Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841396
Á. F. García-Fernández, J. Ralph, P. Horridge, S. Maskell
This paper presents a Gaussian implementation of the Poisson multi-Bernoulli mixture (PMBM) filter for sets of trajectories with non-linear/non-Gaussian measurements. In this filter, the single-trajectory densities are Gaussian and their updates are performed using the iterated posterior linearisation technique, applied on the current target state. With this approach, we first compute the posterior distribution of the current target state by iteratively refining the linear approximation of the measurement, and the resulting mean square error of the linearisation, based on our current guess of the posterior distribution. After obtaining a Gaussian approximation of the current target state, the distribution of the past states of the trajectory can be obtained in closed form. Via numerical simulations, we compare different algorithms to approximate the single-trajectory posteriors and normalising constants for trajectory PMBM and trajectory Poisson multi-Bernoulli filters.
{"title":"Gaussian trajectory PMBM filter with nonlinear measurements based on posterior linearisation","authors":"Á. F. García-Fernández, J. Ralph, P. Horridge, S. Maskell","doi":"10.23919/fusion49751.2022.9841396","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841396","url":null,"abstract":"This paper presents a Gaussian implementation of the Poisson multi-Bernoulli mixture (PMBM) filter for sets of trajectories with non-linear/non-Gaussian measurements. In this filter, the single-trajectory densities are Gaussian and their updates are performed using the iterated posterior linearisation technique, applied on the current target state. With this approach, we first compute the posterior distribution of the current target state by iteratively refining the linear approximation of the measurement, and the resulting mean square error of the linearisation, based on our current guess of the posterior distribution. After obtaining a Gaussian approximation of the current target state, the distribution of the past states of the trajectory can be obtained in closed form. Via numerical simulations, we compare different algorithms to approximate the single-trajectory posteriors and normalising constants for trajectory PMBM and trajectory Poisson multi-Bernoulli filters.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123805906","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841301
P. Kowalski, A. Jousselme
Hybrid threat events are rare and cannot be modelled solely based on data. Instead they require a focus on discovery of emergent knowledge through information sharing across agencies and systems. That requires a shared conceptualisation of the problem and entities involved. It also means that uncertain and possibly conflicting information describing multiple entities and their relationships needs to be reasoned about. In this paper we discuss the relationship between uncertain conceptual graphs and belief functions. We put forward a fusion process which allows for taking advantage of evidential reasoning capabilities in a multi-entity context. We show how information from conceptual graphs can be fed into or represented as an evidential networks and how the inference results obtained from valuation networks can be used to generate a probability distribution on conceptual graphs. This is demonstrated on a multi-entity threat assessment situation where a hybrid threat is formed by several possibly cooperating vessels.
{"title":"Reasoning with conceptual graphs and evidential networks for multi-entity maritime threat assessment","authors":"P. Kowalski, A. Jousselme","doi":"10.23919/fusion49751.2022.9841301","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841301","url":null,"abstract":"Hybrid threat events are rare and cannot be modelled solely based on data. Instead they require a focus on discovery of emergent knowledge through information sharing across agencies and systems. That requires a shared conceptualisation of the problem and entities involved. It also means that uncertain and possibly conflicting information describing multiple entities and their relationships needs to be reasoned about. In this paper we discuss the relationship between uncertain conceptual graphs and belief functions. We put forward a fusion process which allows for taking advantage of evidential reasoning capabilities in a multi-entity context. We show how information from conceptual graphs can be fed into or represented as an evidential networks and how the inference results obtained from valuation networks can be used to generate a probability distribution on conceptual graphs. This is demonstrated on a multi-entity threat assessment situation where a hybrid threat is formed by several possibly cooperating vessels.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128178325","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841283
Linfeng Xu, Zonglin Hou, M. Mallick, Yang-wang Fang
For the challenging task of modeling actual complex motions, we propose a new class of Gaussian process (GP) models that are data-driven and also take into account prior knowledge of the motion intention. As a theoretical basis, we show that the GP regression is mathematically equivalent to regularized least-squares estimation for random functions with known prior means. Compared with the popular GP models in machine learning literature, the proposed GP motion model priors with conditional kernels have at least two advantages: 1) they are nonstationary and more applicable to represent complex motions by integrating the basic kinematic principles; 2) conditional kernels are further devised by incorporating the motion intent so that the resultant GP models are more versatile and would expectedly entail more accurate trajectory prediction. A superior property of the GP models with conditional kernels is found to improve the computational efficiency. Finally, illustrative examples are provided to show the superiority of the proposed motion models and to verify the theoretical results given in the paper.
{"title":"Intention-Aware Motion Modeling Using GP Priors With Conditional Kernels","authors":"Linfeng Xu, Zonglin Hou, M. Mallick, Yang-wang Fang","doi":"10.23919/fusion49751.2022.9841283","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841283","url":null,"abstract":"For the challenging task of modeling actual complex motions, we propose a new class of Gaussian process (GP) models that are data-driven and also take into account prior knowledge of the motion intention. As a theoretical basis, we show that the GP regression is mathematically equivalent to regularized least-squares estimation for random functions with known prior means. Compared with the popular GP models in machine learning literature, the proposed GP motion model priors with conditional kernels have at least two advantages: 1) they are nonstationary and more applicable to represent complex motions by integrating the basic kinematic principles; 2) conditional kernels are further devised by incorporating the motion intent so that the resultant GP models are more versatile and would expectedly entail more accurate trajectory prediction. A superior property of the GP models with conditional kernels is found to improve the computational efficiency. Finally, illustrative examples are provided to show the superiority of the proposed motion models and to verify the theoretical results given in the paper.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130896033","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841345
S. Coraluppi
Though significant progress has been achieved in the mathematical theory of multi-target tracking and its application in numerous surveillance domains, robust solutions are not always achieved in practice. This paper offers design suggestions for improved performance with a primary focus on multiple-hypothesis tracking based methods.
{"title":"Robustness in Multiple-Hypothesis Tracking","authors":"S. Coraluppi","doi":"10.23919/fusion49751.2022.9841345","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841345","url":null,"abstract":"Though significant progress has been achieved in the mathematical theory of multi-target tracking and its application in numerous surveillance domains, robust solutions are not always achieved in practice. This paper offers design suggestions for improved performance with a primary focus on multiple-hypothesis tracking based methods.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129502980","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841348
Xiujuan Lu, Wei Yi, Yangming Lai, Yang Su
This paper addresses the problem of the low probability of intercept (LPI) performance optimization for target tracking in the distributed MIMO radar systems through the transmitting resource scheduling (TRS) strategy. The mechanism of the proposed LPI-based TRS strategy is to adopt the optimization technique to collaboratively schedule the transmit radar node and signal power with the target tracking accuracy requirement, which aims to enhance the LPI performance of the overall system. Based on the existing research, we develop an intercept model to describe two stages of the signal intercept process with the specific interceptor equipped on the moving target, i.e., intercept and detection. Under the proposed model, the probability of report (PR) is proposed to evaluate the LPI performance for a single transmitting radar node. Then, we consider the maximum PR to represent the LPI performance metric for the overall system. Hence, using it as the objective function, the optimization problem is established with the constraints of the target tracking requirement and the system resource. By introducing the two-step partition-based solution, the proposed non-convex problem is solved efficiently. Finally, several numerical simulations demonstrate the theoretical calculations and validate the effectiveness of the proposed LPI-based TRS strategy.
{"title":"LPI-Based Joint Node Selection and Power Allocation Strategy for Target Tracking in Distributed MIMO Radar","authors":"Xiujuan Lu, Wei Yi, Yangming Lai, Yang Su","doi":"10.23919/fusion49751.2022.9841348","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841348","url":null,"abstract":"This paper addresses the problem of the low probability of intercept (LPI) performance optimization for target tracking in the distributed MIMO radar systems through the transmitting resource scheduling (TRS) strategy. The mechanism of the proposed LPI-based TRS strategy is to adopt the optimization technique to collaboratively schedule the transmit radar node and signal power with the target tracking accuracy requirement, which aims to enhance the LPI performance of the overall system. Based on the existing research, we develop an intercept model to describe two stages of the signal intercept process with the specific interceptor equipped on the moving target, i.e., intercept and detection. Under the proposed model, the probability of report (PR) is proposed to evaluate the LPI performance for a single transmitting radar node. Then, we consider the maximum PR to represent the LPI performance metric for the overall system. Hence, using it as the objective function, the optimization problem is established with the constraints of the target tracking requirement and the system resource. By introducing the two-step partition-based solution, the proposed non-convex problem is solved efficiently. Finally, several numerical simulations demonstrate the theoretical calculations and validate the effectiveness of the proposed LPI-based TRS strategy.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128309621","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841387
W. Koch
In order to protect their common heritage of culture, personal freedom, and the rule of law in an increasingly fragile world, democracies must be able to defend themselves “at machine speed” if necessary. The use of AI in defense there-fore comprises responsible weapons engagement as well as military use cases such as logistics, predictive maintenance, intelligence, surveillance or reconnaissance. This poses a timeless question: How to decide well according to what is recognized as true? For approaching towards an answer, responsible control-lability needs to be turned into three tasks of systems engineering: (1) Design artificially intelligent automation in a way that human beings are mentally and emotionally able to master each situation. (2) Identify technical design principles to facilitate the responsible use of AI in defence. (3) Guarantee that human decision makers always have full superiority of information, decision-making, and options of action. The Ethical AI Demonstrator (E-AID) proposed here for air defence is paving the way by letting soldiers experience the use of AI in the targeting cycle along with associated aspects of stress as realistically as possible.
{"title":"Elements of an Ethical AI Demonstrator for Responsibly Designing Defence Systems","authors":"W. Koch","doi":"10.23919/fusion49751.2022.9841387","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841387","url":null,"abstract":"In order to protect their common heritage of culture, personal freedom, and the rule of law in an increasingly fragile world, democracies must be able to defend themselves “at machine speed” if necessary. The use of AI in defense there-fore comprises responsible weapons engagement as well as military use cases such as logistics, predictive maintenance, intelligence, surveillance or reconnaissance. This poses a timeless question: How to decide well according to what is recognized as true? For approaching towards an answer, responsible control-lability needs to be turned into three tasks of systems engineering: (1) Design artificially intelligent automation in a way that human beings are mentally and emotionally able to master each situation. (2) Identify technical design principles to facilitate the responsible use of AI in defence. (3) Guarantee that human decision makers always have full superiority of information, decision-making, and options of action. The Ethical AI Demonstrator (E-AID) proposed here for air defence is paving the way by letting soldiers experience the use of AI in the targeting cycle along with associated aspects of stress as realistically as possible.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"2018 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127640329","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841273
J. Dezert
This paper presents a new effective measure of un-certainty (MoU) of basic belief assignments. This new continuous measure is effective in the sense that it satisfies a small number of very natural and essential desiderata. Our new simple math-ematical definition of MoU captures well the interwoven link of randomness and imprecision inherent to basic belief assignments. Its numerical value is easy to calculate. This new effective MoU characterizes efficiently any source of evidence used in the belief functions framework. Because this MoU coincides with Shannon entropy for any Bayesian basic belief assignment, it can be also interpreted as an effective generalization of Shannon entropy. We also provide several examples to show how this new MoU works.
{"title":"An Effective Measure of Uncertainty of Basic Belief Assignments","authors":"J. Dezert","doi":"10.23919/fusion49751.2022.9841273","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841273","url":null,"abstract":"This paper presents a new effective measure of un-certainty (MoU) of basic belief assignments. This new continuous measure is effective in the sense that it satisfies a small number of very natural and essential desiderata. Our new simple math-ematical definition of MoU captures well the interwoven link of randomness and imprecision inherent to basic belief assignments. Its numerical value is easy to calculate. This new effective MoU characterizes efficiently any source of evidence used in the belief functions framework. Because this MoU coincides with Shannon entropy for any Bayesian basic belief assignment, it can be also interpreted as an effective generalization of Shannon entropy. We also provide several examples to show how this new MoU works.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126603689","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841353
G. Pavlin, J. P. de Villiers, Kathryn B. Laskey, F. Mignet, L. Jansen
This paper introduces a Bayesian approach to estimating distribution shifts over the modelled variables and continuous model adaptations to mitigate the impact of such shifts. The method exploits probabilistic inference over sets of correlated variables in causal models describing data generating processes. By extending the models with latent auxiliary variables, probabilistic inference over sets of correlated variables enables estimation of the distribution shifts impacting different parts of the models. Moreover, the introduction of latent auxiliary variables makes inference more robust against distribution shifts and supports automated, self-supervised adaptation of the modelling parameters during the operation, often significantly reducing the adverse impact of the distribution shifts. The effectiveness of the method has been validated in systematic experiments using synthetic data.
{"title":"Continuous Model Evaluation and Adaptation to Distribution Shifts: A Probabilistic Self-Supervised Approach","authors":"G. Pavlin, J. P. de Villiers, Kathryn B. Laskey, F. Mignet, L. Jansen","doi":"10.23919/fusion49751.2022.9841353","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841353","url":null,"abstract":"This paper introduces a Bayesian approach to estimating distribution shifts over the modelled variables and continuous model adaptations to mitigate the impact of such shifts. The method exploits probabilistic inference over sets of correlated variables in causal models describing data generating processes. By extending the models with latent auxiliary variables, probabilistic inference over sets of correlated variables enables estimation of the distribution shifts impacting different parts of the models. Moreover, the introduction of latent auxiliary variables makes inference more robust against distribution shifts and supports automated, self-supervised adaptation of the modelling parameters during the operation, often significantly reducing the adverse impact of the distribution shifts. The effectiveness of the method has been validated in systematic experiments using synthetic data.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127190436","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841297
Minseop Choi, John Houle, T. Wickramarathne
Small-Scale Unmanned Autonomous Systems $(sUASs)$ have become an integral part of situation assessment and decision support tasks across a multitude of application domains. Evaluation methods that provide decisive comparisons between different sUAS platforms are critical for not only selecting suitable sUASs, but also for making sure that the chosen sUAS platform can satisfy the required minimum capabilities for a given application. Toward developing sUAS test methods, a new quantitative Operator Situation Awareness (OSA) assessment method is presented for evaluating and comparing sUAS platforms for their ability to provide adequate levels of Situation Awareness (SA) in subterranean (SubT) environments. The work presented here improves upon our previous work based on Attention Allocation Model $(AAM)$ and Man-Machine Integration Design and Analysis (MIDAS)-based SA model by (a) applying formulas to the Salience, Effort, Expectancy, and Value (SEEV) model for accurate quantification and (b) utilizing improved experiments with new operationally relevant scenarios designed to validate those formulas. In particular, our new OSA assessment method accounts for spatial perception differences across platforms by introducing a new component that we refer to as Virtual Proportion, which is obtained from Attention Allocation Proportion of other other Situation Elements by comparing the correct rate of Situation Awareness Global Assessment Technique (SAGAT). Our approach is illustrated via USA comparison of two (02) military-grade sUAS platforms. The paper concludes with a discussion on potential future expansions.
{"title":"On the Development of Quantitative Operator Situational Awareness Assessment Methods for Small-Scale Unmanned Aircraft Systems","authors":"Minseop Choi, John Houle, T. Wickramarathne","doi":"10.23919/fusion49751.2022.9841297","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841297","url":null,"abstract":"Small-Scale Unmanned Autonomous Systems $(sUASs)$ have become an integral part of situation assessment and decision support tasks across a multitude of application domains. Evaluation methods that provide decisive comparisons between different sUAS platforms are critical for not only selecting suitable sUASs, but also for making sure that the chosen sUAS platform can satisfy the required minimum capabilities for a given application. Toward developing sUAS test methods, a new quantitative Operator Situation Awareness (OSA) assessment method is presented for evaluating and comparing sUAS platforms for their ability to provide adequate levels of Situation Awareness (SA) in subterranean (SubT) environments. The work presented here improves upon our previous work based on Attention Allocation Model $(AAM)$ and Man-Machine Integration Design and Analysis (MIDAS)-based SA model by (a) applying formulas to the Salience, Effort, Expectancy, and Value (SEEV) model for accurate quantification and (b) utilizing improved experiments with new operationally relevant scenarios designed to validate those formulas. In particular, our new OSA assessment method accounts for spatial perception differences across platforms by introducing a new component that we refer to as Virtual Proportion, which is obtained from Attention Allocation Proportion of other other Situation Elements by comparing the correct rate of Situation Awareness Global Assessment Technique (SAGAT). Our approach is illustrated via USA comparison of two (02) military-grade sUAS platforms. The paper concludes with a discussion on potential future expansions.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"220 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113972389","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}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841266
Robin Forsling, Zoran Sjanic, F. Gustafsson, Gustaf Hendeby
Data fusion in a communication constrained sensor network is considered. The problem is to reduce the dimensionality of the joint state estimate without significantly decreasing the estimation performance. A method based on scalar subspace projections is derived for this purpose. We consider the cases where the estimates to be fused are: (i) uncorrelated, and (ii) correlated. It is shown how the subspaces can be derived using eigenvalue optimization. In the uncorrelated case guarantees on mean square error optimality are provided. In the correlated case an iterative algorithm based on alternating minimization is proposed. The methods are analyzed using parametrized examples. A simulation evaluation shows that the proposed method performs well both for uncorrelated and correlated estimates.
{"title":"Optimal Linear Fusion of Dimension-Reduced Estimates Using Eigenvalue Optimization","authors":"Robin Forsling, Zoran Sjanic, F. Gustafsson, Gustaf Hendeby","doi":"10.23919/fusion49751.2022.9841266","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841266","url":null,"abstract":"Data fusion in a communication constrained sensor network is considered. The problem is to reduce the dimensionality of the joint state estimate without significantly decreasing the estimation performance. A method based on scalar subspace projections is derived for this purpose. We consider the cases where the estimates to be fused are: (i) uncorrelated, and (ii) correlated. It is shown how the subspaces can be derived using eigenvalue optimization. In the uncorrelated case guarantees on mean square error optimality are provided. In the correlated case an iterative algorithm based on alternating minimization is proposed. The methods are analyzed using parametrized examples. A simulation evaluation shows that the proposed method performs well both for uncorrelated and correlated estimates.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116398123","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}
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