A Novel Imaging Algorithm for High-Resolution Wide-Swath Space-Borne SAR Based on a Spatial-Variant Equivalent Squint Range Model

High-Resolution Wide-Swath (HRWS) is an important development direction of space-borne Synthetic Aperture Radar (SAR). The two-dimensional spatial variation of the Doppler parameters is the most significant characteristic of the sliding spotlight space-borne SAR system under the requirements of HRWS. Therefore, the compensation of the two-dimensional spatial variation is the most challenging problem faced in the imaging of HRWS situations. The compensatory approach is then proposed to address this problem in this paper. The spatial distribution of the Doppler parameters for the HRWS space-borne SAR data in the sliding spotlight working mode is firstly analyzed, based on which a Spatial-Variant Equivalent Slant Range Model (SV-ESRM) is put forward to accurately formulate the range history for the distributed target. By introducing an azimuth-varying term, the SV-ESRM can precisely describe the range history for not only central targets but also marginal targets, which is more adaptive to the HRWS space-borne SAR requirements. Based on the SV-ESRM, a Modified Hybrid Correlation Algorithm (MHCA) for HRWS space-borne SAR imaging is derived to focus the full-scene data on one single imaging processing. A Doppler phase perturbation incorporated with the sub-aperture method is firstly performed to eliminate the azimuth variation of the Doppler parameters and remove the Doppler spectrum aliasing. Then, an advanced hybrid correlation is employed to achieve the precise differential Range Cell Migration (RCM) correction and Doppler phase compensation. A range phase perturbation method is also utilized to eliminate the range profile defocusing caused by range-azimuth coupling for marginal targets. Finally, a de-rotation processing is performed to remove the azimuth aliasing and the residual azimuth-variance and obtain the precisely focused SAR image. Simulation shows that the SAR echoes for a 20 km × 20 km scene with a 0.25 m resolution in both the range and azimuth directions could be focused precisely via one single imaging processing, which validates the feasibility of the proposed algorithm.

Advanced open-source tools for detecting and analyzing peaks in cloud radar Doppler spectra

<p>Cloud radar Doppler spectra contain vertically highly resolved valuable information about the hydrometeors present in the cloud. A mixture of different hydrometeor types can lead to several peaks in the Doppler spectrum due to their different fall speeds, giving a hint about the size/ density/ number of the respective particles. Tools to separate and interpret peaks in cloud radar Doppler spectra have been developed in the past, but their application is often limited to certain radar settings, or the code not freely available to other users.</p> <p>We here present the effort of joining two methods, which have been developed and published (Radenz et al., 2019; Kalesse et al., 2019) with the aim to make them insensitive to instrument type and settings, and available on GitHub, and applicable to all cloud radars which are part of the ACTRIS CloudNet network.</p> <p>A supervised machine learning peak detection algorithm (PEAKO, Kalesse et al., 2019) is used to derive the optimal parameters to detect peaks in cloud radar Doppler spectra for each set of instrument settings. In the next step, these parameters are used by peakTree (Radenz et al., 2019), which is a tool for converting multi-peaked (cloud) radar Doppler spectra into a binary tree structure. PeakTree yields the (polarimetric) radar moments of each detected peak and can thus be used to classify the hydrometeor types. This allows us to analyze Doppler spectra of different cloud radars with respect to, e.g. the occurrence of supercooled liquid water or ice needles/columns with high linear depolarisation ratio (LDR).</p>

Turbulence statistics from three different nacelle lidars

Atmospheric turbulence can be characterized by the Reynolds stress tensor, which consists of the second-order moments of the wind field components. Most of the commercial nacelle lidars cannot estimate all components of the Reynolds stress tensor due to their limited number of beams; most can estimate the along-wind velocity variance relatively well. Other components are however also important to understand the behavior of, e.g., the vertical wind profile and meandering of wakes. The SpinnerLidar, a research lidar with multiple beams and a very high sampling frequency, was deployed together with two commercial lidars in a forward-looking mode on the nacelle of a Vestas V52 turbine to scan the inflow. Here, we compare the lidar-derived turbulence estimates with those from a sonic anemometer using both numerical simulations and measurements from a nearby mast. We show that from these lidars, the SpinnerLidar is the only one able to retrieve all Reynolds stress components. For the two- and four-beam lidars, we study different methods to compute the along-wind velocity variance. By using the SpinnerLidar's Doppler spectra of the radial velocity, we can partly compensate for the lidar's probe volume averaging effect and thus reduce the systematic error of turbulence estimates. We find that the variances of the radial velocities estimated from the maximum of the Doppler spectrum are less affected by the lidar probe volume compared to those estimated from the median or the centroid of the Doppler spectrum.

A Coherent on Receive X-Band Marine Radar for Ocean Observations

Marine radars are increasingly popular for monitoring meteorological and oceanographic parameters such as ocean surface wind, waves and currents as well as bathymetry and shorelines. Within this paper a coherent on receive marine radar is introduced, which is based on an incoherent off the shelf pulsed X-band radar. The main concept of the coherentization is based on the coherent on receive principle, where the coherence is achieved by measuring the phase of the transmitted pulse from a leak in the radar circulator, which then serves as a reference phase for the transmitted pulse. The Doppler shift frequency can be computed from two consecutive pulse-pairs in the time domain or from the first moment of the Doppler spectrum inferred by means of a short time Fast Fourier Transform. From the Doppler shift frequencies, radial speed maps of the backscatter of the ocean surface are retrieved. The resulting backscatter intensity and Doppler speed maps are presented for horizontal as well as vertical polarization, and discussed with respect to meteorological and oceanographic applications.

Estimating Coastal Winds by Assimilating High-Frequency Radar Spectrum Data in SWAN

Many activities require accurate wind and wave forecasts in the coastal ocean. The assimilation of fixed buoy observations into spectral wave models such as SWAN (Simulating Waves Nearshore) can provide improved estimates of wave forecasts fields. High-frequency (HF) radar observations provide a spatially expansive dataset in the coastal ocean for assimilation into wave models. A forward model for the HF Doppler spectrum based on first- and second-order Bragg scattering was developed to assimilate the HF radar wave observations into SWAN. This model uses the spatially varying wave spectra computed using the SWAN model, forecast currents from the Navy Coastal Ocean Model (NCOM), and system parameters from the HF radar sites to predict time-varying range-Doppler maps. Using an adjoint of the HF radar model, the error between these predictions and the corresponding HF Doppler spectrum observations can be translated into effective wave-spectrum errors for assimilation in the SWAN model for use in correcting the wind forcing in SWAN. The initial testing and validation of this system have been conducted using data from ten HF radar sites along the Southern California Bight during the CASPER-West experiment in October 2017. The improved winds compare positively to independent observation data, demonstrating that this algorithm can be utilized to fill an observational gap in the coastal ocean for winds and waves.

Azimuth Multichannel Reconstruction Based on Advanced Hyperbolic Range Equation

To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, almost all azimuth multichannel reconstruction algorithms have been mainly based on conventional hyperbolic range equation (CHRE), but the accuracy of the CHRE model is usually not suitable for the HRWS mode, especially for high resolution and large squint observation cases. In this study, the azimuth multichannel signal model based on the advanced hyperbolic range equation (AHRE) is established and analyzed. The major difference between multichannel signal models based on CHRE and AHRE is the additional time-varying phase error between azimuth channels. The time-varying phase error is small and can be ignored in the monostatic DPCMAB SAR system, but it must be considered and compensated in the distributed DPCMAB SAR system. In addition to the time-varying phase error, additional Doppler spectrum shift and extended Doppler bandwidth should be considered in the squint case during azimuth multichannel reconstruction. The azimuth multichannel reconstruction algorithm based on AHRE is proposed in this paper. Before multichannel reconstruction and combination, time-varying phase errors between azimuth channels were first compensated, and the range-frequency-dependent de-skewing function was derived to remove the two-dimension (2D) spectrum tilt to avoid azimuth under-sampling. Then, azimuth multichannel data were reconstructed according to the azimuth multichannel impulse response based on AHRE. Finally, the range-frequency dependent re-skewing function was introduced to recover the tilted 2D spectrum. Simulation results on both point and distributed targets validated the proposed azimuth multichannel reconstruction approach.

Ocean Waves in K-distributed Clutter

<div> <div> <div> <p>Two examples of low grazing angle radar sea clutter, both well described by the compound K-distribution model, are studied. Pulse Doppler processing is applied to obtain two dimensional range-time textures for the intensity, centroid and width of the Doppler spectrum. The first example exhibits a monochromatic swell pattern, allowing phase averaging to be applied to the textures. The second example has a more typical ocean wave spectrum. The intensity textures are gamma distributed, consistent with the compound K-distribution model, but the Doppler spectrum centroid and width textures are also found to be gamma distributed. Based on this analysis, a new method for simulation of coherent radar sea clutter is proposed, where separate memoryless nonlinear transformations are applied to a simulated water surface to generate the spatially and temporally varying intensity, centroid and width of the Doppler spectrum. The method builds on the evolving Doppler spectrum model for radar sea clutter simulation and established methods for simulation of water surfaces. </p> </div> </div> </div>

Ocean Waves in K-distributed Clutter

<div> <div> <div> <p>Two examples of low grazing angle radar sea clutter, both well described by the compound K-distribution model, are studied. Pulse Doppler processing is applied to obtain two dimensional range-time textures for the intensity, centroid and width of the Doppler spectrum. The first example exhibits a monochromatic swell pattern, allowing phase averaging to be applied to the textures. The second example has a more typical ocean wave spectrum. The intensity textures are gamma distributed, consistent with the compound K-distribution model, but the Doppler spectrum centroid and width textures are also found to be gamma distributed. Based on this analysis, a new method for simulation of coherent radar sea clutter is proposed, where separate memoryless nonlinear transformations are applied to a simulated water surface to generate the spatially and temporally varying intensity, centroid and width of the Doppler spectrum. The method builds on the evolving Doppler spectrum model for radar sea clutter simulation and established methods for simulation of water surfaces. </p> </div> </div> </div>