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RSS/TDoA-Based Source Localization in Microwave UWB Sensors Networks Using Two Anchor Nodes.

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This study introduces an optimal algorithm for estimating ultra-wideband signal amplitude and delay time using a hybrid RSS/TDoA method. The developed algorithm improves passive target location accuracy by leveraging Bayesian estimation and analyzing signal characteristics.

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Area of Science:

  • Signal Processing
  • Radio Frequency Engineering
  • Estimation Theory

Background:

  • Passive location systems are crucial for tracking fixed targets without active transmission.
  • Ultra-wideband (UWB) signals offer high resolution for accurate time-of-arrival measurements.
  • Hybrid Received Signal Strength (RSS) and Time Difference of Arrival (TDoA) methods combine complementary information for improved localization.

Purpose of the Study:

  • To develop an optimal algorithm for estimating UWB signal amplitude and propagation delay time.
  • To enhance passive target location accuracy in a two-dimensional space using two base stations.
  • To analyze the performance of the estimation algorithm against the Bayesian Cramer-Rao Lower Bound (BCRLB).

Main Methods:

  • Utilizing a Bayesian strategy based on maximum a posteriori (MAP) probability density.
  • Incorporating a priori statistical data of the Line of Sight (LoS) radio channel with Gaussian monocycle propagation.
  • Calculating the Bayesian Cramer-Rao Lower Bound (BCRLB) for time-discrete signal parameter estimation.

Main Results:

  • An algorithm for optimal estimation of UWB signal amplitude and propagation delay time was developed.
  • The developed algorithm enables optimal estimation of distances from the radiation source to base stations.
  • Statistical characteristics of the obtained estimates were calculated and analyzed for functional dependence on various parameters.

Conclusions:

  • The proposed algorithm provides an optimal estimation of UWB signal parameters for passive location.
  • The method demonstrates improved accuracy by considering channel properties and employing Bayesian estimation.
  • The study validates the algorithm's performance by comparing estimates against the theoretical BCRLB.