensing an...

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8th sensor signal processing for defence conference (sspd)

作者： Wasserzier, Christoph Worms, Josef G. O'Hagan, Daniel W. Fraunhofer Inst High Frequency Phys & Radar Tech Fraunhoferstr 20 Wachtberg Germany

ISBN: (纸本)9781728105031

Research on modern EW algorithms follows a trend of increasing digital hardware implementations. the powerful features of these digital algorithms allow detection, location, identification and jamming of hostile radars. this paper presents a sensor concept in which mature EW features are expanded by an active sensing component using noise radar technology. It is shown that the flawless integration of noise radar into the EW functionality is accompanied with effective separation of all concurrent but different tasks of the combined sensor. Experimental results are presented which underline the basic idea of noise radar technology being the key enabler of this combined sensor concept.

关键词： Electronic Warfare Resource Management Multi-Function RF sensors Software-Defined Radar Software-Defined EW Spectrum Sharing Noise Radar

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Detection of Incumbent Radar in the 3.5 GHz CBRS Band Using Support Vector Machines 8

Detection of Incumbent Radar in the 3.5 GHz CBRS Band Using ...

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8th sensor signal processing for defence conference (sspd)

作者： Caromi, Raied Souryal, Michael NIST Commun Technol Lab Gaithersburg MD 20899 USA

ISBN: (纸本)9781728105031

In the 3.5GHz Citizens Broadband Radio Service (CBRS), 100MHz of spectrum will be dynamically shared between commercial users and federal incumbents. Dynamic use of the band relies on a network of sensors dedicated to detecting the presence of federal incumbent signals and triggering protection mechanisms when necessary. this paper uses field-measured waveforms of incumbent signals in and adjacent to the band to evaluate the performance of support vector machine (SVM) classifiers for these sensors. We find that a peak analysis classifier and a higher-order statistics classifier perform comparably when the signal is in white Gaussian noise or commercial long term evolution (LTE) emissions, but with out-of-band emissions of adjacent-band systems the peak analysis classifier is far superior. this result also highlights the importance of including adjacent-band emissions in any performance evaluation of 3.5GHz sensors.

关键词： 3.5GHz CBRS radar detection machine learning sensor

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Training and Validation of Automatic Target Recognition Systems using Generative Adversarial Networks 8

Training and Validation of Automatic Target Recognition Syst...

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8th sensor signal processing for defence conference (sspd)

作者： Karjalainen, Antti Ilari Mitchell, Roshenac Vazquez, Jose SeeByte Ltd Edinburgh Midlothian Scotland

ISBN: (纸本)9781728105031

this research provides advances aiming to improve the adaptability and usability of Automatic Target Recognition (ATR) algorithms in new environments. We propose to use a Generative Adversarial Networks (GAN) based approach to add simulated contacts into real sidescan sonar images. Our results show that the GAN approach is able to create realistic contacts. We carried out a visual experiment to validate that a trained operator was unable to distinguish real objects from simulated objects. In addition, we demonstrate that an ATR tuned using simulated objects, generated by the GAN, achieves a comparable performance to an ATR tuned using real data.

关键词： GAN (Generative Adversarial Networks) Deep neural networks Image processing Automatic Target Recognition (ATR)

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TWO STAGE AUDIO-VIDEO SPEECH SEPARATION USING MULTIMODAL CONVOLUTIONAL NEURAL NETWORKS 8

TWO STAGE AUDIO-VIDEO SPEECH SEPARATION USING MULTIMODAL CON...

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8th sensor signal processing for defence conference (sspd)

作者： Xian, Yang Sun, Yang Wang, Wenwu Naqvi, Syed Mohsen Newcastle Univ Intelligent Sensing & Commun Res Grp Newcastle Upon Tyne Tyne & Wear England Univ Surrey Ctr Vis Speech & Signal Proc Surrey England

ISBN: (纸本)9781728105031

the performance of the audio-only neural networks based monaural speech separation methods is still limited, particularly when multiple-speakers are active. the very recent method [1] used the audio-video (AV) model to find the non-linear relationship between the noisy mixture and the desired speech signal. However, the over-fitting problem always happens when the AV model is trained. Hence, the separation performance is limited. To address this limitation, we propose a system with two sequentially trained AV models to separate the desired speech signal. In the proposed system, after the first AV model is trained, its output is used to calculate the training target of the second AV model, which is exploited to further improve the separation performance. the GRID audiovisual sentence corpus is used to generate the training and testing datasets. the signal to distortion ratio (SDR) and short-time objective intelligibility (STOI) proved the proposed system outperforms the state-of-the-art method.

关键词： speech separation mapping relation AV model sequentially AV models

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TRADEOFFS IN DETECTION AND LOCALISATION PERFORMANCE FOR MOBILE sensor SCANNING STRATEGIES 8

TRADEOFFS IN DETECTION AND LOCALISATION PERFORMANCE FOR MOBI...

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8th sensor signal processing for defence conference (sspd)

作者： Spyrou, Loukianos Chambersy, Pat Sellathuraiy, Mathini thompson, John Univ Edinburgh Sch Engn IDCOM Edinburgh EH9 3JL Midlothian Scotland Heriot Watt Univ Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9781728105031

Wireless sensor networks enjoy many advantages over wired networks due to their ability to be deployed easily and flexibly in many scenarios. However, they suffer from the drawback that the environment may be unknown and hence the sensor network cannot easily be optimised for it. Furthermore, state-of-the-art studies only consider the detection and localisation performance separately. the main novelty of this work is that we compare the theoretical properties of three scanning strategies both in terms of their detection and localisation performance. We consider: a) sequential scanning, where all sensors scan the channels in sequence, b) groupwise scanning, where the sensors are split into groups with each one performing a sequential scan, and c) random scanning, where each sensor is assigned a channel at random. We demonstrate the theoretical properties of the strategies and perform a numerical evaluation for a typical radio surveillance scenario. the tradeoffs of the methods between detection and localisation performance are demonstrated to be dependent on the detection and localisation accuracy with respect to the number of sensors. Approximate knowledge of those curves can aid in the design of an optimal sensor scanning strategy.

关键词： sensor networks sensor scanning multiuser detection localisation

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Adaptive Detection With Diffuse Multipath Exploitation in Partially Homogeneous Environments 8

Adaptive Detection With Diffuse Multipath Exploitation in Pa...

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8th sensor signal processing for defence conference (sspd)

作者： Gulen, Seden Hazal Hayvaci, Harun Taha TOBB Univ Econ & Tech ETU Dept EEE Ankara Turkey

ISBN: (纸本)9781728105031

In this paper, we deal with the problem of detecting point-like targets in the presence of multipath under the assumption of a partially homogeneous Gaussian disturbance with unknown covariance matrix. therefore, we introduce an unknown scaling factor which represents the mismatch between the noise covariance matrices of test and training signals. Besides, we model the target echo as a combination of direct and multipath components where multipath echoes are thought of as scattered signals from a glistening surface which is referred to as diffuse multipath environment. Hence, the total multipath return is also represented as a Gaussian distributed random vector with an unknown covariance matrix. At the design stage, we construct a constrained Generalized Likelihood Ratio Test (GLRT) by assuming that the total primary data covariance structure, in the target present case, resembles to the covariance matrix obtained from secondary data up to a degree (related to noise scaling factor and multipath contribution). Finally, at the analysis stage, we compared the developed algorithm with the existing solutions available in the open literature. the results highlight that the new detector copes well with severe multipath conditions and has considerable scale-invariance.

关键词： Covariance matrices Detectors Clutter Random variables Data models Probability density function signal to noise ratio

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Message Passing for Joint Registration and Tracking in Multistatic Radar 8

Message Passing for Joint Registration and Tracking in Multi...

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8th sensor signal processing for defence conference (sspd)

作者： Cormack, David Hopgood, James R. Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland Univ Edinburgh Sch Engn Edinburgh EH9 3FG Midlothian Scotland

ISBN: (纸本)9781728105031

sensor registration is fundamental in sensor fusion. Inaccuracies in sensor location and rotation can manifest themselves into the measurements used in Multiple Target Tracking (MTT), and dramatically degrade its performance. these registration parameters are often estimated separately to any multitarget estimation, which could lead to increased computational expense, and also to systematic errors. Recent works have shown that MTT algorithms derived from Belief Propagation (BP) are computationally efficient and highly scalable for large tracking scenarios. this work presents a hierarchical Bayesian model inspired by single-cluster methods from the Random Finite Set (RFS) literature, that allow for the registration parameters to be estimated jointly with the multiple target tracking. Simulations are carried out on a multistatic radar network containing two radars with a relative range and azimuth bias between them. Results are presented for a particle-BP MTT algorithm, and it's performance is compared to that of a Sequential Monte Carlo (SMC)-Probability Hypothesis Density (PHD) filter. the results show that the BP algorithm outperforms the PHD implementation in terms of accuracy by around 10%.

关键词： Data fusion sensor registration belief propagation radar target tracking

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Evaluation of Performance of VDSR Super Resolution on Real and Synthetic Images 8

Evaluation of Performance of VDSR Super Resolution on Real a...

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8th sensor signal processing for defence conference (sspd)

作者： Vint, D. Di Caterina, G. Soraghan, J. J. Lamb, R. A. Humphreys, D. Univ Strathclyde Ctr Signal & Image Proc Glasgow Lanark Scotland Leonardo MW Ltd Airborne & Space Syst Div Edinburgh Midlothian Scotland

ISBN: (纸本)9781728105031

this paper presents an evaluation of the suitability of the Very Deep Super Resolution (VDSR) architecture, to increase the spatial resolution of lower quality images. For this aim, two sets of tests are performed. the former being on real life images to determine the networks ability to improve low resolution images. the second test is performed on images of a resolution chart, and therefore synthetic. this is to analyse the frequency response of the network. For each test, three metrics are used to assess image quality. these are the Peak signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Modulation Transfer Function (MTF). Experimental results show that the VDSR network is able to increase the quality of the images within the first test in all three metrics, therefore showing that the network is suitable for super resolution. the second test provides more information on the limitations of the network when given a high contrast image, and the resulting ringing effects it can create. therefore results in PSNR/SSIM values are not improved over the low resolution images, however they have a higher MTF curve as well as more visually sharp images.

关键词： Diffraction Deep Learning VDSR PSNR SSIM MTF

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Prediction of sensor performance required for reliable aircraft target discrimination 8

Prediction of sensor performance required for reliable aircr...

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8th sensor signal processing for defence conference (sspd)

作者： Parker, David White, Henry Oakley, John Bishop, Gary BAE Syst Air Sect Warton England BAE Syst Air Sect Bristol Avon England Dmist Res Ltd Stockport Lancs England

ISBN: (纸本)9781728105031

For new military aircraft a specification of sensor characteristics and performance is required at an early stage in the design cycle, well before testing of a prototype. In the early days of military aviation the Johnson criteria [1] were used to determine the sensor resolution required for target recognition by a human. In the present day sensor data are processed by computer using various Automatic Target Recognition (ATR) algorithms. However there is no accepted method for predicting the sensor resolution and SNR required for reliable ATR and hence there is risk that any chosen sensor may fail to support the required ATR performance. this paper reports a study into the use of publicly-available CAD models for aircraft to address this requirement. the study considers the worst-case confusion between two views of 15 different aircraft types. For simplicity only rotations by an angle. about the Z (vertical) axis are considered. Firstly the sensor resolution and noise level is fixed. then for each aircraft type and view angle an ensemble of synthetic silhouettes are generated. Using these ensembles, a-posteriori distributions of 5 standard scale-invariant shape features (eccentricity, orientation, solidity, circularity and bounding box aspect ratio) are estimated for each view angle.. the performance of ATR at the given resolution and noise level is predicted by estimating the Bayes Error Rate [2] when deciding between each aircraft type and the 14 non-matching types using these features. the worst-case confusion in terms of erroneous aircraft type and view angle is identified. the sensor resolution is then changed and the above process repeated to investigate the effect of varying sensor resolution on performance. As expected, high sensor resolution leads to low probability of misclassification, even in the worst-case. Reduction in resolution and increasing noise level causes the Bayes Error Rate to rise quickly. the Bayes Error Rate gives a fundamental limit to th

关键词： Classification Automatic Target Recognition Bayes Error Rate

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