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Target Tracking with Sensor Navigation Using Coupled RSS and AoA Measurements.

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Summary
This summary is machine-generated.

This study introduces new algorithms for tracking mobile targets in wireless sensor networks (WSNs) using received signal strength (RSS) and angle of arrival (AoA) measurements. The proposed methods enhance accuracy even with unknown target power and sensor location uncertainties.

Keywords:
Kalman filter (KF)angle of arrival (AoA)maximum a posteriori (MAP) estimatorreceived signal strength (RSS)sensor navigationtarget tracking

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Area of Science:

  • Wireless Sensor Networks (WSNs)
  • Mobile Target Tracking
  • Sensor Signal Processing

Background:

  • Mobile target tracking in WSNs is challenging due to unknown target transmit power and sensor location inaccuracies.
  • Existing methods often struggle with non-linear measurement models and imperfect environmental parameters like path loss exponent (PLE).

Purpose of the Study:

  • To develop advanced algorithms for accurate mobile target tracking in WSNs.
  • To address challenges posed by unknown target power, sensor navigation, and model uncertainties.
  • To improve the efficiency and accuracy of target localization using RSS and AoA data.

Main Methods:

  • Linearization of the non-linear measurement model for signal strength and angle of arrival.
  • Application of a Bayesian approach, combining linearized observations with state transition models.
  • Development of Maximum a Posteriori (MAP) and Kalman Filtering (KF) based algorithms.
  • Introduction of a mobile sensor navigation procedure to enhance estimation accuracy.

Main Results:

  • Proposed MAP and KF algorithms demonstrate superior performance in target tracking.
  • The mobile sensor navigation routine significantly improves estimation accuracy with fewer sensors.
  • Algorithms effectively handle model flaws, including uncertainties in path loss exponent and sensor locations.
  • Extensive simulations confirm the effectiveness and superiority of the developed methods.

Conclusions:

  • The proposed Bayesian-based MAP and KF algorithms offer a robust solution for mobile target tracking in WSNs.
  • The integrated sensor navigation strategy enhances tracking precision and reduces sensor requirements.
  • The developed framework effectively mitigates the impact of unknown parameters and model uncertainties, paving the way for more reliable WSN applications.