Imp mprovin ing S Sensitivity o on K Kea CubeS eSat GPS GPS - - PowerPoint PPT Presentation

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Imp mprovin ing S Sensitivity o on K Kea CubeS eSat GPS GPS Rec ecei eiver ers Eamonn Glennon Kea CubeSat GPS Receivers Follow-on from Namuru V3.2R3A developed for DST-Group Biarri program L1 C/A code GPS receiver with

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Eamonn Glennon

Imp mprovin ing S Sensitivity o

  • n K

Kea CubeS eSat GPS GPS Rec ecei eiver ers

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney 2

  • Follow-on from Namuru V3.2R3A

developed for DST-Group Biarri program

  • L1 C/A code GPS receiver with

features designed to support in-orbit and high dynamics operation

  • SmartFusion 2 SoC & BL2627 RFFE
  • To be flown on 3 CubeSat missions

– Buccaneer Risk Mitigation – UNSW QB50 EC0 and – USyd QB50 iInspire 2

Kea CubeSat GPS Receivers

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 3

  • Some space applications have weak signals
  • GPS Radio Occultation

– Signals passing through the atmosphere’s limb are weak and subject to ionospheric fading – Continuous phase lock needed for carrier phase observations

  • GPS in medium, high or geostationary orbit

– Space Service Volume aims for consistent performance in MEO, HEO & GEO – Navigation requires use edges of GPS beam pattern and side- lobes

High Sensitivity in Space

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 4

Space Service Volume

http://www.gps.gov/governance/advisory/meetings/2015-06/bauer.pdf

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  • High sensitivity techniques developed for cell-phone AGPS

– Combination of longer coherent integration (CI) periods (i.e. separate accumulation of in-phase and quadrature phase channels) – Up to 20 ms without data wiping – Longer CI reduces squaring losses, but increases number of searches – Non-coherent integration (NCI) for additional sensitivity (i.e. accumulation of magnitude or magnitude squares of I & Q)

  • Acquisition assistance for reduced search times

– Less feasible for MEO, HEO & GEO, but TLEs could be used

  • Search acceleration hardware

– Useful for acquisition, but not for tracking

High Sensitivity GPS

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 6

  • Pre modification sensitivity not great!

– 4 ms coherent integration (CI), so bandwidth is 250 Hz – 8 non-coherent rounds of integration (NCRI) – PLL ran at 2 x 4 ms, but detection as above

  • Improvements required

– Ability to support up to 20 ms CI, with 4+ NCRI – DSP produces ½ dumps for FLL to allow FLL to run at the same rate as the PLL – PLL runs at up to 20 ms, so bandwidth is 50 Hz – Detection using 20 ms x 4+ NCRI – Automatic switch to higher CI when bit-synchronisation achieved

KeaV41SBR4 Modifications

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 7

Sensitivity Predictions

5 10 15 20 25 30 35 22 27 32 37 Output SNR (dB) Input C/N0 ( dBHz)

Predicted Sensitivity for Different Coherent/Non-coherent Integrations

4 ms x 8 NCR : SNR (dB) 10 ms x 4 NCR : SNR (dB) 10 ms x 8 NCR : SNR (dB) 20 ms x 4 NCR : SNR (dB)

8.4 dB 6.8 dB

van Diggelen Sensitivity Spreadsheets

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 8

  • High sensitivity requires SNR to be maximized
  • Longer coherent integration reduces squaring losses
  • Additional non-coherent integration further improves SNR

BUT

  • Detection requires comparison of CI & NCI against a detection

threshold

  • Question: How to set the detection threshold?
  • Answer: Adaptively estimate the threshold using a correlator noise

finger/tap T = μ + k σ μ & σ are the (estimated or measured) mean & standard deviation of the NCIs k is a constant that sets the false alarm rate (FAR)

Adaptive Thresholds

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 9

  • σ1ms values update at 1 kHz, so faster response time
  • Not effective in the presence of Multiple Access Interference (MAI) /

Cross Correlation Noise

Measure σ1ms statistic from 1 ms I & Q dumps Calculate μNCI(σ1ms) & σNCI(σ1ms) assuming NC & NCR σ1ms Selected Coherent integration (CI) & Non coherent integration (NCI) CI & NCI Threshold T = μNCI(σ1ms) + K σNCI(σ1ms)

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 10

  • Slow update rate of 1000/(CI*NCR) Hz / input sample
  • Need (say) 100 samples for meaningful statistics
  • Threshold calculated from μNCI & σNCI assumes Normal distribution

– BUT this is not necessarily true (approaches Normal ~ Central Limit Theorem)

  • Histogram method allows Th to be directly extracted with a given

False Alarm Rate and can be maintained efficiently

– See the paper for details

Noise Tap Usage – NC & NCI statistics/histogram

Noise tap N processed exactly as E, P, L taps. Calculate ΣN & ΣN2 from which μNCI& σNCI are obtained Noise samples Construct histogram

  • f Noise samples

Threshold Tc = μNCI + K σNCI μNCI σNCI Threshold Th

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 11

  • Create signals at IF in Matlab that are down-converted and

de-spread with noise & E,P,L taps

  • Four satellite scenarios considered

– 1 strong, rest weak – 6 strong – 6 strong, SV1 affected by 1 SV MAI – 6 strong, SV1 affected by 2 SVs of MAI

  • Two different tracking scenarios

– 4 ms & 8 NCR, same as legacy Namuru & Kea – 20 ms & 4 NCR, highest sensitivity setting

  • Estimate μNCI, σNCI analytically, derived from 1 ms dump

statistic σ1ms and from NCI noise samples directly

Matlab Experiment

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IGNSS 2016, 6-8 Dec 2016, UNSW,Sydney | 12

Matlab Experiment - Statistics

5 10 15 20 25 1 Strong 6 Strong 6 Strong, 1 MAI 6 Strong, 2 MAI Mean NC Noise Test Scenario

Mean NC Noise for 4 ms x 8 NCR

Mean(Est) Mean(Meas 1ms) Meas Mean 1 2 3 4 5 6 7 1 Strong 6 Strong 6 Strong, 1 MAI 6 Strong, 2 MAI StdDev NC Noise Scenario

StdDev NC Noise for 4 ms x 8 NCR

StdDev(Est) StdDev(Meas 1ms) Meas StdDev 5 10 15 20 1 Strong 6 Strong 6 Strong, 1 MAI 6 Strong, 2 MAI Mean NC Noise Test Scenario

Mean NC Noise for 20 ms x 4 NCR

Mean(Est) Mean(Meas 1ms) Meas Mean 1 2 3 4 5 6 7 1 Strong 6 Strong 6 Strong, 1 MAI 6 Strong, 2 MAI StdDev NC Noise Scenario

StdDev NC Noise for 20 ms x 4 NCR

StdDev(Est) StdDev(Meas 1ms) Meas StdDev

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Matlab Experiment - Thresholds

10 20 30 40 50 1 Strong 6 Strong 6 Strong, 1 MAI 6 Strong, 2 MAI Threshold Value Scenarios

Thresholds for 4 ms x 8 NCR Using Various Techniques

T(2 s1ms) T(3 s1ms) T(2 s1ms) T(3 s1ms) T(2 sNC) T(3 sNC) T5% T1% 10 20 30 40 1 Strong 6 Strong 6 Strong, 1 MAI 6 Strong, 2 MAI Threshold Scenarios

Thresholds for 20 ms x 4 NCR Using Various Techniques

T(2 s1ms) T(3 s1ms) T(2 s1ms) T(3 s1ms) T(2 sNC) T(3 sNC) T5% T1%

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  • Legacy firmware tested against modified firmware with Spirent

GSS8000 power gradually reduced using a splitter

– Improvement of 6 dB achieved (as predicted) BUT – Legacy firmware stopped navigating at -2 dB, but only lost lock at -1 dB, whereas new firmware navigated down to -4 dB – Removing signal level quality thresholds allowed legacy receiver to track & navigate down to -1 dB, so only a 3 dB improvement

  • Tuning the new receiver to use 10 ms x 8 NCR and repeating the

test without the splitter gave the new firmware a 5 dB improvement

  • ver the legacy firmware
  • Once 20 ms x 4 NCR tracking is improved, an additional 1.6 dB can

be expected.

Simulator Tests

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  • Improved signal processing and other changes

have improved Kea sensitivity by about 8 dB

  • Thresholds are now determined adaptively using

corrrelator noise taps

  • Longer coherent integration reduces squaring

losses

  • Further 1.5 dB improvement possible

Conclusion

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Questions