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Python implementation of the simulation for paper "Computationally Efficient and Location Robust TDOA Positioning Estimator in MPR for IoT Device"

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Efficient_LocationRobust_TDOALoc_MPR-Python

Python implementation of the simulation for paper "Computationally Efficient and Location Robust TDOA Positioning Estimator in MPR for IoT Device"

By Chengyu Li and Y. Sun, College of Computer Science, Sichuan University

A computationally efficient and location robust estimator for TDOA localization in MPR, which is designed for IoT device. The solution performs similarly to the GTRS solution [1] but requires less computational load.

[1] Y. Sun, K. C. Ho, and Q. Wan, “Solution and analysis of TDOA localization of a near or distant source in closed form,” IEEE Trans. Signal Process., vol. 67, no. 2, pp. 320-335, Jan. 2019.

If you use any of the following codes in your research, please cite the paper as a reference in your publication. Thank you!

Computationally Efficient and Location Robust Estimator for IoT Device (2/22/2024)

Reference

Y. Sun et al., "Computationally Attractive and Location Robust Estimator for IoT Device Positioning," IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10891-10907, Jul. 2022, doi: 10.1109/jiot.2021.3127690.

Code List:

  • SCO Closed-Form Solution: TDOA_SCO_MPR
  • SUM Closed-Form Solution: TDOA_SUM_MPR
  • GTRS Closed-Form Solution: TDOA_GTRS_MPR
  • CRLB: ConsCRLB
  • Example: Fig2to9
  • Example: Fig10
  • Example: Fig20to23
  • Example: Fig24to27

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Python implementation of the simulation for paper "Computationally Efficient and Location Robust TDOA Positioning Estimator in MPR for IoT Device"

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