An Algorithm for Detecting Coherence in Cyclone Global Navigation Satellite System Mission Level-1 Delay-Doppler Maps

An algorithm for detecting coherence in Cyclone Global Navigation Satellite System (CYGNSS) mission delayDoppler maps (DDMs) is presented. Because CYGNSS DDMs report only the observed power without phase information, the algorithm uses estimates of power spread within the DDM to flag coherency. Since the estimate used is a ratio of the powers in differing portions of the DDM, it is less sensitive to absolute power calibration and to the GPS C/A code type observed, and is applied to CYGNSS Level-1 uncalibrated DDMs. The basic detector formulation is described along with modifications to improve performance in lower signal-to-noise ratio (SNR) situations. The required detection thresholds are determined using matchups with CYGNSS Raw I/F mode measurements for which the DDM phase can be computed and used to identify coherence more precisely. Application of the final detector over a large CYGNSS data set suggests that approximately 8.9% of all inland returns are coherent. Inland regions persistently identified as coherent were found largely to be associated with the presence of water bodies. A smaller set of desert locations apparently having very low surface roughness were also found to be associated with persistent coherence. The detector was also applied to a set of ocean measurements, with the results showing that persistent coherence is limited to areas with sheltered waters. Ocean tests avoiding such regions indicate that the detector's false-alarm rate is approximately 0.0012% for the detection threshold used.

Automated summary

An algorithm for detecting coherence in CYGNSS mission delay-Doppler maps is presented, using estimates of power spread within the maps to flag coherence. Application of the algorithm to a large dataset shows that approximately 8.9% of inland returns are coherent, mostly associated with the presence of water bodies.