IEEE Dayton Section

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

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

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

Please join IEEE Dayton Signal Processing Society in welcoming 2023 IEEE Distinguished Industry Speaker Dr. Linda Moore! Radar offers some unique capabilities compared to other sensing phenomenologies. For example, radar can operate at long ranges, during the day and night, and in most weather conditions. Synthetic aperture radar (SAR) enables formation of 2D and 3D images of ground scenes for a wide array of military and commercial applications. In this talk, Dr. Linda Moore will discuss current challenges in SAR signal processing, including the challenge of applying machine/deep learning techniques to SAR automatic target recognition (ATR). Measured and synthetic SAR data has been made publicly available by the U.S. Air Force Research Laboratory and can assist in developing new techniques for today's SAR signal processing challenges. Available data sets will be associated with relevant technical challenges and examples of related IEEE published work will be highlighted. Speaker(s): Dr. Linda Moore Virtual: https://events.vtools.ieee.org/m/374409

Brenden Glover, a PhD student working with AFRL, will be presenting his work on locking of photoinc based oscillators. Virtual: https://events.vtools.ieee.org/m/385170

Commercial-grade silicon-based solar cells have an efficiency in the 20-30% range. The addition of a non-uniform magnetic field can increase the efficiency of a solar cell by manipulating the movement of the charge carriers within the silicon. This manipulation can increase the current produced by the solar cell which helps to increase the power and efficiency of the solar cell. This theory can be tested by measuring a solar cell’s output current and voltage both with and without the presence of a non-uniform magnetic field. A higher output power means the efficiency of the solar cell is increased. Co-sponsored by: Wright-Patt Multi-Intelligence Development Consortium (WPMDC), The DOD & DOE Communities Speaker(s): Jason Agenda: Commercial-grade silicon-based solar cells have an efficiency in the 20-30% range. The addition of a non-uniform magnetic field can increase the efficiency of a solar cell by manipulating the movement of the charge carriers within the silicon. This manipulation can increase the current produced by the solar cell which helps to increase the power and efficiency of the solar cell. This theory can be tested by measuring a solar cell’s output current and voltage both with and without the presence of a non-uniform magnetic field. A higher output power means the efficiency of the solar cell is increased. Virtual: https://events.vtools.ieee.org/m/387035