Detecting Targets above the Earth’s Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1

Hu, Changjiang; Benson, Craig R.; Park, Hyuk; Camps, Adriano; Qiao, Li; Rizos, Chris

doi:10.3390/rs11192327

Cited by 15 publications

(9 citation statements)

References 33 publications

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“…It appears that from the CRB presented in this contribution, it is possible to deduce the specific scenario studied in [28]. However, in other scenarios, the Doppler shifts of the LOS and the NLOS signals may be different (e.g., if the signal is reflected from a moving object [47]) and a more general approach is needed, for which the result in [28] is not valid and the proposed CRB provides an answer.…”

Section: Comparison With Existing Literaturementioning

confidence: 95%

Joint Delay-Doppler Estimation Performance in a Dual Source Context

et al. 2020

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Evaluating the time-delay, Doppler effect and carrier phase of a received signal is a challenging estimation problem that was addressed in a large variety of remote sensing applications. This problem becomes more difficult and less understood when the signal is reflected off one or multiple surfaces and interferes with itself at the receiver stage. This phenomenon might deteriorate the overall system performance, as for the multipath effect in Global Navigation Satellite Systems (GNSS), and mitigation strategies must be accounted for. In other applications such as GNSS reflectometry (GNSS-R) it may be interesting to estimate the parameters of the reflected signal to deduce the geometry and the surface characteristics. In either case, a better understanding of this estimation problem is directly brought by the corresponding lower performance bounds. In the high signal-to-noise ratio regime of the Gaussian conditional signal model, the Cramér-Rao bound (CRB) provides an accurate lower bound in the mean square error sense. In this article, we derive a new compact CRB expression for the joint time-delay and Doppler estimation in a dual source context, considering a band-limited signal and its specular reflection. These compact CRBs are expressed in terms of the baseband signal samples, making them especially easy to use whatever the baseband signal considered, therefore being valid for a variety of remote sensors. This extends existing results in the single source context and opens the door to a plethora of usages to be discussed in the article. The proposed CRB expressions are validated in two representative navigation and radar examples.

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Section: Comparison With Existing Literaturementioning

confidence: 95%

Joint Delay-Doppler Estimation Performance in a Dual Source Context

et al. 2020

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“…(6) Remove other noises: Hu et al reported 14 DDM data with anomalies [35]. These abnormal data contain not only the normal DDM waveform but also abnormal bright spots and other weak signals.…”

Section: Data Quality Controlmentioning

confidence: 99%

Improving the SSH Retrieval Precision of Spaceborne GNSS-R Based on a New Grid Search Multihidden Layer Neural Network Feature Optimization Method

Wang

Zheng

et al. 2022

Remote Sensing

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The altimetry precision of conventional spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) is limited, and the error models are complicated. To compensate for the shortcomings of conventional methods, we present a new grid search multihidden layer neural network feature optimization method (GSMHLFO) for sea surface height (SSH) retrieval. Firstly, the GSMHLFO is constructed by combining the multihidden layer neural network, feature engineering, and a grid search algorithm. Moreover, the retrieval performance of the GSMHLFO and its sensitivity to various features are analyzed. By analyzing 14 feature sets with different information details, we concluded that the elevation, signal-to-noise ratio (SNR), atmospheric delay, and ocean wind speed can provide essential contributions to the SSH retrieval based on GSMHLFO. Secondly, the Technical University of Denmark 18 mean sea surface (DTU18 MSS), which is corrected by the TPXO8 global tide model, was used to verify the GSMHLFO. The number of hidden layers and neurons was optimized using the grid search algorithm. The experimental results show that the proposed GSMHLFO with four hidden layers and 200 neurons per layer has a better retrieval performance. Compared with DTU18, the mean absolute difference (MAD), the root mean square error (RMSE), and the Pearson correlation coefficient (PCC) equal 4.23 m, 5.94 m, and 0.98, respectively. The retrieval precision obtained is significantly improved compared to that reported in the literature for the TDS-1 SSH retrieval. Finally, the retrieval performance of the GSMHLFO and the traditional HALF single-point retracking method were compared. The precision of GSMHLFO is higher than that of traditional retracking method according to MAD, RMSE, and PCC, which are increased by 32.86, 25.00, and 8.99%. The GSMHLFO will provide innovative theoretical and methodological support for the high-precision SSH retrieval of GNSS-R altimetry satellites in the future.

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“…Objects on the Earth's surface that are susceptible to being detected using GNSS-R instruments include sea ice [39], manmade objects on the sea surface [14,15], oil slicks [40], etc. Their detection is based on the difference of the reflection coefficient between the sea around it and the target.…”

Section: Target Detectionmentioning

confidence: 99%

“…This technology has been proven to work in five space-based missions: UK-DMC [2], TDS-1 [3,4], CYGNSS [5,6,7], BF-1 [8], and more recently FSSCat [9]. The former was only a proof-of-concept mission that demonstrated the capabilities and potential of GNSS-R from space, whereas TDS-1 and CYGNSS have provided a wealth of GNSS-R data that has been successfully used for sea state retrieval [4], sea ice detection [10], soil moisture [11,12,13], target detection [14,15], and waste on the ocean surface, among others.…”

Section: Introductionmentioning

confidence: 99%

Airborne GNSS-R: A Key Enabling Technology for Environmental Monitoring

Perez-Portero

Muñoz-Martín

Park

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Weather forecast using GNSS-R relies, to a large extent, on data acquired by airborne and spaceborne platforms due to the extended coverage that can be achieved. GEO satellites can retrieve measurements over large areas, but with spatial resolutions on the order of thousands of meters per pixel. LEO satellites with polar orbits can provide measurements over the entire world, but they can not provide good spatial nor high temporal resolutions by themselves. LEO constellations of small satellites have the same coverage with improved spatial resolution and revisit times by using data fusion techniques. High-Altitude Pseudo-Satellites (HAPS) offer an excellent temporal and spatial resolution, but only over limited areas. In this work, the potential of using hosted GNSS-R payloads on commercial aircraft is explored as an alternative and cost-effective means to achieve a very high resolution and a very low revisit time for regional environmental applications. Finally, a case study of soil moisture monitoring over Europe is presented, including an analysis of the expected performance.

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Detecting Targets above the Earth’s Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1

Cited by 15 publications

References 33 publications

Joint Delay-Doppler Estimation Performance in a Dual Source Context

Joint Delay-Doppler Estimation Performance in a Dual Source Context

Improving the SSH Retrieval Precision of Spaceborne GNSS-R Based on a New Grid Search Multihidden Layer Neural Network Feature Optimization Method

Airborne GNSS-R: A Key Enabling Technology for Environmental Monitoring

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