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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter.

Iñigo Cortés1,2, Johannes Rossouw van der Merwe1, Elena Simona Lohan2

  • 1Satellite Based Positioning Systems Department, Fraunhofer IIS, Nordostpark 84, 90411 Nuremberg, Germany.

Sensors (Basel, Switzerland)
|January 22, 2022
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Summary
This summary is machine-generated.

This study enhances Global Navigation Satellite System (GNSS) receivers by adapting the direct-state Kalman filter (DSKF) using a loop-bandwidth control algorithm (LBCA). The LBCA-based lookup table-DSKF offers superior performance and lower complexity for robust adaptive tracking.

Keywords:
adaptive tracking techniquesdirect-state Kalman filter (DSKF)global navigation satellite system (GNSS)lookup table direct-state Kalman filter (LUT-DSKF)loop-bandwidth control algorithm (LBCA)scalar tracking loop (STL)

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Area of Science:

  • Signal Processing
  • Navigation Systems
  • Estimation Theory

Background:

  • Modern Global Navigation Satellite System (GNSS) receivers utilize the direct-state Kalman filter (DSKF) for optimal state estimation under Gaussian assumptions.
  • Time-varying scenarios in GNSS, including noise and multipath, degrade DSKF performance by necessitating difficult-to-model noise parameters, leading to sub-optimal solutions.
  • Adaptive tracking techniques are crucial for maintaining DSKF performance in dynamic and noisy environments.

Purpose of the Study:

  • To evaluate the performance of robust adaptive tracking techniques for direct-state Kalman filters (DSKF) in GNSS receivers.
  • To introduce and assess two novel methods, LBCA-based DSKF and LBCA-based lookup table (LUT)-DSKF, for adapting DSKF using the loop-bandwidth control algorithm (LBCA).
  • To compare the proposed adaptive techniques against the carrier-to-noise density ratio (C/N0)-based DSKF in simulated GNSS scenarios.

Main Methods:

  • Implementation of two LBCA-based adaptive DSKF techniques: one adapting steady-state process noise variance and the other relating loop bandwidth to Kalman gains.
  • Integration of these adaptive techniques into an open software interface GNSS hardware receiver.
  • Performance evaluation through simulated scenarios featuring diverse dynamics and noise conditions, assessing receiver tracking and overall system performance.

Main Results:

  • The loop-bandwidth control algorithm (LBCA) effectively adapts the DSKF in time-varying GNSS scenarios.
  • The LBCA-based lookup table (LUT)-DSKF demonstrates superior static and dynamic system performance compared to existing adaptive DSKF methods.
  • The LBCA-based LUT-DSKF achieves the lowest implementation complexity among the evaluated adaptive tracking techniques.

Conclusions:

  • The loop-bandwidth control algorithm (LBCA) is a viable and effective method for adapting direct-state Kalman filters (DSKF) in GNSS receivers.
  • The LBCA-based LUT-DSKF offers a significant advancement in GNSS receiver tracking performance, balancing robustness, adaptability, and computational efficiency.
  • This research validates the potential of LBCA-driven adaptation for enhancing the reliability and accuracy of satellite navigation systems.