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Phase unwrapping, also known as ambiguity resolution, is an underdetermined problem in which assumptions must be made to obtain a result in SAR interferometry (InSAR) time series analysis. This problem is particularly acute for distributed scatterer InSAR, in which noise levels can be so large that they are comparable in magnitude to the signal of investigation. In addition, deformation rates can be highly nonlinear and orders of magnitude larger than neighboring point scatterers, which may be part of a more stable object. The combination of these factors has often proven too challenging for the conventional InSAR processing methods to successfully monitor these regions. We present a methodology which allows for additional environmental information to be integrated into the phase unwrapping procedure, thereby alleviating the problems described above. We show how problematic epochs that cause errors in the temporal phase unwrapping process can be anticipated by the machine learning algorithms which can create categorical predictions about the relative ambiguity level based on the readily available meteorological data. These predictions significantly assist in the interpretation of large changes in the wrapped interferometric phase and enable the monitoring of environments not previously possible using standard minimum gradient phase unwrapping techniques.

The Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) onboard Mars Express, which operates between 0.1 and 5.5 MHz, suffered from a complete blackout for 10 days in September 2017 when observing on the nightside (a rare occurrence). Moreover, the Shallow Radar (SHARAD) onboard the Mars Reconnaissance Orbiter, which operates at 20 MHz, also suffered a blackout for three days when operating on both dayside and nightside. We propose that these blackouts are caused by solar energetic particles of few tens of keV and above associated with an extreme space weather event between 10 and 22 September 2017, as recorded by the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. Numerical simulations of energetic electron precipitation predict that a lower O-2(+) nighttime ionospheric layer of magnitude similar to 10(10) m(-3) peaking at similar to 90-km altitude is produced. Consequently, such a layer would absorb radar signals at high frequencies and explain the blackouts. The peak absorption level is found to be at 70-km altitude. Plain Language Summary Several instrument operations, as well as communication systems with rovers at the surface, depend on radio signals that propagate throughout the atmosphere of Mars. This is the case also for two radars that are currently working in Mars' orbit, sounding the ionosphere, surface, and subsurface of the planet. In mid-September 2017, a powerful solar storm hit Mars, producing a large amount of energetic particle precipitation over a 10-day period. We have found that high-energy electrons ionized the atmosphere of Mars, creating a dense layer of ions and electrons at similar to 90 km on the Martian nightside. This layer attenuated radar signals continuously for 10 days, stopping the radars to receive any signal from the planetary surface. In this work, we assess the properties of this layer in order to understand the implications of this kind of phenomenon for radar performance and communications.

The Dutch peatlands are a notoriously difficult region to monitor using InSAR. Low temporal coherence and signal-to-clutter levels necessitate the extraction of collective behaviour by the suppression of noise and clutter. Conventional techniques used to accomplish this include multilooking and phase-linking. The t-distributed Stochastic Neighbour Embedding (t-SNE) algorithm is a dimensionality reduction technique that aids in the analysis of large datasets. In this paper, we present an initial investigation into the suitability of the t-SNE algorithm to take the idea of extracting collective behaviour further. Similarly-behaved patches of land are automatically grouped together by the algorithm which aids in the specification of a functional model for that group. Our initial results show that the algorithm is able to successfully identify and group together areas in a scene which display similar behaviour over time. We also find that groups which display the same behaviour may also contain the same kinds of processing errors (for example unwrapping errors or cycle slips) and that these can also be automatically detected by the algorithm. We present this result as the first building block in an approach to smart InSAR data analysis which can learn from the data it is processing.

We present a new method for determining the total electron content (TEC) in the Martian ionosphere based on the time delay of received radar pulses of the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS) on board the Mars Express spacecraft. Previous studies of the same dataset have produced differing results for the day-side ionosphere, so it is useful to have an alternative way to compute the TEC in this region. This method iterates a model ionosphere in order to simultaneously match the ionospheric delays of the signals received by the radar’s two channels by finding the model which minimizes the root mean square error (RMSE) between the measured and simulated delays. Topographical information is obtained from data from the Mars Orbiter Laser Altimeter (MOLA) instrument. The model parameters are held constant for a given orbit, and a very good agreement between the simulated and measured delays is obtained. The TEC can then be inverted from the ionospheric model. Matching the delays of both channels simultaneously applies an additional constraint to the model which has not been made in previous studies. The model is additionally validated by matching the simulated pulses with the raw range-compressed measurements for one orbit. Finally, typical model parameters are compared to those obtained by previous studies, which are also simulated. The method is applied to orbits during moderate solar activity, and results show very good agreement with previous studies.

Noise correlation matrices and noise parameters are useful for low-noise amplifier designers seeking to achieve the best noise performance out of their circuits. In some applications, such as radio astronomy, antennas and antenna arrays are part of receivers and, thus, modeling of the antenna noise is useful. Currently, antenna noise is modeled through antenna efficiency. A more accurate information on the highest-possible antenna efficiency is obtained from its $S$ -parameters or noise parameters. In this letter, we show how the two-port $S$ -parameters of an antenna can be determined by measuring its reflection coefficients inside two specifically sized Wheeler caps, in addition to a free-space measurement. We use the measurement to determine the noise correlation matrices and noise parameters of an antenna. The challenges of constructing appropriately sized Wheeler caps are discussed, and a new way of constructing a Wheeler cap is shown.

An optical communications system employing intradyne reception and offline digital signal processing is tested over a 10.45 km link through the atmosphere. 40 GBaud transmission using binary phase-shift keying in the C-band is demonstrated and compared with laboratory measurements. Simultaneous photodetector measurements show that the turbulence in the atmospheric channel is representative for relevant and worst-case conditions in the geostationary satellite uplink scenario.

The noise correlation matrix and noise parameters are parameters of interest for antennas, and antenna arrays, particularly in the radio-astronomy context. For a passive network, such as an antenna, 2-port scattering parameters can be used to establish a noise correlation matrix and consequently the antenna noise parameters. In this paper we show how the 2-port scattering parameters of an antenna can be completely determined by measuring its reflection coefficient inside two specifically sized Wheeler caps, in addition to a free space measurement. We use the measurement to determine the noise correlation matrices and noise parameters of an antenna.

(1999). Informal carers of aphasic stroke patients: stresses and interventions. Aphasiology: Vol. 13, No. 12, pp. 889-900.

Background: Communication difficulties due to aphasia following stroke are particularly stressful to caregivers. Objective: To examine the impact of a psychoeducation programme on caregivers’ burden and stress and communication between the caregiver and aphasic stroke patient. Design: Randomized wait-list controlled trial with immediate or three-month delayed treatment. Setting: Three public hospital rehabilitation services in Sydney, Australia. Subjects: Thirty-nine caregivers of aphasic stroke patients, up to 12 months post stroke: 19 given immediate treatment and 20 in a delayed treatment control group. Interventions: Four-session weekly caregiver programme that included elements of education, support and communication skills conducted by a speech pathologist, social worker and clinical psychologist. Main measures: The General Health Questionnaire (GHQ) was used to measure caregiver stress, the Relatives’ Stress Scale was used to measure caregiver burden and a communication questionnaire was designed specifically for this project. Results: Thirty-one caregivers completed the study. Caregivers in the immediate treatment group had significant reductions in GHQ measured stress (GHQ mean (SD) at baseline= 6.26 (5.67), GHQ post treatment 3.21 (SD 4.20), P = 0.006). There was no improvement in wait-listed caregivers. Improvement was not maintained at three-month follow-up. There were no significant effects of the programme on communication skills or on caregiver burden. Conclusions: Stroke caregiver support, education and training programmes have short-term effects on caregiver stress levels but are likely to require ongoing involvement to maintain their effect.

(1999). Informal carers of aphasic stroke patients. A brief response. Aphasiology: Vol. 13, No. 12, pp. 927-928.

Alternatieve externe calibratie doelen (grote antennes, radio telescopen) komen naar voren als mogelijkheid voor het karakteriseren en ook volgen in de tijd van het radar instrument van BIOMASS