Experiments for multibeam Backscatter Adjustments on the NOAA Ship FAIRWEATHER

A series of experiments were conducted to adjust and normalize the acoustic backscatter acquired by Reson 8111 and 8160 systems. The dependency of the backscatter on the receiver gain, transmit power, pulse width and acquisition mode was analyzed. Empirical beam patterns are calculated as the difference between the backscatter measured by the sonars and the expected backscatter. Expected acoustic backscatter is estimated based on a mathematical model

Seafloor Characterization from Spatial Variation of Multibeam Backscatter vs. Grazing Angle

Backscatter vs. grazing angle, which can be extracted from multibeam backscatter data, depend on characteristics of the multibeam system and the angular responses of backscatter that are characteristic of different seafloor properties, such as sediment hardness and roughness. Changes in backscatter vs. grazing angle that are contributed by the multibeam system normally remain fixed over both space and time. Therefore, they can readily be determined and removed from backscatter data. The variation of backscatter vs. grazing angle due to the properties of sediments will vary from location to location, as sediment type changes. The sediment component of variability can be inferred using the redundant observations from different grazing angles in several small pieces of seafloor where the sediment property is uniform in any given piece of seafloor yet vary from one piece of the seafloor to another. Thanks to the multibeam survey (Roger Flood, State University of New York) at SAX 99 Project sponsored by Office of Naval Research (ONR), which had 800\% coverage in most of the survey area; there is a data set, which is suitable for investigating seafloor characterization. The investigation analyzed the spatial variation of the backscatter vs. grazing angle and compared that with ground truth sediment data. In this research, the 6.9 gigabytes raw multibeam data were cleaned using an automated outlier detection algorithm (Tianhang Hou, Lloyd Huff and Larry Mayer. 2001). Then, the surveyed area was equally divided into 52X78 rectangle working cells (4056), the side of each cell was about 20 meters. The backscatter vs. grazing angle of backscatter data for each cell is computed by averaging backscatter data by the corresponding beam numbers using all data with the same beam number from different survey lines. Systematic effects on the backscatter vs. grazing angle, caused by multibeam system hardware or software as well as system installation, were corrected in order to remove the asymmetric and skew effects. In order to easily evaluate the spatial variation of the backscatter vs. grazing angle, a graphic interface was developed. With a mouse click, the images based on different subsets of the data can be compared throughout the survey area. The subsets were created using specific beam numbers. These images for different beams show significant variations between nadir and off-nadir beams. These variations allow an interesting interpretation to be made of the images in light of seafloor characteristics, which were derived from ground truth data, such as sediment grain size, density and velocity

Wirelessly Powered Backscatter Communication Networks: Modeling, Coverage and Capacity

Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering this massive number of embedded devices is a key challenge of designing IoT since batteries increase the devices' form factors and battery recharging/replacement is difficult. To tackle this challenge, we propose a novel network architecture that enables D2D communication between passive nodes by integrating wireless power transfer and backscatter communication, which is called a wirelessly powered backscatter communication (WP-BackCom) network. In the network, standalone power beacons (PBs) are deployed for wirelessly powering nodes by beaming unmodulated carrier signals to targeted nodes. Provisioned with a backscatter antenna, a node transmits data to an intended receiver by modulating and reflecting a fraction of a carrier signal. Such transmission by backscatter consumes orders-of-magnitude less power than a traditional radio. Thereby, the dense deployment of low-complexity PBs with high transmission power can power a large-scale IoT. In this paper, a WP-BackCom network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad-hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. The backscatter nodes can harvest energy from and backscatter carrier signals transmitted by PBs. Furthermore, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of backscatter parameters, including backscatter duty cycle and reflection coefficient, as well as the PB density. The effects of the parameters on network performance are characterized.Comment: 28 pages, 11 figures, has been submitted to IEEE Trans. on Wireless Communicatio

Interannual variability in North American grassland biomass/productivity detected by SeaWinds scatterometer backscatter

We analyzed 2000–2004 growing-season SeaWinds Ku-band microwave backscatter and MODIS leaf area index (LAI) data over North America. Large anomalies in mid-growing-season mean backscatter and LAI, relative to 5-year mean values, occurred primarily in the western Great Plains; backscatter and LAI anomalies had similar spatial patterns across this region. Backscatter and LAI time series data for three ∼103 km2 regions in the western Great Plains were strongly correlated (r2 ∼ 0.6–0.8), and variability in mid-growing season values was well-correlated with annual precipitation (October through September). The results indicate that SeaWinds backscatter is sensitive to interannual variability in grassland biomass/productivity, and can provide an assessment that is completely independent of optical/near-infrared remote sensing instruments

Seafloor Characterization from Spatial Variation of Multibeam Backscatter vs. Best Estimated Grazing Angle

Backscatter vs. grazing angle, which can be extracted from multibeam backscatter data, depends on characteristics of the multibeam system and the angular responses of backscatter that are characteristic of different seafloor properties, such as sediment hardness and roughness. Changes in backscatter vs. grazing angle that are contributed by the multibeam system normally remain fixed over both space and time. Therefore, they can readily be determined and removed from backscatter data. The component of backscatter vs. grazing angle due to the properties of sediments varies from location to location, as the sediment changes. The sediment component of variability can be inferred using the redundant observations from different grazing angles in several small sections of seafloor assuming that the sediment property is uniform in any given section of seafloor yet varies from one section of the seafloor to another. The multibeam data used in this research is from the ONR sponsored STRATAFORM project. The location of the study area was the mid-outer continental shelf off New Jersey. A small subset (11 x 17 km) of the NJ multibeam survey was selected and divided into 1380 equal working cells. The backscatter vs. grazing angle dependence for each cell was computed by averaging backscatter data by the corresponding grazing angles using all data with the same grazing angle from different survey lines. Taking into account the effects of local topographic variations of the seabed, the estimated grazing angle for each beam has been computed from available adjacent soundings within a 15-meter radius using a least squares fit with a Butterfly weighting function. A graphic interface was developed to ease evaluation of the spatial variation of backscatter vs. grazing angle. With a mouse click, images based on different subsets of the data can be compared throughout the survey area. The subsets were created from specific grazing angles. These images show significant variations between nadir and off-nadir beams. Variations apparent in the images may provide some indication of the sediment (or seafloor) characteristics, which can be compared to ground truth data (sediment grain size) and measured values such as velocity and density

The high-frequency backscattering angular response of gassy sediments: Model/data comparison from the Eel River Margin, California

A model for the high-frequency backscatter angular response of gassy sediments is proposed. For the interface backscatter contribution we adopted the model developed by Jackson et al. @J. Acoust. Soc. Am. 79, 1410–1422 ~1986!#, but added modifications to accommodate gas bubbles. The model parameters that are affected by gas content are the density ratio, the sound speed ratio, and the loss parameter. For the volume backscatter contribution we developed a model based on the presence and distribution of gas in the sediment. We treat the bubbles as individual discrete scatterers that sum to the total bubble contribution. This total bubble contribution is then added to the volume contribution of other scatters. The presence of gas affects both the interface and the volume contribution of the backscatter angular response in a complex way that is dependent on both grain size and water depth. The backscatter response of fine-grained gassy sediments is dominated by the volume contribution while that of coarser-grained gassy sediments is affected by both volume and interface contributions. In deep water the interface backscatter is only slightly affected by the presence of gas while the volume scattering is strongly affected. In shallow water the interface backscatter is severely reduced in the presence of gas while the volume backscatter is only slightly increased. Multibeam data acquired offshore northern California at 95 kHz provides raw measurements for the backscatter as a function of grazing angle. These raw backscatter measurements are then reduced to scattering strength for comparison with the results of the proposed model. The analysis of core samples at various locations provides local measurements of physical properties and gas content in the sediments that, when compared to the model, show general agreement

Laboratory measurements of forward and backward scattering of laser beams in water droplet clouds

Many aspects of the forward and backward scattering in dense water droplet clouds were studied using a laboratory scattering facility. This system is configured in a lidar geometry to facilitate comparison of the laboratory results to current lidar oriented theory and measurements. The backscatter measurements are supported with simultaneous measurements of the optical density, mass concentration, and droplet size distribution of the clouds. Measurements of the extinction and backscatter coefficients at several important laser wavelength have provided data on the relationship between these quantities for laboratory clouds at .633, 1.06, and 10.6 microns. The polarization characteristics of the backscatter of 1.06 microns were studied using several different types of clouds. More recently, the laboratory facility was modified to allow range-resolved backscatter measurements at 1.06 microns. Clouds made up of 3 layers, each with its own density, can be constructed. This allows the study of the effect of cloud inhomogeneity on the forward and backscatter