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5th sensor signal processing for defence, sspd 2015

ISBN: (纸本)9781479974443

the proceedings contain 32 papers. the topics discussed include: sensor management with regional statistics for the PHD filter;joint navigation and synchronization using SOOP in GPS-denied environments: algorithm and empirical study;how signal processing underpins military information superiority;micro-Doppler based recognition of ballistic targets using 2-D Gabor filters;maximum likelihood signal parameter estimation via track before detect;direction of arrival estimation using a cluster of beams in a cone-shaped digital array radar;low-complexity robust adaptive beamforming algorithms exploiting shrinkage for mismatch estimation;normalized multi-stage clustering equalizer for underwater acoustic channels;and low complexity parameter estimation for off-the-grid targets.

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Maximum Likelihood signal Parameter Estimation via Track before Detect 5

Maximum Likelihood Signal Parameter Estimation via Track bef...

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5th sensor signal processing for defence, sspd 2015

作者： Üney, Murat Mulgrew, Bernard Clark, Daniel Institute for Digital Communications School of Engineering University of Edinburgh EdinburghEH9 3JL United Kingdom School of Eng. and Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom

ISBN: (纸本)9781479974443

In this work, we consider the front-end processing for an active sensor. We are interested in estimating signal amplitude and noise power based on the outputs from filters that match transmitted waveforms at different ranges and bearing angles. these parameters identify the distributions in, for example, likelihood ratio tests used by detection algorithms and characterise the probability of detection and false alarm rates. Because they are observed through measurements induced by a (hidden) target process, the associated parameter likelihood has a time recursive structure which involves estimation of the target state based on the filter outputs. We use a track-before-detect scheme for maintaining a Bernoulli target model and updating the parameter likelihood. We use a maximum likelihood strategy and demonstrate the efficacy of the proposed approach with an example. © 2015 IEEE.

关键词： Parameter estimation

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GPU-Accelerated Gaussian Processes for Object Detection 5

GPU-Accelerated Gaussian Processes for Object Detection

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5th sensor signal processing for defence, sspd 2015

作者： Blair, Calum thompson, John Robertson, Neil Institute for Digital Communications University of Edinburgh Edinburgh United Kingdom Institute for Sensors Signals and Systems Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9781479974443

Gaussian Process classification (GPC) allows accurate and reliable detection of objects. the high computational load of squared-error or radial basis function kernels limits the applications that GPC can be used in, as memory requirements and computation time are both limiting factors. We describe our version of accelerated GPC on GPU (Graphics processing Unit). GPUs have limited memory so any GPC implementation must be memory-efficient as well as computationally efficient. Using a high-performance pedestrian detector as a starting point, we use its packed or block-based feature descriptor and demonstrate a fast matrix multiplication implementation of GPC which is also extremely memory efficient. We demonstrate a speed up of 3.7 times over a multicore, BLAS-optimised CPU implementation. Results show that this is more accurate and reliable than results obtained from a comparable support vector machine algorithm. © 2015 IEEE.

关键词： Graphics processing unit

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Variational Bayesian PHD Filter with Deep Learning Network Updating for Multiple Human Tracking 5

Variational Bayesian PHD Filter with Deep Learning Network U...

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5th sensor signal processing for defence, sspd 2015

作者： Feng, Pengming Wang, Wenwu Naqvi, Syed Mohsen Chambers, Jonathon Communications Sensors Signal and Information Processing Group Newcastle University United Kingdom Center for Vision Speech and Signal Processing University of Surrey United Kingdom

ISBN: (纸本)9781479974443

We propose a robust particle probability hypothesis density (PHD) filter where the variational Bayesian method is applied in joint recursive prediction of the state and the time varying measurement noise parameters. the proposed particle PHD filter is based on forming variational approximation to the joint distribution of states and noise parameters at each frame separately;the state is estimated with a particle PHD filter and the measurement noise variances used in the update step are estimated with a fixed point iteration approach. A deep belief network (DBN) is used in the update step to mitigate the effect of measurement noise on the calculation of particle weights in each frame. the deep learning network is trained based on both colour and oriented gradient histogram (HOG) features and then used to mitigate the measurement noise from the particle selection step, thereby improving the tracking performance. Simulation results using sequences from the CAVIAR dataset show the improvements of the proposed DBN aided variational Bayesian particle PHD filter over the traditional particle PHD filter. © 2015 IEEE.

关键词： Bayesian networks

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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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Lattice-Reduction Aided Successive Optimization Tomlinson-Harashima Precoding Strategies for Physical-Layer Security in Wireless Networks 4

Lattice-Reduction Aided Successive Optimization Tomlinson-Ha...

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4th Conference of the sensor signal processing for defence (sspd)

作者： Lu, Xiaotao Zu, Keke de Lamare, Rodrigo C. Univ York Dept Elect Commun Res Grp York YO10 5DD N Yorkshire England Ericsson Res Kista Stockholm Sweden CETUC PUC Rio Gavea RJ Brazil

ISBN: (纸本)9781479952939;9781479952946

In this paper, we investigate precoding techniques for physical-layer security in multi-user MIMO systems under various conditions of channel state information (CSI). A Lattice Reduction (LR) aided non-linear precoding technique based on Successive Optimization Tomlinson-Harashima Precoding (SO-thP) and Simplified Generalized Matrix Inversion (S-GMI) technique is proposed along with a strategy for injecting artificial noise prior to transmission. Simulation results show that the proposed LR-SO-thP+S-GMI precoding technique outperforms existing non-linear and linear precoding algorithms in terms of BER and secrecy rate performances.

关键词： Physical-layer security techniques precoding transmit processing

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Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm 12

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Al...

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12th sensor signal processing for defence Conference (sspd)

作者： Soleymani, Seyed Ahmad Goudarzi, Shidrokh Liu, Xingchi Mihaylova, Lyudmila Wang, Wenwu Xiao, Pei Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford Surrey England Univ West London Sch Comp & Engn London England Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England Univ Surrey Inst Commun Syst 5GIC Guildford Surrey England

ISBN: (纸本)9798350337327

this paper proposes a scheme for multiple un-manned aerial vehicles (UAVs) to track multiple targets in challenging 3-D environments while avoiding obstacle collisions. the scheme relies on Received-signal-Strength-Indicator (RSSI) measurements to estimate and track target positions and uses a Q-Learning (QL) algorithm to enhance the intelligence of UAVs for autonomous navigation and obstacle avoidance. Considering the limitation of UAVs in their power and computing capacity, a global reward function is used to determine the optimal actions for the joint control of energy consumption, computation time, and tracking accuracy. Extensive simulations demonstrate the effectiveness of the proposed scheme, achieving accurate and efficient target tracking with low energy consumption.

关键词： Multi-target tracking UAV Q-Learning Edge Computing

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Implementation of Adaptive Kernel Kalman Filter in Stone Soup 12

Implementation of Adaptive Kernel Kalman Filter in Stone Sou...

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12th sensor signal processing for defence Conference (sspd)

作者： Wright, James S. Hopgood, James R. Davies, Mike E. Proudler, Ian K. Sun, Mengwei Univ Edinburgh Inst Digital Commun Edinburgh EH9 3FG Midlothian Scotland Dstl Dstl Porton Down Bldg 5Rm 101 Salisbury SP4 0JQ Wilts England Univ Strathclyde Dept Elect & Elect Engn Ctr Signal & Image Proc CeSIP Glasgow G1 1XW Lanark Scotland

ISBN: (纸本)9798350337327

the recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing-only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.

关键词： Adaptive kernel Kalman filter Tracking Stone Soup

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sspd Conference 2015 - Welcome

2015 Sensor Signal Processing for Defence, SSPD 2015

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2015 sensor signal processing for defence, sspd 2015 2015年

作者： Davies, Mike Chambers, Jonathon thomas, Paul

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sensor management for tracking smart targets

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DIGITAL signal processing 2009年第6期19卷 968-977页

作者： Savage, C. O. La Scala, B. F. Univ Melbourne Dept Elect & Elect Engn Melbourne Syst Lab Melbourne Vic 3010 Australia

We consider the problem of tracking a "smart" target. that is, a target that is aware it is being tracked and modifies its behaviour accordingly. To track such targets effectively it is necessary to modify the behaviour of the tracking sensor in response to the target. We consider that the best framework for such a problem is that of a mathematical game. In this paper, we consider an idealised version of this problem that illustrates some of the issues that can occur when attempting to track a smart target and the utility of a game theoretic framework. (C) 2007 Elsevier Inc. All rights reserved.

关键词： Game theory sensor management Target tracking

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