2015 Sensor Signal Processing for Defence (SSPD)最新文献_第2页

Removing Speckle Noise by Analysis Dictionary Learning 通过分析字典学习去除斑点噪声

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-10-05 DOI: 10.1109/SSPD.2015.7288521

Jing Dong, Wenwu Wang, J. Chambers

引用次数: 3

Sparsity Based Ground Moving Target Imaging via Multi-Channel SAR 基于稀疏度的多通道SAR地面运动目标成像

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-10-05 DOI: 10.1109/SSPD.2015.7288524

Di Wu, Mehrdad Yaghoobi, M. Davies

引用次数: 5

Fusion of Radar and Secondary Sensor Data Using Kinematic Models of Multiple Simultaneous Targets 基于多目标同步运动模型的雷达与二次传感器数据融合

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-10-05 DOI: 10.1109/SSPD.2015.7288504

B. Karlsen, E. Nielsen, Morten T. Pedersen

引用次数: 1

Joint Navigation and Synchronization Using SOOP in GPS-Denied Environments: Algorithm and Empirical Study gps拒绝环境下基于SOOP的联合导航与同步:算法与实证研究

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-10-05 DOI: 10.1109/SSPD.2015.7288507

M. Leng, S. G. Razul, C. See, Wee Peng Tay, Chi Cheng, F. Quitin

引用次数: 6

Observing the Dynamics of Waterborne Pathogens for Assessing the Level of Contamination 观察水媒病原菌动态以评估污染水平

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-10-05 DOI: 10.1109/SSPD.2015.7288514

Isabella McKenna, F. Tonolini, Rachael Tobin, J. Houssineau, H. Bridle, C. McDougall, Isabel Schlangen, J. McGrath, M. Jimenez, Daniel E. Clark

引用次数: 2

Distributed Implementation for Person Re-Identification 人员再识别的分布式实现

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-10-05 DOI: 10.1109/SSPD.2015.7288501

S. Sthapit, J. Thompson, J. Hopgood, N. Robertson

{"title":"Distributed Implementation for Person Re-Identification","authors":"S. Sthapit, J. Thompson, J. Hopgood, N. Robertson","doi":"10.1109/SSPD.2015.7288501","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288501","url":null,"abstract":"Person re-identification is to associate people across different camera views at different locations and time. Current computer vision algorithms on person re-identification mainly focus on performance, making it unsuitable for distributed systems. For distributed system, computational complexity, network usage, energy consumption and memory requirement are as important as the performance. In this paper, we compare the merits of the current algorithms. We consider three key algorithms Keep It Simple and Straightforward MEtric (KISSME), Symmetry-Driven Accumulation of Local Features (SDALF) and Unsupervised Saliency Matching (USM). The advantage of SDALF, and USM is that they are unsupervised methods so training is not required but computationally many time expensive than KISSME. Saliency based method is superior in performance but also has the longest feature length. As the feature needs to be transmitted from one camera to other in distributed system, this mean higher energy consumption and longer time delay. Among these three, KISSME offers a balance between performance, complexity and feature lengths hence more suitable for distributed systems.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116023499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Shortening of Paraunitary Matrices Obtained by Polynomial Eigenvalue Decomposition Algorithms 由多项式特征值分解算法得到的拟酉矩阵的缩短

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-09-11 DOI: 10.1109/SSPD.2015.7288523

J. Corr, K. Thompson, Stephan Weiss, I. Proudler, J. McWhirter

引用次数: 22

Micro-Doppler Based Recognition of Ballistic Targets Using 2D Gabor Filters 基于二维Gabor滤波器的微多普勒弹道目标识别

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-09-09 DOI: 10.1109/SSPD.2015.7288512

A. Persico, C. Clemente, C. Ilioudis, D. Gaglione, Jianlin Cao, J. Soraghan

引用次数: 10

Fractional Cosine Transform (FRCT)-Turbo Based OFDM for Underwater Acoustic Communication 基于分数阶余弦变换-Turbo的水声通信OFDM

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-09-09 DOI: 10.1109/SSPD.2015.7288503

Yixin Chen, Carmine Clamente, J. Soraghan, Stephan Weiss

引用次数: 8

Normalised Multi-Stage Clustering Equaliser For Underwater Acoustic Channels 用于水声信道的归一化多级聚类均衡器

2015 Sensor Signal Processing for Defence (SSPD) Pub Date : 2015-09-01 DOI: 10.1109/SSPD.2015.7288513

R. Mitra, V. Bhatia

{"title":"Normalised Multi-Stage Clustering Equaliser For Underwater Acoustic Channels","authors":"R. Mitra, V. Bhatia","doi":"10.1109/SSPD.2015.7288513","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288513","url":null,"abstract":"Underwater communications systems are being increasingly used in defence, security service, oil exploration, ocean science and in many other applications. The underwater acoustic channel is characterised by the large delay spread, Doppler shifts, limited bandwidths and time variability. The channel is also affected by additive impulsive noise, which makes the underwater communication even more challenging. Since the channel and noise characteristics vary immensely, an adaptive equaliser at the communications receiver forms a viable solution for increasing the bit error rate of the communication link. The adaptive multistage clustering based equaliser is one such solution which provides high throughput. However, the performance of the multistage clustering equaliser degrades in the presence of impulsive noise. To improve the throughput and robustness, we propose an adaptive normalised multistage clustering based blind equaliser for underwater acoustic channel. From simulation results, it is observed that the proposed algorithm has better convergence and symbol error rate performance. Convergence analysis of the proposed algorithm is also presented in the paper.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129183591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1