Through-the-Multilayered Wall Imaging Using Passive Synthetic Aperture Radar

Compressive Sensing (CS) is a very useful algorithm that is used in Ground Penetrating Radar (GPR) or through the wall imaging (TWI). CS generates high resolution images by decreasing the required measurement number. Although there are several recent studies about TWI with CS, they assume that targets are point like positioned at only discrete grid locations and wall thickness and its dielectric constant are perfectly is known. However, in real applications, it is not possible to know the exact target positions or estimate the wall parameters definitely. This work details the theory for CS based TWI and analyzes the imaging ability of CS. In addition, in this work, the effect of errors in unknown parameters on the imaging performance is analyzed and possible solutions are discussed.

Imaging techniques based on time reversal (TR) methods are particularly suited for detection of targets embedded in strongly scattering media. Through the wall scenario is a well known example of such media in which the scatterer is behind a wall which usually has a high reflection and is strongly scattering. Generally in TR imaging techniques we need to know the Green's function of the medium and the exact location of the transmitter and receiver antennas. We have applied a new method (Time Reversal - Independent Component Analysis (TR-ICA)) for through the wall imaging (TWI) in which imaging is done using only one transmitting antenna. Numerical simulations are presented to illustrate the capabilities of our proposed method. We have used two dimensional finite difference time domain code in our simulations and TR-ICA is used for selective focusing on perfect electric conductor scatterers behind a wall. We have applied the Green's function of the layered medium for a wall with typical parameters.

We propose Deep Learning (DL) as a framework for simultaneous waveform estimation and image reconstruction in passive synthetic aperture radar (SAR). We consider a passive imaging scenario where the scene is illuminated by source of opportunity with known position, but transmitting an unknown waveform. Image reconstruction is then posed as a machine learning task for which a recurrent neural network (RNN) can be constructed by unfolding the iterations of a proximal gradient descent algorithm. We separate the unknown waveform from the known components of the forward model and parameterize the network operator by the waveform coefficients. The image estimate generated by the RNN is then mapped back to the data space by a linear decoder stage, so that the waveform coefficients are refined in an unsupervised manner using passive SAR measurements as training data. As a result, network becomes a recurrent auto-encoder. In this architecture, forward propagation recovers scene reflectivity, and backpropagation solves for waveform coefficients. The non-convex training routine is initialized using the partially known SAR forward model, which helps ensure that the optimization problem converges to a satisfactory optimal point.

In this study, a printed monopole antenna with well-matched impedance bandwidth, reasonable gain and omnidirectional radiation pattern for through the wall imaging (TWI) applications is proposed. This work can be divided into two parts. The antenna design process which is the first part of this study can be explained with three design stages: antenna design is initiated with a base printed monopole antenna; parasitic patch elements are applied to the base antenna in order to obtain suitable frequency region below -10 dB and -20 dB in terms of reflection coefficient parameter; finally, the antenna is modified with the slotted ground to have better impedance match below -20 dB. Lastly, the antenna design with optimized parameters is used in a TWI scenario which is the second part of this paper. The scattering parameters are measured in two sequential time steps, and the difference in these two data is used as the data of the inversion scheme for TWI scenario. The imaging process is performed via the linear sampling method and the factorization method. The numerical simulations with a realistic wall model demonstrate that the antenna is capable of detecting objects behind the wall.

In recent years, Through the Wall Imaging (TWI) has become a topic of research interest due to its promising applications in home-land security situations.In this paper, the basic concept of Stepped-Frequency Continuous Wave (SFCW) for ranging the targets behind the wall is described.Imaging of targets is implemented using the back-projection algorithm.This paper also brings out the received signal model for detection of stationary human being behind the wall using SFCW approach.The detection is based on estimation of heart beat and respiratory motion.Simulation results of human detection through Doppler processing and the 2-D imaging of targets are brought out for illustration.MATLAB software has been used for the development of signal processing modules.

In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors. Keywords: sea ice, data assimilation, synthetic aperture radar, binary, Bayesian, probability, forward model (Published: 18 September 2015) Citation: Tellus A 2015, 67, 27218, http://dx.doi.org/10.3402/tellusa.v67.27218

More than 69,000 people died following the magnitude 8.0 Wenchuan earthquake of May 12, 2008. Bad weather hampered relief efforts, and in some cases rescuers had to trek into the disaster area on foot and search for trapped survivors by hand as roads were blocked by debris. Due to travel difficulties, spatial information needs to be extracted in the disaster area by remote sensing techniques. The main problem focused on in this paper is how to use the all-weather and all-day/night capability of Synthetic Aperture Radar (SAR) to extract primary seismic disaster information after the earthquake. Using air- and space-borne SAR images with different bands, polarizations and incidence angles, including multi-polarization X-band air-borne data, C-band polarimetric Radarsat-2, X-band TerraSAR-X with high resolution, multi-polarization X-band COSMO-SkyMed and L-band multi-polarization ALOS-PalSAR space-borne data, we perform image characteristics analysis of landslides. Obvious differences can be recognized between old and new landsides in SAR images with different bands. Multi-polarization SAR can play an important role in landslide discrimination. Two SAR images at different incidence angles do not provide much more information if the difference between the two angles is small. Landslide recognition accuracy strongly depends on the direction of view, especially for large incidence angles, in which case the characteristic difference for landslide recognition is great. There are different polarization responses between a landslide and its surroundings that can be used to recognize the landslide. Interferometric SAR images, on the other hand, do not provide good recognition capability due to temporal decorrelation and resolution. Meanwhile, information extraction of barrier lakes using different resolution and incidence angle SAR images is analyzed in this paper; small incidence angles and high resolutions improve the object recognition and information extraction of barrier lakes.

Passive synthetic aperture radar (SAR) using Global Navigation Satellite System (GNSS) signals as the illuminators of opportunity has a relative coarse range resolution due to the narrow bandwidth of the navigation signals and the bi-static acquisition geometry, limiting its application for high-resolution requirement. In this paper, an image formation algorithm for range resolution improvement is investigated. A modified second order differentiation based method is applied on the range compressed signal of reference channel and surveillance channel, improving range resolution and keeping side-lobes at a lower level simultaneously, and the phase of processed signal is preserved by phase multiplication compensation for the following azimuth compression processing. To validate the effectiveness and benefits of the proposed algorithm in terms of range resolution and targets side-lobes, point targets simulation under different situations and real field experiment with strong scatters are conducted and discussed. Compared with the traditional GNSS-based passive SAR imaging algorithm, the results of the proposed method show that the imaging performance can be improved for better range resolution and lower side-lobes, benefitting for better distinguishing nearby targets and recognizing weak targets.

Power distribution and correlation characteristic of cochannel interference are analyzed in Ship-based Passive Synthetic Impulse and Aperture Radar (SIAR) scenario. Characteristic of interference at interested range bin is estimated accurately using echoes from some positive and all negative frequency range bins after stretching with cosine weight. Thereafter, Karhunen-Loeve transformation is used to the echo from interested range bin and interference is suppressed provided that its power is stronger than radar echoes. Theoretical analysis and suppression procedure presented are confirmed by real data acquired in year 2005.

An assessment of marine atmospheric boundary layer roll detection using Sentinel-1 SAR data

Approximately 70% of South Korea’s land is mountainous, and as expressways and national highways have expanded across this terrain, extensive roadside slopes have been formed. Although various reinforcement and stabilization measures were applied during road construction, failures, such as collapses and settlements, continue to occur due to heavy rainfall, typhoons, and other extreme weather events. In recent years, South Korea has initiated research efforts to utilize imagery acquired from Synthetic Aperture Radar (SAR) satellites to detect long-term ground displacements along roadside slopes and support preparedness for potential geohazards. This study assessed the detectability of ground displacements on road slopes by considering the motion direction and incidence angle characteristics of SAR satellites over the Korean Peninsula. SAR satellites can acquire high-resolution images regardless of daylight or weather conditions. Furthermore, interferometric techniques (InSAR) enable the generation of digital elevation model (DEM) with sub-meter accuracy and ground displacement estimations with millimeter-level precision. However, as radar satellites observe the Earth at oblique angles relative to their flight direction and employ side-looking geometry perpendicular to the line of sight, geometric distortion may occur in areas with highly irregular terrain. In such conditions, shadowing effects—including layover and radar shadow can arise where radar signals are unable to reach. Steep road slopes are particularly susceptible to these limitations, and accurate observation may be impossible depending on the satellite’s motion direction and incidence angle. Therefore, before applying SAR data to slope-monitoring studies, it is essential to assess the feasibility of observations for the target slopes. In this study, the motion and observation characteristics of the Sentinel-1B satellite which continuously acquires imagery across the Korean Peninsula at approximately 12 day intervals were analyzed to evaluate the feasibility of observing road slopes nationwide. To evaluate the SAR observation feasibility for road slopes in South Korea, the evaluation was conducted in three stages: 1. Road-slope information, road networks, and other required datasets were compiled and converted into geospatial (Geographic Information System) data formats. 2. SAR imagery covering the Korean Peninsula was collected, and satellite motion direction and incidence angles were derived from image header files and metadata, then converted into raster datasets. 3. The relative spatial relationship between the road-slope data and satellite-observation data was analyzed, and based on these results, the observation feasibility of each road slope was evaluated considering the effects of the SAR satellite’s motion direction and incidence angle. As a result, the study evaluated the SAR observation feasibility for approximately 27,000 road slopes across South Korea. Considering the orbit and incidence-angle characteristics of the Sentinel-1B satellite, it was found that about 66% of all slopes were located within the satellite’s effective imaging range.AcknowledgementThis research is based upon work supported by Korea Institute of Civil Engineering & Building Technology(KICT), Project No.20250285-001

In this study, a truncated singular value decomposition (TSVD) based computationally efficient through the wall imaging (TWI) is addressed. Mainly, two different scenarios with identical and non-identical multiple scatterers behind the wall have been considered. The scattered data are processed with special scheme in order to improve quality of the results and measurements are performed at four different frequencies. Next, effects of selecting truncation threshold in TSVD methods are analyzed and a detailed quantitative comparison is provided to demonstrate capabilities of these TSVD methods over selection of truncation threshold.

Art. 6374334

The Radarsat synthetic aperture radar (SAR) instrument was designed to be very versatile in its capabilities, and the standard operations of the Radarsat SAR use only a subset of the potential imaging modes. The standard mode best suited to ship monitoring provides coverage of a swath of over 500 km, but large scale ship surveillance at mid-latitudes is limited in this mode by two factors. the interval between repeat coverage and the steep incidence angle. Ideally, the system would allow any critical area to be imaged once each day, and at higher incidence angles, where ship-to-clutter ratio is better. This paper considers alternative modes of operation for the Radarsat SAR which provide enhanced ship monitoring. These modes operate with wider beams and use significantly lower pulse repetition frequencies (PRF) and longer receive windows than the standard SAR modes. The proposed form of ship surveillance mode can be demonstrated with the system currently in orbit using the existing command data and downlink format, but with modified processing. It can provide coverage of a total swath of around 1000 km at incidence angles of 40 degrees and above. Some modest modifications which might be incorporated into a future SAR system are also suggested to enhance these capabilities further.

Through-the-wall imaging (TWI) is a challenging problem, even if the wall parameters and characteristics are known to the system operator. Proper target classification and correct imaging interpretation require the application of high resolution techniques using limited array size. In inverse synthetic aperture radar (ISAR), signal subspace methods such as Multiple Signal Classification (MUSIC) are used to obtain high resolution imaging. In this paper, we adopt signal subspace methods and apply them to the 2-D spectrum obtained from the delay-andsum beamforming image. This is in contrast to ISAR, where raw data, in frequency and angle, is directly used to form the estimate of the covariance matrix and array response vector. Using beams rather than raw data has two main advantages, namely, it improves the signal-to-noise ratio (SNR) and can correctly image typical indoor extended targets, such as tables and cabinets, as well as point targets. The paper presents both simulated and experimental results using synthesized and real data. It compares the performance of beam-space MUSIC and Capon beamformer. The experimental data is collected at the test facility in the Radar Imaging Laboratory, Villanova University.