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检索条件"任意字段=12th Sensor Signal Processing for Defence Conference, SSPD 2023"

共 21 条记录，以下是1-10 订阅

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2023 sensor signal processing for defence conference, sspd 2023

2023 Sensor Signal Processing for Defence Conference, SSPD 2...

引用

12th sensor signal processing for defence conference, sspd 2023

ISBN: (纸本)9798350337327

the proceedings contain 20 papers. the topics discussed include: association based feedback aided underwater passive target tracking;implementation of adaptive kernel Kalman filter in stone soup;simulation of anisoplanatic turbulence for images and videos;consensus-based distributed variational multi-object tracker in multi-sensor network;computational enhancement of accumulated CA-CFAR process in side scan sonar data;generalized sequential matrix diagonalization for the SVD of polynomial matrices;a novel adaptive architecture: joint multi-targets detection and clutter classification;DB-Drift: concept drift aware density-based anomaly detection for maritime trajectories;and adaptive kernel Kalman filter for magnetic anomaly detection-based metallic target tracking.

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Consensus-based Distributed Variational Multi-object Tracker in Multi-sensor Network 12

Consensus-based Distributed Variational Multi-object Tracker...

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12th sensor signal processing for defence conference (sspd)

作者： Li, Qing Gan, Runze Godsill, Simon Univ Cambridge Dept Engn Cambridge England

ISBN: (纸本)9798350337327

the growing need for accurate and reliable tracking systems has driven significant progress in sensor fusion and object tracking techniques. In this paper, we design two variational Bayesian trackers that effectively track multiple targets in cluttered environments within a sensor network. We first present a centralised sensor fusion scheme, which involves transmitting sensor data to a fusion center. then, we develop a distributed version leveraging the average consensus algorithm, which is theoretically equivalent to the centralised sensor fusion tracker and requires only local message passing with neighbouring sensors. In addition, we empirically verify that our proposed distributed variational tracker performs on par with the centralised version with equal tracking accuracy. Simulation results show that our distributed multi-target tracker outperforms the suboptimal distributed sensor fusion strategy that fuses each sensor's posterior based on arithmetic sensor fusion and an average consensus strategy.

关键词： distributed sensor fusion multiple object tracking variational inference average consensus data association

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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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Simulation of Anisoplanatic Turbulence for Images and Videos 12

Simulation of Anisoplanatic Turbulence for Images and Videos

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12th sensor signal processing for defence conference (sspd)

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

ISBN: (纸本)9798350337327

Turbulence is a common phenomenon in the atmosphere and can generate a variety of distortions in an image. this can cause further image processing tasks to struggle due to lack of detail in the resulting turbulence affected imagery. It is therefore useful to attempt to remove such distortions as a post processing step. However, the development of such algorithms is difficult due to the complex nature of turbulence data acquisition. To alleviate these issues, this paper presents the development of a turbulence simulator that is capable of imparting the effects of a turbulent atmosphere onto clean images and videos. this work also provides a large, publicly available dataset that can be used as a benchmark. the simulator and dataset will be valuable resources in the field of turbulence mitigation. Indeed, the simulator allows researchers to simulate specific turbulent conditions for any application as required;while the dataset provides the ability to make use of turbulent data without the expensive time commitment of simulation.

关键词： Atmospheric Turbulence Simulation Anisoplanatic Wave propagation Dataset

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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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Joint sensor Scheduling and Target Tracking with Efficient Bayesian Optimisation 12

Joint Sensor Scheduling and Target Tracking with Efficient B...

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12th sensor signal processing for defence conference (sspd)

作者： Liu, Xingchi Lyu, Chenyi Soleymani, Seyed Ahmad Wang, Wenwu Mihaylova, Lyudmila Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford Surrey England

ISBN: (纸本)9798350337327

the received signal strength measurement has been widely used in search and tracking applications and its benefit is linked with the distance between the transmitter and receiver. this paper proposes an online Bayesian optimisation-based approach that relies on signal strength measurements to schedule multiple sensors for searching and tracking a moving target, without any prior knowledge of the target's state or motion model. A unique contribution lies in incorporating the Gaussian processes factorisation method into the Bayesian optimisation framework, which enhances the effectiveness of the proposed approach. Numerical results obtained from different sizes of measurements demonstrate that the proposed approach can efficiently schedule two unmanned aerial vehicles. Particularly, it achieves at most 21% lower computational time for deciding measurement locations and 79% lower time for updating the surrogate model as compared to the benchmark approach.

关键词： Active sensing Bayesian optimisation factorised Gaussian process target tracking sensor management unmanned aerial vehicles hierarchical off-diagonal low-rank (HODLR) factorisation

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Adaptive Kernel Kalman Filter for Magnetic Anomaly Detection-based Metallic Target Tracking 12

Adaptive Kernel Kalman Filter for Magnetic Anomaly Detection...

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12th sensor signal processing for defence conference (sspd)

作者： Sun, Mengwei Hodgskin-Brown, Richard Davies, Mike E. Proudler, Ian K. Hopgood, James R. Univ Edinburgh Inst Digital Commun Edinburgh EH9 3FG Midlothian Scotland Univ Strathclyde Dept Elect & Elect Engn Ctr Signal & Image Proc CeSIP Glasgow G1 1XW Lanark Scotland Univ Manchester Dept Elect & Elect Engn Electromagnet Sensing Grp Manchester M13 9PL Lancs England

ISBN: (纸本)9798350337327

this paper proposes the use of the adaptive kernel Kalman filter (AKKF) to track metallic targets using magnetic anomaly detection (MAD). the proposed AKKF-based approach enables accurate tracking of moving metallic targets using magnetometer sensors, even in the presence of dynamic and unknown magnetic moments. the experimental results demonstrate that the proposed method exhibits favourable tracking and estimation performance with reduced computational complexity compared with the bootstrap particle filter (PF). For example, in magnetic moment strength estimation, the relative root mean square error (RRMSE) of the proposed algorithm using 50 particles can approach 2.5% with a computation time of 0.18 seconds, whereas the RRMSE of the PF using 2000 particles is 4.5% with a computation time of 1.4 seconds. this study highlights the potential of AKKF in MAD for metallic target tracking using magnetometer sensors.

关键词： Adaptive kernel Kalman filter magnetic anomaly detection metallic target tracking

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JOINT LEARNING WIth SHARED LATENT SPACE FOR SELF-SUPERVISED MONAURAL SPEECH ENHANCEMENT 12

JOINT LEARNING WITH SHARED LATENT SPACE FOR SELF-SUPERVISED ...

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12th sensor signal processing for defence conference (sspd)

作者： Li, Yi Sun, Yang Wang, Wenwu Naqvi, Syed Mohsen Newcastle Univ Intelligent Sensing & Commun Res Grp Newcastle Upon Tyne Tyne & Wear England Univ Oxford Big Data Inst Oxford England Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9798350337327

Supervised learning has been used to solve monaural speech enhancement problem, offering state-of-the-art performance. However, clean training data is difficult or expensive to obtain in real room environments, which limits the training of supervised learning-based methods. In addition, mismatch conditions e.g., noises in the testing stages may be unseen in the training stage, present a common challenge. In this paper, we propose a self-supervised learning-based monaural speech enhancement method, using two autoencoders i.e., the speech autoencoder (SAE) and mixture autoencoder (MAE), with a shared layer, which help to mitigate mismatch conditions by learning a shared latent space between speech and mixture. To further improve the enhancement performance, we also propose phase-aware training and multi-resolution spectral losses. the latent representations of the amplitude and phase are independently learned in two decoders of the proposed SAE with only a very limited set of clean speech signals. Moreover, multi-resolution spectral losses help extract rich feature information. Experimental results on a benchmark dataset demonstrate that the proposed method outperforms the state-of-the-art self-supervised and supervised approaches. the source code is available at https://***/Yukino-3/Complex-SSL-SE.(1)

关键词： monaural speech enhancement self-supervised learning multi-resolution spectral losses phase-aware joint training

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Multisensor fusion for compiling battlespace tactical picture

Multisensor fusion for compiling battlespace tactical pictur...

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IEEE 12th signal processing and Communications Applications conference

作者： Sari, F Sari, N BTAE MAM Tubitak TR-41470 Gebze Kocaeli Turkey

ISBN: (纸本)0780383184

this paper presents a modeling and simulation approach for a multisensor data fusion system to obtain a common tactical picture in defence survelliance applications. We explore an architecture for building a more accurate tactical picture. In addition it combines evidence to determine platform position, velocity and identity parameters. One of the advantages in our method is its ability to effectively combine the processing of position and velocity information with the processing of Class (vehicle type), ID (specific vehicle information) and thread type (friend/hostile) information. We define two types of fusion algorithms. the first one is based on the nearest neighbor clustering techniques for data association to determine more accurate position and velocity of targets. the second technique is the identity fusion based on the fuzzy rule based classifier.

关键词： sensor data fusion

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Bayesian Estimation of A Periodically-Releasing Biochemical Source Using sensor Networks 12

Bayesian Estimation of A Periodically-Releasing Biochemical ...

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UKACC 12th International conference on Control (CONTROL)

作者： Hu, Liang Su, Jinya Hutchinson, Michael Liu, Cunjia Chen, Wen-Hua De Montfort Univ Sch Comp Sci & Informat Leicester Leics England Loughborough Univ Dept Aeronaut & Automot Engn Loughborough Leics England

ISBN: (纸本)9781538628645

this paper develops a Bayesian estimation method to estimate source parameters of a biochemical source using a network of sensors. Based on existing models of continuous and instantaneous releases, a model of discrete and periodic releases is proposed, which has extra parameters such as the time interval between two successive releases. Different from existing source term estimation methods, based on the sensor characteristic of chemical sensors, the zero readings of sensors are exploited in our algorithm where the zero readings may be caused by the concentration being below the threshold of the sensors. Two types of Bayesian inference algorithms for key parameters of the sources are developed and their particle filtering implementation is discussed. the efficiency of the proposed algorithms for periodic release is demonstrated and verified by simulation where the algorithm with the exploitation of the zero readings significantly outperforms that without.

关键词： Source-term estimation Bayesian estimation Atmospheric dispersion model sensor networks

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