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检索条件"任意字段=10th Sensor Signal Processing for Defence Conference, SSPD 2021"

共 32 条记录，以下是1-10 订阅

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2021 sensor signal processing for defence conference, sspd 2021

2021 Sensor Signal Processing for Defence Conference, SSPD 2...

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10th sensor signal processing for defence conference, sspd 2021

ISBN: (纸本)9781665433143

the proceedings contain 21 papers. the topics discussed include: fast givens rotation approach to second order sequential best rotation algorithms;detection of human target location under simulated randomized rubble using global Fresnel’s reflection coefficient;the maximal eigengap estimator for acoustic vector-sensor processing;joint surface detection and depth estimation from single-photon lidar data using ensemble estimators;joint spatio-temporal bias estimation and tracking for GNSS-denied sensor networks;semi-supervised domain adaptation via adversarial training;target detection and recognition of ground penetrating radar using morphological image analysis and graph Laplacian regularization;and exponential filters for passive underwater acoustic detections - a global processing gain perspective.

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Exponential Filters for Passive Underwater Acoustic Detections - A Global processing Gain Perspective 10

Exponential Filters for Passive Underwater Acoustic Detectio...

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Blouin, Stephane Def R&D Canada DRDC Atlantic Res Ctr Dartmouth NS B2Y 3Z7 Canada

ISBN: (纸本)9781665433143

Multiple passive acoustic detectors exist for under/above-water applications. Implementations involving exponential filters have also been tested. However, the literature provides little guidance about their tuning, which may be non-intuitive. To alleviate this shortcoming, this publication reviews the key configuration parameters of a particular implementation of such a detector. Performance metrics are also proposed and their relationship to configuration parameters is established. the computation of the global processing gain is the main focus of this publication, and of interest to practitioners the impact of configuration parameters is studied. Examples demonstrate how the proposed closed-form expression adequately matches simulated results. Also, a novel and approximate output variance expression is proposed. Early results indicate the need for optimization among many performance metrics.

关键词： signal processing detection filter processing gain acoustics configuration

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Fast Givens Rotation Approach to Second Order Sequential Best Rotation Algorithms 10

Fast Givens Rotation Approach to Second Order Sequential Bes...

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Khattak, Faizan Weiss, Stephan Proudler, Ian K. Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland

ISBN: (纸本)9781665433143

the second order sequential best rotation (SBR2) algorithm is a popular algorithm to decompose a parahermitian matrix into approximate polynomial eigenvalues and eigenvectors. the work horse behind SBR2 is a Givens rotation interspersed by delay operations. In this paper, we investigate and analyse the application of a fast Givens rotation in order to reduce the computation complexity of SBR2. the proposed algorithm inherits the SBR2's proven convergence to a diagonalised and spectrally majorised solution for the polynomial eigenvalues. We provide some analysis and examples for the execution speed of this fast Givens-based SBR2 compared to a standard SBR2 implementation.

关键词： signal processing algorithms signal processing Approximation algorithms Cost function Eigenvalues and eigenfunctions sensors Delays

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Learning a Secondary Source From Compressive Measurements for Adaptive Projection Design 10

Learning a Secondary Source From Compressive Measurements fo...

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Coutts, Fraser K. thompson, John Mulgrew, Bernard Univ Edinburgh Inst Digital Commun Edinburgh EH9 3FG Scotland

ISBN: (纸本)9781665433143

Recent work has established that the gradient of the mutual information (MI) in Gaussian channels with input noise can be used for projection design in a compressive sensing (CS) scenario with two independent, Gaussian mixture (GM)-distributed inputs. the resulting CS projection matrices have been shown to be capable of controlling the input-output MI terms for the two inputs. One downside of such information theoretic strategies is their reliance on access to a priori knowledge of the input source statistics. In this paper, we assume that the GM distribution of a primary input is known and that the GM distribution of a secondary input is unknown. We derive a methodology for the online training of the distribution of the secondary input via compressive measurements and illustrate that once the distribution of this secondary source is known, we can use projection design to control the input-output MI of the system. We demonstrate through simulations the various performance trade-offs that exist within the proposed methodology.

关键词： Training Adaptation models Optimized production technology signal processing Mutual information Gaussian channels Compressed sensing

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Modelling bi-static uncertainties in sequential Monte Carlo with the GLMB model 10

Modelling bi-static uncertainties in sequential Monte Carlo ...

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Uney, Murat Narykov, Alexey Ralph, Jason Maskell, Simon Univ Liverpool Sch Elect Engn Elect & Comp Sci Liverpool L69 3BX England

ISBN: (纸本)9781665433143

Bi-static sensing, where the transmitter and receiver of sensors are separately located, underlies a wide range of collaborative sensing systems. Bi-static detections generally feature a signal time-of-flight (ToF) and an angle-of-arrival (AoA). the current practice in multi-object tracking uses the bi-static geometry to map these pairs onto a selected coordinate frame and filter the mapped detections with a noisy range-bearing (i.e., a mono-static) sensor model. However, the uncertainties in ToF-AoA pairs are not equivalently captured by this model, and the sensing geometry may result in significant degradation of the modelling accuracy. We introduce bi-static likelihood and false alarm models together with Monte Carlo (MC) computational methods to accurately capture the uncertainties involved and use them within Bayesian filtering. We demonstrate the efficacy of our proposed model in simulations with multiple objects using a sequential MC version of the generalised labelled multi-Bernoulli (GLMB) track filter. We compare the filtering performance with the conventional approximation mentioned above.

关键词： Geometry Uncertainty Monte Carlo methods Filtering Transmitters Computational modeling signal processing

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the Maximal Eigengap Estimator for Acoustic Vector-sensor processing 10

The Maximal Eigengap Estimator for Acoustic Vector-Sensor Pr...

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Bassett, Robert Foster, Jacob Gemba, Kay L. Leary, Paul Smith, Kevin B. Naval Postgraduate Sch Operat Res Dept Monterey CA 93943 USA US Naval Res Lab Acoust Div Code 7160 Washington DC 20375 USA Naval Postgrad Sch Phys Dept Monterey CA 93943 USA

ISBN: (纸本)9781665433143

this paper introduces the maximal eigengap estimator for finding the direction of arrival of a wideband acoustic signal using a single vector-sensor. We show that in this setting narrowband cross-spectral density matrices can be combined in an optimal weighting that approximately maximizes signal-to-noise ratio across a wide frequency band. the signal subspace resulting from this optimal combination of narrowband power matrices defines the maximal eigengap estimator. We discuss the advantages of the maximal eigengap estimator over competing methods, and demonstrate its utility in a real-data application using signals collected in 2019 from an acoustic vector-sensor deployed in the Monterey Bay.

关键词： direction of arrival acoustic vector-sensor signal subspace eigengap

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Detection of Weak Transient signals Using a Broadband Subspace Approach 10

Detection of Weak Transient Signals Using a Broadband Subspa...

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Weiss, Stephan Delaosa, Connor Matthews, James Proudler, Ian K. Jackson, Ben A. Univ Strathclyde Dept Elect Elect Engn Glasgow G1 1XW Scotland PA Consulting Global Innovat & Technol Ctr Cambridge Melbourn SG8 6DP England UK Def Sci & Technol Lab Dstl Salisbury SP4 0JQ England

ISBN: (纸本)9781665433143

We investigate the detection of broadband weak transient signals by monitoring a projection of the measurement data onto the noise-only subspace derived from the stationary sources. this projection utilises a broadband subspace decomposition of the data's space-time covariance matrix. the energy in this projected 'syndrome' vector is more discriminative towards the presence or absence of a transient signal than the original data, and can be enhanced by temporal averaging. We investigate the statistics, and indicate in simulations how discrimination can be traded off with the time to reach a decision, as well as with the sample size over which the space-time covariance is estimated.

关键词： Correlation Computational modeling signal processing Broadband communication Reliability Noise measurement Transient analysis

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Rate Splitting Multiple Access for Multi-Antenna Multi-Carrier Joint Communications and Jamming 10

Rate Splitting Multiple Access for Multi-Antenna Multi-Carri...

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Dizdar, Onur Clerckx, Bruno Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (纸本)9781665433143

Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for downlink multi-antenna wireless networks. In this work, we investigate a novel application of RSMA for joint communications and jamming with a Multi-Carrier (MC) waveform in Multiple Input Single Output (MISO) Broadcast Channel (BC). Our aim is to simultaneously communicate with Information Users (IU) and jam Adversarial Users (AU) to disrupt their communications in a setting where all users perform broadband communications by MC waveforms in their respective networks. We consider the practical setting of imperfect CSI at transmitter (CSIT) for the IUs and statistical CSIT for AUs. the optimal information and jamming precoders are designed to maximize the sum-rate under jamming power constraints on the pilot subcarriers of AUs, a jamming method considered to be among the most destructive methods for MC waveforms under the considered system model. We compare the sum-rate performance of RSMA and Space Division Multiple Access (SDMA) schemes by numerical results to demonstrate that RSMA achieves a significant sumrate gain compared to SDMA.

关键词： Gold Transmitters Wireless networks Simulation signal processing Numerical models Multiaccess communication

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Adaptive Kernel Kalman Filter 10

Adaptive Kernel Kalman Filter

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Sun, Mengwei Davies, Mike E. Proudler, Ian Hopgood, James R. Univ Edinburgh Inst Digital Commun Edinburgh EH9 3FG Scotland Univ Strathclyde Dept Elect & Elect Engn Ctr Signal & Image Proc CeSIP Glasgow G1 1XW Scotland

ISBN: (纸本)9781665433143

this paper presents a novel model-based Bayesian filter called the adaptive kernel Kalman filter (AKKF). the proposed filter approximates the arbitrary probability distribution functions (PDFs) of hidden states as empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). Specifically, particles are generated and updated in the data space to capture the properties of the dynamical system model, while the corresponding kernel weight vector and matrix associated with the particles' feature mappings are predicted and updated in the RKHS based on the kernel Kalman rule (KKR). We illustrate and confirm the advantages of our approach through simulation, offering detailed comparison with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF) algorithms.

关键词： Adaptive kernel Kalman filter kernel Kalman rule kernel mean embedding non-linear Bayesian filter

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Joint Spatio-Temporal Bias Estimation and Tracking for GNSS-Denied sensor Networks 10

Joint Spatio-Temporal Bias Estimation and Tracking for GNSS-...

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10th conference of the sensor-signal-processing-for-defence (sspd)

作者： Macdonald, Sofie Hopgood, James R. Univ Edinburgh Inst Digital Commun Sch Engn Edinburgh Scotland

ISBN: (纸本)9781665433143

sensor parameter estimation is a key process that must be considered when performing data fusion in a multisensor object tracking scenario. For example, significant relative time delays in sensor data arriving at a fusion centre can result in a reduction of track accuracy, false tracks, or early termination of a true object track. the same issues may arise in the presence of some relative angular bias between sensors. this article presents a technique for simultaneous target tracking and estimation of relative time delays and angular biases in data for a multi-sensor system with no access to a global frame of reference. the proposed technique makes use of a hierarchical Bayesian model and couples a grid-based search method with an array of augmented state Kalman filters to accomplish this. Results are provided comparing the root-mean-squared error in a simulated single object tracking scenario. the performance of a single sensor, two sensors with correct registration, two sensors with incorrect registration, and two sensors with registration correction are compared. the results demonstrate a significant improvement in tracking performance when registration errors are corrected with the proposed method, as well as an increase in accuracy over object tracking with only a single sensor.

关键词： hierarchical Bayesian model spatio-temporal alignment single object tracking GNSS-denied bias estimation Kalman filter grid-based search

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