- No events scheduled for November 12, 2023.
- No events scheduled for November 13, 2023.
- No events scheduled for November 15, 2023.
- No events scheduled for November 16, 2023.
Information-Theoretic Approach to Fully Adaptive Radar Resource Management
Virtual: https://events.vtools.ieee.org/m/378859- No events scheduled for November 18, 2023.
Week of Events
Software Verification of Systematic Resampling for Optimization of Particle Filters
Software Verification of Systematic Resampling for Optimization of Particle Filters
Systematic resampling is the most popularly used resampling method in particle filters. This paper seeks to further the understanding of systematic resampling by defining a formula made up of variables from the sampling equation and the particle weights. The formula is then verified via SPARK, a software verification language. The verified systematic resampling formula states that the minimum/maximum number of possible samples taken of a particle is equal to the floor/ceiling value of particle weight divided by the sampling interval, respectively. This allows for a creation of a randomness spectrum that each resampling method can fall within. Methods on the lower end e.g. systematic resampling have less randomness, thus, are quicker to reach an estimate. Although, lower randomness allows for error by having a larger bias towards the size of the weight; having this bias creates vulnerabilities to the noise in the environment e.g. jamming. Conclusively, this is the first step in characterizing each resampling method. This will allow target-tracking engineers to pick the best resampling method for their environment instead of choosing the most popularly used one. Speaker(s): Lt Terry, Virtual: https://events.vtools.ieee.org/m/382838
Information-Theoretic Approach to Fully Adaptive Radar Resource Management
Information-Theoretic Approach to Fully Adaptive Radar Resource Management
The recent emergence of agile software-controlled waveform generation provides an opportunity to dramatically improve radar system performance. This talk describes an integrated algorithm for estimating the state of a surveillance region and using this estimate to design future radar transmissions. Our fully adaptive radar (FAR) resource management (RM) approach emulates the perception-action cycle (PAC) of cognition. The FAR-RM PAC includes a perceptual processor that performs multiple radar system tasks and an executive processor that allocates system resources to the tasks and decides the next transmission of the radar on a dwell-by-dwell basis. The executive processor uses an information theoretic objective function to select the collection of waveforms that is expected to maximally improve the perception on the next cycle. We illustrate our approach in simulation using a model of an agile multi-mode radar whose mission is tracking and classifying multiple target aircraft. Speaker(s): Dr. Kristine Bell, Agenda: Friday, November 17, 2023 (All times listed below are in Eastern Time) 11:45 A.M. - 12:00 P.M. - Attendees join the Zoom call 12:00 P.M. - 12:45 P.M. - Dr. Kristine Bell presents "Information-Theoretic Approach to Fully Adaptive Radar Resource Management" 12:45 P.M. - 1:00 P.M. - Questions & Answers 1:00 P.M. - 1:30 P.M. - Additional time for Questions & Answers, if desired Virtual: https://events.vtools.ieee.org/m/378859
Path Planning for Autonomous Agents
Path Planning for Autonomous Agents
This work focuses on path planning for autonomous agents, leveraging multiple sensing domains to provide navigation solutions in contested environments. The emphasis is on mutli-objective optimization, finding optimal path costs that minimize uncertainty in the goal region. The algorithm developed is based on the Rapidly-exploring Random Tree (RRT) probabilistic planning algorithm, but extends into the belief space to plan over uncertainty. The Rapidly-exploring Random Belief Alt-Nav Graph (RRBANG) leverages the probabilistic guarantees of the RRT-based algorithms, ensuring the properties for probabilistic completeness and asymptotic optimality. The algorithm is designed to be agent and measurement model agnostic, but specifically how complementary navigation techniques obtain their measurements when developing plans within a complex environment. The algorithm provides an offline, initial plan for an agent given a priori world information. There are several, significant planned avenues for advancement, targeting the algorithm itself, extending to implement real-time dynamic re-planning, as well as benchmarking against other belief space planning (BSP) algorithms. Captain Machin will also discuss several ANT center research efforts focused on pushing Autonomy. Co-sponsored by: Wright-Patt Multi-Intelligence Development Consortium (WPMDC), The DOD & DOE Communities Speaker(s): Tim Agenda: This work focuses on path planning for autonomous agents, leveraging multiple sensing domains to provide navigation solutions in contested environments. The emphasis is on mutli-objective optimization, finding optimal path costs that minimize uncertainty in the goal region. The algorithm developed is based on the Rapidly-exploring Random Tree (RRT) probabilistic planning algorithm, but extends into the belief space to plan over uncertainty. The Rapidly-exploring Random Belief Alt-Nav Graph (RRBANG) leverages the probabilistic guarantees of the RRT-based algorithms, ensuring the properties for probabilistic completeness and asymptotic optimality. The algorithm is designed to be agent and measurement model agnostic, but specifically how complementary navigation techniques obtain their measurements when developing plans within a complex environment. The algorithm provides an offline, initial plan for an agent given a priori world information. There are several, significant planned avenues for advancement, targeting the algorithm itself, extending to implement real-time dynamic re-planning, as well as benchmarking against other belief space planning (BSP) algorithms. Captain Machin will also discuss several ANT center research efforts focused on pushing Autonomy. Virtual: https://events.vtools.ieee.org/m/384182