75 citations · 151 across the 28 of their papers we have counts for
31 papers
Asynchronous Distributed Multi-Sensor Fusion Over Sensor Track Hypotheses
Ryan Draves, Nisar Ahmed, Eric Borcherding +1
This work proposes a multi-target tracking solution that reasons over combinations of sensor-level tracks to perform distributed multiple hypothesis tracking (MHT) in an asynchrono…
Distributed Observation Scheduling and Sensor Fusion For Large Agile Constellations
Nicholas Niziolek, Ryan Draves, Nolan Ales +4
With the rapid increase in interest towards large scale earth observing satellite constellations, the problem of properly commanding and utilizing these systems has become increasi…
Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation
Christopher Funk, Ofer Dagan, Benjamin Noack +1
A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is of…
Kalman Filter Auto-tuning through Enforcing Chi-Squared Normalized Error Distributions with Bayesian Optimization
Zhaozhong Chen, Harel Biggie, Nisar Ahmed +2
The nonlinear and stochastic relationship between noise covariance parameter values and state estimator performance makes optimal filter tuning a very challenging problem. Popular…
Towards Decentralized Heterogeneous Multi-Robot SLAM and Target Tracking
Ofer Dagan, Tycho L. Cinquini, Luke Morrissey +3
In many robotics problems, there is a significant gain in collaborative information sharing between multiple robots, for exploration, search and rescue, tracking multiple targets,…
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits
Shohei Wakayama, Nisar Ahmed
In autonomous robotic decision-making under uncertainty, the tradeoff between exploitation and exploration of available options must be considered. If secondary information associa…
Nonlinear Heterogeneous Bayesian Decentralized Data Fusion
Ofer Dagan, Tycho L. Cinquini, Nisar R. Ahmed
The factor graph decentralized data fusion (FG-DDF) framework was developed for the analysis and exploitation of conditional independence in {heterogeneous Bayesian decentralized f…
Pareto Optimal Strategies for Event-Triggered Estimation
Anne Theurkauf, Nisar Ahmed, Morteza Lahijanian
Although resource-limited networked autonomous systems must be able to efficiently and effectively accomplish tasks, better conservation of resources often results in worse task pe…
A Factor-Based Framework for Decision-Making Competency Self-Assessment
Brett W. Israelsen, Nisar Ahmed
We summarize our efforts to date in developing a framework for generating succinct human-understandable competency self-assessments in terms of machine self confidence, i.e. a robo…
Conservative Filtering for Heterogeneous Decentralized Data Fusion in Dynamic Robotic Systems
Ofer Dagan, Nisar R. Ahmed
This paper presents a method for Bayesian multi-robot peer-to-peer data fusion where any pair of autonomous robots hold non-identical, but overlapping parts of a global joint proba…
Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing
Shohei Wakayama, Nisar Ahmed
Humans cannot always be treated as oracles for collaborative sensing. Robots thus need to maintain beliefs over unknown world states when receiving semantic data from humans, as we…
Time Dependence in Kalman Filter Tuning
Zhaozhong Chen, Christoffer Heckman, Simon Julier +1
In this paper, we propose an approach to address the problems with ambiguity in tuning the process and observation noises for a discrete-time linear Kalman filter. Conventional app…
Factor Graphs for Heterogeneous Bayesian Decentralized Data Fusion
Ofer Dagan, Nisar R. Ahmed
This paper explores the use of factor graphs as an inference and analysis tool for Bayesian peer-to-peer decentralized data fusion. We propose a framework by which agents can each…
Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion
Ofer Dagan, Nisar R. Ahmed
In Bayesian peer-to-peer decentralized data fusion, the underlying distributions held locally by autonomous agents are frequently assumed to be over the same set of variables (homo…
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data
Aastha Acharya, Rebecca Russell, Nisar R. Ahmed
The deployment of reinforcement learning (RL) in the real world comes with challenges in calibrating user trust and expectations. As a step toward developing RL systems that are ab…
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems
Michael L. Iuzzolino, Tetsumichi Umada, Nisar R. Ahmed +1
We investigate how different active learning (AL) query policies coupled with classification uncertainty visualizations affect analyst trust in automated classification systems. A…
Kalman Filter Tuning with Bayesian Optimization
Zhaozhong Chen, Nisar Ahmed, Simon Julier +1
Many state estimation algorithms must be tuned given the state space process and observation models, the process and observation noise parameters must be chosen. Conventional tunin…
Decentralized Gaussian Mixture Fusion through Unified Quotient Approximations
Nisar R. Ahmed
This work examines the problem of using finite Gaussian mixtures (GM) probability density functions in recursive Bayesian peer-to-peer decentralized data fusion (DDF). It is shown…
Extrinisic Calibration of a Camera-Arm System Through Rotation Identification
Steve McGuire, Christoffer Heckman, Daniel Szafir +2
Determining extrinsic calibration parameters is a necessity in any robotic system composed of actuators and cameras. Once a system is outside the lab environment, parameters must b…
Factorized Machine Self-Confidence for Decision-Making Agents
Brett W Israelsen, Nisar R Ahmed, Eric Frew +2
Algorithmic assurances from advanced autonomous systems assist human users in understanding, trusting, and using such systems appropriately. Designing these systems with the capaci…
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
Zhaozhong Chen, Christoffer Heckman, Simon Julier +1
Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time an…
Data-Free/Data-Sparse Softmax Parameter Estimation with Structured Class Geometries
Nisar Ahmed
This note considers softmax parameter estimation when little/no labeled training data is available, but a priori information about the relative geometry of class label log-odds bou…
Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach
Luke Burks, Ian Loefgren, Nisar Ahmed
This work develops novel strategies for optimal planning with semantic observations using continuous state partially observable markov decision processes (CPOMDPs). Two major innov…
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
Luke Burks, Ian Loefgren, Luke Barbier +4
In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large…