2019 22th International Conference on Information Fusion (FUSION)最新文献_第8页

How to Calibrate your Enemy's Capabilities? Inverse Filtering for Counter-Autonomous Systems 如何校准敌人的能力?反自治系统的逆滤波

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011232

V. Krishnamurthy, M. Rangaswamy

{"title":"How to Calibrate your Enemy's Capabilities? Inverse Filtering for Counter-Autonomous Systems","authors":"V. Krishnamurthy, M. Rangaswamy","doi":"10.23919/fusion43075.2019.9011232","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011232","url":null,"abstract":"We consider the following adversarial Bayesian signal processing problem involving “us” and the “enemy”: an enemy observes our state in noise; updates its posterior distribution of the state and then chooses an action based on this posterior. Given knowledge of “our” state and sequence of enemy's actions observed in noise, we consider two problems: (i) How can the enemy's posterior distribution be estimated? Estimating the posterior is an inverse filtering problem involving a random measure - we formulate and solve several versions of this problem in a Bayesian setting. (ii) How can the enemy's observation likelihood be estimated? This tells us how accurate the enemy's sensors are. We compute the maximum likelihood estimator for the enemy's observation likelihood given our measurements of the enemy's actions where the enemy's actions are in response to estimating our state. The above questions are motivated by the design of counter-autonomous systems: given measurements of the actions of a sophisticated autonomous enemy, how can a counter-autonomous system estimate the underlying belief of the enemy, predict future actions and therefore guard against these actions.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126433501","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

On combining probabilistic and semantic similarity-based methods toward off-domain reasoning for situational awareness 结合基于概率和语义相似度的情境感知离域推理方法研究

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011197

Van Nguyen

{"title":"On combining probabilistic and semantic similarity-based methods toward off-domain reasoning for situational awareness","authors":"Van Nguyen","doi":"10.23919/fusion43075.2019.9011197","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011197","url":null,"abstract":"Hard and soft high-level fusion plays an important role in the situational awareness literature. To deal with complex real-world situations, it is highly desirable that such systems are able to effectively capture the rich semantics associated with soft information and world/domain knowledge, to efficiently reason with imperfect information, and to benefit and learn from any data that may be available, among others. In these respects, first-order probabilistic models, such as Markov Logic Networks, hold great promise and have received recent attention for high-level fusion. By combining the expressiveness of first-order logic and probabilistic graphical models, such models are able to facilitate representation, reasoning and learning with complex relational information and rich probabilistic structure within a unifying framework. However, first-order probabilistic models may face various challenges in dealing with real-world situational awareness, including scalability of reasoning, learning and knowledge base construction, and robustness in open worlds. In this paper, we motivate a new and pragmatic approach toward collectively addressing these concerns; that is, endowing high-level fusion systems a capability to perform off-domain reasoning, through the ability to reason about unknown/unmodelled concepts. In particular, we will discuss how such an approach could be achieved by means of combining probabilistic and semantic similarity-based methods. We will also explore the potential contribution of semantic similarity measures derived from both taxonomic knowledge (e.g., ontologies) and distributional semantic models (generated from text corpora) toward achieving this goal.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127977090","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: 4

Unsupervised Bayesian Estimation and Tracking of Time-Varying Convolutive Multichannel Systems 时变卷积多通道系统的无监督贝叶斯估计与跟踪

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011190

H. Buchner, Karim Helwani, S. Godsill

{"title":"Unsupervised Bayesian Estimation and Tracking of Time-Varying Convolutive Multichannel Systems","authors":"H. Buchner, Karim Helwani, S. Godsill","doi":"10.23919/fusion43075.2019.9011190","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011190","url":null,"abstract":"In this paper we focus on Bayesian blind and semi-blind adaptive signal processing based on a broadband MIMO FIR model (e.g., for blind source separation (BSS) and blind system identification (BSI)). Specifically, we study in this paper a framework allowing us to systematically incorporate various types of prior knowledge: (1) source signal statistics, (2) deterministic knowledge on the mixing system, and (3) stochastic knowledge on the mixing system. In order to exploit all possible types of source signal statistics (1), our considerations are based on TRINICON, a previously introduced generic framework for broadband blind (and semi-blind) adaptive MIMO signal processing. The motivation for this paper is threefold: (a) the extension of TRINICON to Bayesian point estimation to address (3) in addition to (1), and (b) more specifically to unify system-based blind adaptive MIMO signal processing with the tracking of time-varying scenarios, and finally (c) to show how the Bayesian TRINICON-based tracking can be formulated as a sequence estimation approach on arbitrary partly smooth manifolds. As we will see in this paper, the Bayesian approach to incorporate stochastic priors and the manifold learning approach to exploit deterministic system knowledge (2) complement one another very efficiently in the context of TRINICON.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128760450","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

A Rollout Based Path Planner for Emitter Localization 基于铺展的发射器定位路径规划器

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011368

Folker Hoffmann, Hans Schily, A. Charlish, M. Ritchie, H. Griffiths

引用次数: 7

Distributed Multiple Hypothesis Tracking in Finite Point Process Formalism: A Simple Two Station Case 有限点过程形式中的分布式多假设跟踪:一个简单的两站情况

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011284

S. Mori, C. Chong, K. Chang

引用次数: 0

Non-Euclidean Kalman Filters for Nonlinear Measurements 非线性测量的非欧几里得卡尔曼滤波

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011350

Samuel A. Shapero, P. Miceli

引用次数: 0

Distributed Learning over Time-Varying Graphs with Adversarial Agents 基于对抗代理的时变图分布式学习

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011353

P. Vyavahare, Lili Su, N. Vaidya

{"title":"Distributed Learning over Time-Varying Graphs with Adversarial Agents","authors":"P. Vyavahare, Lili Su, N. Vaidya","doi":"10.23919/fusion43075.2019.9011353","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011353","url":null,"abstract":"In this work, we study the problem of non-Bayesian learning in time-varying (dynamic) networks when there are some adversarial (faulty) agents in the network. The set of faulty agents is fixed across time. The connectivity graph of the network is changing at each time step and is unknown to the agents. In every time step, each non-faulty agent collects partial information about an unknown state of the environment. Each non-faulty agent tries to estimate the true state of the environment by iteratively sharing information with its neighbors at each time step. We first present an analysis of a distributed algorithm in static communication network with faulty agents which does not require the network to achieve consensus. Existing algorithms in this setting require that all non-faulty agents in the network should be able to achieve consensus via local information exchange. We then extend this analysis to dynamic networks with relaxed network condition. We show that if every non-faulty agent can receive enough information (via iteratively communicating with neighbors) to differentiate the true state of the world from other possible states then it can indeed learn the true state.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131804140","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: 5

Fusion within a Detection System Family 探测系统家族中的融合

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011205

M. Oxley, Christine M. Schubert-Kabban

引用次数: 0

Adaptive Diffusion for Distributed Optimization over Multi-Agent Network with Random Delays (Poster) 随机延迟多智能体网络的自适应扩散分布优化(Poster)

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011172

Yi Qiu, Jie Zhou

引用次数: 0

SLAM Based on Double Layer Cubature Kalman Filter 基于双层培养卡尔曼滤波的SLAM

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011369

Feng Yang, Bo Jin, Mengting Yan, Yujuan Luo

引用次数: 0