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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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IoT defence: An Internet Based Remote Area Monitoring and Control System 5

IoT Defence: An Internet Based Remote Area Monitoring and Co...

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Degadwala, Sheshang Musa, Sabith Ahamed Vyas, Dhairya Mitra, Pooja Sigma Institute of Engineering Head of Computer Engineering Department Gujarat Vadodara India Computer Engineering Gujarat Technological University India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Computer Engineering Gujarat Technological University India

ISBN: (纸本)9781665435246

the objective of this study is to develop an automated security system for defenses operations that can identify, track, and eliminate targets. the system has two modes of operation, the first of which uses the Arduino programming system to track the target automatically. the module, by the other hand, can be operated manually, in which case the authority picks the target and, if required, shoots. Python-based image processing methods are implemented using Anaconda Spyder. the procedure starts with computer processing of image sequence signal acquired by the Arduino image capture device, then choosing a mark object which can be tracked using various computer vision scrips. Following the marking of the object, and Arduino circuit decides to fire any unlawful individual or movement inside the subject range using PIR sensor. the gun is supported by a stand and things crusade is meticulous by a servo mechanical. © 2021 IEEE.

关键词： Internet of things

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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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IoT defence: An Internet Based Remote Area Monitoring and Control System

IoT Defence: An Internet Based Remote Area Monitoring and Co...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Sheshang Degadwala Sabith Ahamed Musa Dhairya Vyas Pooja Mitra Sigma Institute of Engineering Vadodara Gujarat Computer Engineering Gujarat Technological University India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India

ISBN: (纸本)9781665435253

关键词： Target tracking Firing Lasers Programming Transceivers Security Object recognition

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Head Motion Classification for Single-Accelerometer Virtual Reality Hardware 5

Head Motion Classification for Single-Accelerometer Virtual ...

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5th International Conference on Frontiers of signal processing (ICFSP)

作者： Hachaj, Tomasz Ogiela, Marek R. Pedag Univ Krakow Inst Comp Sci Krakow Poland AGH Univ Sci & Technol Cryptog & Cognit Informat Res Grp Krakow Poland

ISBN: (纸本)9781728152585

Head motions classification applied to virtual reality (VR) systems is still an open problem without a leading pattern recognition solution. In contrary to typical motion capture pattern recognition problem in this case we use only single inertial measurement unit (IMU) sensor. Head motions that we want to recognize in VR systems might be both natural head motions like nodding or shaking head (they might be used while interacting with VR avatars) and also elements of head-based navigation system or interface. the second type of actions is more challenging because it might contains actions that generate motion trajectories that do not appear in real-life, though they have to be possible to execute only by using a head. In this paper we propose a trajectory-based motion features description that is utilized by dynamic time warping (DTW) classificator. the training of the classificator requires using modified dynamic time warping barycenter averaging (DBA) heuristic algorithm which utilizes quaternions to represents rotations. the proposed pattern recognition system together with its evaluation on the set of head motions acquired by VR system is our original contribution. We have evaluated our method on dataset consisted of 8 types of motions performed by two persons (there are 160 motions samples). In leave-one-out evaluation we have obtained very good results: only 10% of one and 15% of another action has been incorrectly classified, while remaining 6 actions classes had been 100% correctly classified. Both dataset and implementation of proposed method can be downloaded, due to this our experiment can be reproduced.

关键词： Head motion analysis signal processing virtual reality glasses dynamic time warping quaternions

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Monitoring respiration using the pressure sensors in a dialysis machine

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PHYSIOLOGICAL MEASUREMENT 2019年第2期40卷 025001-025001页

作者： Sandberg, Frida Holmer, Mattias Olde, Bo Lund Univ Dept Biomed Engn Lund Sweden Baxter Int Inc Renal Innovat Dept Lund Sweden

Objective: Although respiratory problems are common among patients with end-stage renal disease, respiration is not continuously monitored during dialysis. the purpose of the present study is to investigate the feasibility of monitoring respiration using the pressure sensors of the dialysis machine. Approach: Respiration induces variations in the blood pressure that propagates to the extracorporeal circuit of the dialysis machine. However, the magnitude of these variations are very small compared to pressure variations induced by the dialysis machine. We propose a new method, which involves adaptive template subtraction and peak conditioned spectral averaging, to estimate respiration rate from the pressure sensor signals. Using this method, an estimate of the respiration rate is obtained every 5th second provided that the signal quality is sufficient. the method is evaluated for continuous monitoring of respiration rate in nine dialysis treatment sessions. Main results: the median absolute deviation between the estimated respiration rate from the pressure sensor signals and a reference capnography recording was 0.02 Hz (1.3 breaths per min). Significance: Our results suggest that continuous monitoring of respiration using the pressure sensors of the dialysis machine is feasible. the main advantage with such monitoring is that no additional sensors are required which may cause patient discomfort.

关键词： hemodialysis respiration extracorporeal pressure sensors signal processing

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2015 sensor signal processing for defence, sspd 2015

2015 Sensor Signal Processing for Defence, SSPD 2015

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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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