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This dataset is based on the ACFR Five Roundabouts Dataset. The original tracking data of over 23,000 traffic vehicles have been processed with an optimization-based filtering method to combat measurement noise and errors. Smooth velocity and acceleration signals are reconstructed. The processed recordings have then undergone a selection process using DBSCAN to remove the erroneous samples. The remaining samples contained in this dataset are considered representative of how average human drivers approach a roundabout scenario in daily driving.

This work provides the measurement data of sixteen high frequency (HF) radio frequency identification (RFID) transponder (tag) chips. In particular, the chip input impedance was characterized versus chip input voltage at 13.56 MHz. The measurement is based on the radio frequency current-voltage impedance measurement method and achieves, compared to previous work, higher measurement accuracy of lower than 1.5 %. The accompanying publication provides additional details.

The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to characterize self-interference for in-band full-duplex underwater acoustic communications. The experiment was conducted in the Lake of Tuscaloosa in July 2019. A single transmission-receiving line was deployed off a boat that was moored in the center of the lake. The transmission-receiving line had one acoustic transmitter and eight hydrophone receivers.

This Matlab model and the included results are submitted as reference for the paper ''. 

Presenting a comparative study of the Sequential Unscented Kalman Filter (SUKF), Least-squares (LS) Multilateration and standard Unscented Kalman Filter (UKF) for localisation that relies on sequentially received datasets. 

The KEWLS and KKF approach presents a novel solution using Linear Kalman Filters (LKF) to extrapolate individual sensor measurements to a synchronous point in time for use in LS Multilateration.