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James Krogmeier

Publications and source records attributed to James Krogmeier.

3 recordsLinked to original sources

Automating Work Orders and Tracking Winter Snow Plows and Patrol Vehicles with Telematics Data

Winter road maintenance is a critical priority for the Indiana Department of Transportation, which manages an extensive fleet across thousands of lane miles. The current manual tracking of snowplow workloads is inefficient and prone to errors. To address these challenges, we developed an in-browser web application that automates the creation and verification of work orders using a large-scale GPS dataset from telematics systems. The application processes millions of GPS data points from hundreds of vehicles over winter, significantly reducing manual labor and minimizing errors. Key features include geohashing for efficient road segment identification, detailed segment-level work records, and robust visualization of vehicle movements, even on repeated routes. Our proposed solution has the potential to enhance the accuracy and granularity of work records, support more effective resource allocation, ensure timely compensation for drivers, alleviate administrative burdens, and allow managers to focus on strategic planning and real-time challenges. The web application can be accessed at https://github.com/oats-center/arrtrack/

cs.CY

Constant Modulus Waveform Design with Space-Time Sidelobe Reduction for DFRC Systems

Dual-function radar-communication (DFRC) is a key enabler of location-based services for next-generation communication systems. In this paper, we investigate the problem of designing constant modulus multiple-input multiple-output (MIMO) waveforms for DFRC systems. We jointly shape the spatial beam pattern and ambiguity function of the transmit space-time matrix to improve target localization accuracy and enhance target resolution in cluttered environments. For communications, we employ constructive interference (CI)-based precoding, which exploits multi-user and radar-induced interference to enhance MIMO symbol detection. We develop two novel solution algorithms based on majorization-minimization (MM) and the linearized alternating direction method of multipliers (LADMM) principles. For the MM approach, we introduce a novel diagonal majorizer for complex quadratic functions, yielding a tighter surrogate and faster convergence than standard largest eigenvalue-based surrogates. After majorization, we decompose the approximated problem into independent subproblems that can be efficiently solved via parallelizable coordinate descent. To accommodate large MIMO dimensions, we further develop a low-complexity LADMM solution. We combine a biconvex reformulation and first-order proximal approximations to handle the nonconvex quartic objective without requiring costly matrix inversions. We evaluate the performance of the proposed algorithms in comparison to the existing DFRC algorithm. Simulation results demonstrate that the proposed algorithms can substantially enhance target detection and imaging performance due to the reduction of space-time sidelobes.

eess.SP

Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications

Cellular-connected unmanned aerial vehicles (UAVs) represent a promising technology for extending the coverage of 5G and 6G networks in a cost-effective manner. Additionally, Massive multiple-input multiple-output (MIMO) serves as an effective solution to interference mitigation in cellular-connected UAV communications. In this letter, we propose a fusion of wireless and sensor data to enhance beam alignment for cellular-connected UAV massive MIMO communications. We develop a predictive beamforming framework, including the frame structure and predictive beamformer. Moreover, we employ an extended Kalman filter (EKF) to integrate channel and sensor data and provide the corresponding state-space and observation models. Simulation results demonstrate that the proposed scheme can improve position/orientation estimation accuracy significantly, leading to higher spectral efficiency.

eess.SP