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Collaboration FST-ULCO

Collaboration FST-ULCO. Context and objective of the work. Context : Wetland monitoring . Water level : ECEF Localization of the water surface in order to get a referenced water level. Soil moisture : Measuring the degree of water saturation to prevent flood and measuring drought indices.

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Collaboration FST-ULCO

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  1. Collaboration FST-ULCO

  2. Context and objective of the work Context : Wetland monitoring • Water level : ECEF Localization of the water surface in order to get a referenced water level. • Soil moisture : Measuring the degree of water saturation to prevent flood and measuring drought indices Research topics : • - Interference Pattern Technique (Altimetry) • SNR estimation (Soil moisture)

  3. Outline • 1) Application context • 2) Problem statement • 3) Non-linear model • 4) Estimation • 5) Experimentation

  4. Interference Pattern Technique Altimetry system:

  5. Interference Pattern Technique Received signal:

  6. Interference Pattern Technique Received signal after integration : With :

  7. We estimate with the observations of phase the antenna height : Interference Pattern Technique

  8. Soil moisture estimation • The system is composed of : • Two antennas with different polarization • A multi-channel GNSS receiver • A mast for ground applications • Estimation : • Estimation with the SNR of the direct and reflected GPS signals • Tracking assistance of the nadir signal with the direct signal • Problem : Weak signal to noise ratio for the nadir signal.

  9. Soil moisture estimation • Roughness parameter • Fresnel coefficient (elevation) • Antenna gain • Path of the signal Values of the coefficient Γ and power variations as a function of satellite elevation and sand moisture

  10. Problem Statement These applications, soil moisture estimation and pattern interference technique, used measurements of the SNR in order to respectively estimate the soil permittivity and the antenna height. • A GNSS receiver provides measurements of the correlation. You can derive from themean value of the correlation the amplitude of the received signal. The amplitude is not normalized in this case. • If you want to derive from these measurements the signal to noise ratio C/N0 ,you must estimate its mean value and its variance : • -> So we have to derive the statistic of the correlation on a set of observations (to estimate two parameters). => In this work we propose to derive a direct relationship between the mean correlation value and the SNR of the received signal. We will define in this case a filter for the direct estimation of the SNR with the observations provided by the correlation.

  11. Problem Statement ci Ck r(t) rIF(t) riIF sin(ωL1 t) fs fs fs • “Ck” is the maximum of correlation because the local code and carrier are supposed to be aligned with the received signal. • We assume that signals are sampled and quantified on one bit. The sampled signal takes the values 1 or -1. cos(ωsd t+Φs) CA(t-τs) => In the next (3 slides) we report the detections “ci” for a period of code (1 ms) and the sum “Ck” (maximum value of correlation) as a function of the Doppler.

  12. Problem Statement • “ci” takes the value one when a sample of the received signal has the same sign • than the local signal. • “ci” takes the value minus one there is a difference between the sign of the received and • the local signal.

  13. Problem Statement

  14. Problem Statement

  15. Problem Statement • For these examples we use a weak noise (small variance) and we can notice • that the number of false detections increases with the Doppler. This effect is dueto the number of zero crossing of the curve. When the noise is stronger • the number of false detections increases also. • In our work we define the statistic of “ci” and then “Ck” as a function of the amplitude, • Doppler, delay of code and phase of the received signals. • We can then compute the expecting function of correlation in the coherent or non • coherent case. For this application only the maximum of the coherent value of • correlation is considered.

  16. Non-linear model Probabilistic model: Card{V} satellites case: =>

  17. Estimation Non linear filtering : Measurements equation of the correlation are highly non linear an EKF can not be used, the proposed solution is a particle filter State equations (alpha beta filter): Measurement equations (Observations of Ck): Tracking process : - Each millisecond the tracking loop provides an estimation of phase, Doppler, and code delay for all the satellites in view - These estimate and the predicted state are used to construct predicted measurements -These measurements are compared in the filter with the observations of correlation provided by the tracking loops

  18. Estimation Amplitude Amplitude velocity Particle Filter : Particles : xi1,k xi2,k Weights pi1,K i=1….N Initialization (inversion of the carrier less case) • Initialization • Prediction • Update • Estimation • Multinomial Resampling N(0,Q) Covariance of state and measure : tuning parameters

  19. Estimation Messages of navigation Weights (6 satellites) Particles t [ms] =>Each ms the estimate Doppler, phase and code delay are used as input in the filter, to construct with the predicted state of Av,k a predicted observation compared to Ck. =>The filter runs a set of particles for each satellite in view. The estimation is processed with the particles which act as the sampled distribution of the states.

  20. Experimentation We show with the proposed model : - Inter-correlation effect due to the satellites codes. - Inter-correlation effect due to the carrier On the estimate value of the correlation Configuration of the experimentation: • The sampling period is 1 [ms]. • The number of visible satellites is 6. • The amplitudes of the GNSS signals is 0.21 (50 [dBHz])For these amplitudes the noise variance is 1 on the received signal.

  21. Experimentation • Random evolution due to : • the code inter-correlation • The carrier evolution

  22. Experimentation

  23. Experimentation Model of simulation : Doppler frequency : Satellite s1 : 1000 Hz Satellite s2 : 3000 Hz Jitter noise model : phase : random walk σ=0.01 frequency : random walk σ=0.1 Code delay : linear evolution Goal of the experimentation : • Assessment on synthetic data • The two satellites case • Static case and dynamic case

  24. Experimentation Estimate parameters (Sat 1): Estimate C/N0 :

  25. Experimentation Estimate Amplitude : Error of estimation of C/N0 :

  26. Conclusion *We state the problem of defining a link between the SNR and the amplitude of the GNSS signals. *We propose a direct model of the maximum of correlation as a function of amplitude, Doppler, code delay and phase of the received signal. *We propose to use a particle filter to inverse the non linear model. *We access the model on synthetic data. Thank You For Your Attention

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