Kalman Filtering

for GNSS Receiver Tracking Loops for Scintillating Signals

Scintillation

  • amplitude fading
    • degrades CN0
  • phase fluctuations
    • induces cycle slips and/or loss-of-lock

Why Kalman Filter?

  • minimize mean-squared phase error
  • can adapt over gradual changes in signal characteristics

Disadvantage

  • degrades or diverges under abrupt changes in signal characteristics

Steps to Kalman Filter

  • Observation Matrix
  • System Model
  • Noise Model (Covariance Matrix)
  • Filter Output
A = M\vec{x}
A=Mx
\text{signal}_k\star \text{ref}_k = ADC(n T_s + \tau_k)C(n T_s+\hat{\tau_k}) e^{j\left[2\pi(f_{d_k} - \hat{f_{d_k}})n T_s + \phi_k - \hat{\phi}_k\right]} + \eta(nT_s)
signalkrefk=ADC(nTs+τk)C(nTs+τk^)ej[2π(fdkfdk^)nTs+ϕkϕ^k]+η(nTs)

correlation output at        block

k^{th}
kth
\approx ADR(\Delta\tau) N\text{sinc}(2\pi\Delta f_d T_I)\exp(j2\pi \Delta f_{d_k} \frac{T_I - T_s}{2})\exp(j\Delta\phi_k)
ADR(Δτ)Nsinc(2πΔfdTI)exp(j2πΔfdk2TITs)exp(jΔϕk)
\eta(n T_s)
η(nTs)

for fine acq., add and subtract adjacent overlapping blocks to cancel noise? investigate, but probs no b/c mult by signal means diff noise

is zero-mean white noise with variance

\sigma_n^2 = P_n = \frac{P_s}{10^{\frac{C/N_0}{10}}}
σn2=Pn=1010C/N0Ps

The End

Kalman Filtering for GNSS Receiver Tracking Under Scintillation

By Brian Breitsch

Kalman Filtering for GNSS Receiver Tracking Under Scintillation

An introduction to adaptive Kalman filtering for applications to GNSS receiver lock loops, specifically for achieving robust performance under scintillating signal conditions.

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