ICEYE operates the first and largest commercial small-satellite based SAR constellation offering all-weather, day-and-night Earth observation capabilities, and proposes a new collection strategy, called spotlight extended dwell. In dwell mode, the satellite continuously illuminates a spot on the ground for 25 seconds. The extremely long imaging duration increases the informative content of the SAR data and allows for many interesting applications, using the amplitude and phase information. We illustrate the potential of dwell mode data products, describing the improved moving target detection and coherent target detection, using these data products.

ICEYE operates the first and largest commercial small-satellite-based synthetic aperture radar (SAR) constellation offering all-weather, day-and-night Earth observation capabilities. This paper introduces a new collection mode, called spotlight extended dwell. In dwell mode, the satellite continuously illuminates a spot on the ground for 25 seconds. The extremely long imaging duration increases the information content of the SAR data and allows for many interesting applications. We illustrate the potential of dwell mode data products, such as the colorized subaperture image and SAR video. The provided examples show the potential of improved moving target detection, sea surface monitoring, and building damage assessment using these data products.

The ICEYE constellation features the first operational microsatellite based X-band SAR sensors suitable for all-weather day-and-night Earth Observation. As the ICEYE fleet grows it is opening up opportunities for experimentation and derivation of new products. This paper presents an update of the ICEYE fleet's capabilities as of early 2022. The latest results from three recent innovations will be presented - Spotlight Extended Area mode, Daily Coherent Ground Tracks, and waveform diversity for nadir and range ambiguity suppression.

A wide-swath synthetic aperture radar (SAR) image is known to be achievable by merging multistatic aliased data that is collected with an appropriate along-track displacement. However, in terms of both flexibility requirements like potentiality of both along-track and cross-track (XT) interferometry and design requirements like orbital safety, etc., a XT baseline is not only demanded but also unavoidable. In this work, a method is proposed in order to merge SAR data to recover the Doppler spectrum unambiguously for the nonzero XT baseline case. The algorithm is compared and validated by simulating an azimuth invariant multistatic SAR satellite constellation. The results are promising in terms of dealing with XT baselines.

Distributed SAR systems provide imaging capabilities that cannot be achieved by traditional monolithic satellites, thanks to the multiple angles and times of observation. In this paper the opportunities offered by a swarm of small satellite nodes operating in S-Band are discussed. The nodes fly in a close formation and operate in MIMO mode. All the$N$satellites transmit and receive the$N$pulses at the same time, following a Frequency Division Multiplexing (FDM) scheme. The work focuses in particular on the impact of the cross-track baselines on the resolution and on the quality of the signal reconstructed from the$N^{2}$channels. A first iteration of a distributed target processor based on adaptive frequency and channel treatment is hence proposed with the aim of effectively accounting for the slope-induced spectral shifts.

This study focuses on the optimization of phased array antenna excitation coefficients for a spaceborne synthetic aperture radar (SAR) system, comprising a joint mono static and bistatic configuration. An amplitude-only binary-coded Genetic algorithm (BGA) is proposed for the joint non-linear optimization problem to improve monostatic and bistatic SAR performance metrics. Based on the developed optimizer, significant performance improvement is achieved for both configurations.

Permanent Scatterers (PS) based on Synthetic Aperture Radar (SAR) data has been approved to be a feasible way to detect and monitor wide area ground subsidence at a low cost. Classical PS algorithms are focused on exploring the point-like radar targets while Quasi-PS (QPS) technique was proposed to manage the detection of both distributed and decorrelating targets. In this work, we explore the potential ability of PS and QPS analysis for subsidence monitoring with L- and X-band. A case study was conducted in Tianjin suburbs and the exploited SAR datasets were composed of 23 ALOS images acquired from 2009/4/24 to 2010/10/28 and 37 TerraSAR-X images from 2009/4/29 to 2010/11/11. The average subsidence velocity map and displacement history have been retrieved by PS and QPS analysis and the validation indicates good agreement between INSAR time series results and leveling data.

The radiometric performance of high-resolution synthetic aperture radar systems under degrading conditions such as the unintentional angular motion of the platform can only be analyzed and evaluated by modeling the whole imaging system through simulation. The raw data generation in the time domain achieves a realistic simulation procedure but, on the contrary, is time inefficient. The frequency-domain approach promises computational efficiency; however, it lacks the consideration of the antenna deviations. In this letter, we propose a frequency-domain stripmap-mode raw data generator of an extended scene, taking into consideration the platform angular deviations. The generated raw data are also validated, and the corresponding radiometric degradation is analyzed.

The performance of the high-resolution synthetic aperture radar (SAR) with moving-target indication and imaging systems can be analyzed and evaluated by modeling the whole imaging system through a simulation. The raw-data generation in the time domain achieves a realistic simulation procedure provided that computational complexity can be tolerable. In this letter, we proposed an efficient SAR raw-data generator of a scene that includes both static and moving targets. The raw data set of an extended scene and the moving target is simulated in the frequency domain separately and superposed in the time domain. The generated raw data for fixed and moving targets are also processed and analyzed.

A new data extrapolation technique which utilizes pole extraction based GPOF method is proposed to fit the scattering characteristics of an object. The proposed data extrapolation method is very efficient for data extrapolation when applied to the ISAR data. Modeling the data as the superposition of complex exponential signals, GPOF and Prony methods do not guarantee a stable prediction filter like the MCM method, while Burg ensures. Meanwhile, the performance of GPOF decreases with the increasing number of scattering centers but the radar images obtained using this extrapolated data is still more accurate than those obtained using the extrapolated data generated by other methods.