ERUO: a spectral processing routine for the MRR-PRO

The Micro Rain Radar (MRR) PRO is a K-band Doppler weather radar, using frequency modulated continuous wave (FMCW) signals, developed by Metek Meteorologische Messtechnik GmbH (Metek) as successor to the MRR-2. Benefiting from four datasets collected during two field campaigns in Antarctica and Switzerland, we developed a processing library for snowfall measurements, named ERUO (Enhancement and Reconstruction of the spectrUm for the MRR-PRO), with a two-fold objective. Firstly, the proposed method addresses a series of issues plaguing the radar variables, which include interference lines, power drops at the extremes of the Doppler spectrum and abrupt cutoff of the transfer function. Secondly, the algorithm aims to improve the quality of the final variables, by lowering the minimum detectable equivalent attenuated reflectivity factor and extending the valid Doppler velocity range through antialiasing. The performance of the algorithm has been tested against the measurements of a co-located W-band Doppler radar. Information from a close-by X-Band Doppler dual-polarization radar has been used to exclude unsuitable radar volumes from the comparison. Particular attention has been dedicated to verify the estimation of the meteorological signal in the spectra covered by interferences.

Evaluation of the performance of algorithms for synthesizing radar images using Cuda technology

The features of the implementation of the algorithm for the synthesis of detail radar images for an aperture synthesis radar using the built-in functions of the Cuda library are presented. The estimation of computational complexity from the standpoint of the organization of parallel computing on Nvidia GPUs is given. The estimation of the real performance of radar synthesis is given, taking into account the volume and features of the placement of primary radar information.

Autonomous Trajectory Planning and Control of Anti-Radiation Loitering Munitions under Uncertain Conditions

As an autonomous system, an anti-radiation loitering munition (LM) experiences uncertainty in both a priori and sensed information during loitering because it is difficult to accurately know target radar information in advance, and the sensing performance of the seeker is affected by disturbance and errors. If, as it does in the state of the art, uncertainties are ignored and the LM travels its planned route, its battle effectiveness will be severely restricted. To tackle this problem, this paper studies the method of autonomous planning and control of loitering routes using limited a priori information of target radar and real-time sensing results. We establish a motion and sensing model based on the characteristics of anti-radiation LMs and use particle filtering to iteratively infer the target radar information. Based on model predictive control, we select a loitering path to minimize the uncertainty of the target information, so as to achieve trajectory planning control that is conducive to the acquisition of target radar information. Simulation results show that the proposed method can effectively complete the autonomous trajectory planning and control of anti-radiation LMs under uncertain conditions.

On the Value of Sentinel-1 InSAR Coherence Time-Series for Vegetation Classification

Synthetic aperture radar (SAR) acquisitions are mainly deemed suitable for mapping dynamic land-cover and land-use scenarios due to their timeliness and reliability. This particularly applies to Sentinel-1 imagery. Nevertheless, the accurate mapping of regions characterized by a mixture of crops and grasses can still represent a challenge. Radar time-series have to date mainly been exploited through backscatter intensities, whereas only fewer contributions have focused on analyzing the potential of interferometric information, intuitively enhanced by the short revisit. In this paper, we evaluate, as primary objective, the added value of short-temporal baseline coherences over a complex agricultural area in the São Paulo state, cultivated with heterogeneously (asynchronously) managed annual crops, grasses for pasture and sugarcane plantations. We also investigated the sensitivity of the radar information to the classification methods as well as to the data preparation and sampling practices. Two supervised machine learning methods—namely support vector machine (SVM) and random forest (RF)—were applied to the Sentinel-1 time-series at the pixel and field levels. The results highlight that an improvement of 10 percentage points (p.p.) in the classification accuracy can be achieved by using the coherence in addition to the backscatter intensity and by combining co-polarized (VV) and cross-polarized (VH) information. It is shown that the largest contribution in class discrimination is brought during winter, when dry vegetation and bare soils can be expected. One of the added values of coherence was indeed identified in the enhanced sensitivity to harvest events in a small but significant number of cases.

Combination of information processing algorithms and the information system structure as a tool for building an information system of a network structure

Formulation of the problem. The development of information systems for collecting, processing and exchanging radar information occurs, on the one hand, in the direction of improving the technical characteristics of information processing facilities and data transmission facilities, on the other hand, in the direction of improving information processing algorithms and the structure of the information system. This article summarizes the possibilities for the development of information systems in the second direction.Purpose. Development of a variant of building an information system with a fully connected network structure and intended for the collection, processing and exchange of radar information.Results. The development, as a tool for building an information system of a network structure, is based on an integrated approach that provides for the use of an algorithm without branching solutions for information processing, a network semi-connected structure itself and network algorithms, a higher level than the level of collection and processing, for organizing functioning of information exchange in the network. The short processing time of information when entering it into the system makes it possible to create a dynamic array of homogeneous radar data, updated when radar information arrives from sources.Practical significance. Information systems, wholly or partly built on the basis of such an integrated approach, are applicable in areas such as air traffic control systems; multi-beam and multi-range radars (ornithological, meteo, etc.), radars for security complexes, incoherent spatially-separated radar information sources, combined into a system (for example, for studying the ionosphere).

Model for assessing the efficiency of departments anti-air defense

An urgent issue of modern local conflicts is the substantiation of the ability of air defense units to carry out their immediate tasks of providing air cover for ground forces. The solution to this issue is especially relevant in local conflicts, when the space in which it is necessary to perform the assigned task is stretched in width and depth. The purpose of the article is to develop a model for evaluating the effectiveness of ground forces cover by air defense units in new positional areas, which have changed in size in width and depth. The article proposes a model that makes it possible to assess the effectiveness of covering weapons and military equipment and infrastructure of ground forces by air defense units. The model was developed using the theory of probability. Conclusions. The proposed model involves the choice of a typical composition of internal and external sources of radar information, the direction of data exchange, the composition of the tasks of data processing, the formation of recommendations on the composition of the air defense team and their management. The developed model helps the commander of the air defense unit to evaluate the options for his structure, choose rational ones, with the best cover efficiency, and helps him make the right decision to repel air strikes. The model is proposed to be used in decision-making systems to help the commander make the right decision to cover ground units from the air. The proposed model will also be effective in automated decision-making systems.

Enriching Low-Density Terrain Maps from Satellite with Autonomous Robots Data

Satellite imagery and remote sensoring have been used for some years in agriculture to create terrain maps for different soil features (humidity, vegetation index, etc.). Multichannel information provides lots of data, but with a big drawback: the low density of information per surface unit; that is, the multi-channeled pixels correspond to a large surface, and a fine characterization of the targeted areas is not possible. In this research, the authors propose the enrichment of such data by the use of autonomous robots that explore and sense the same targeted area of the satellite but yielding a finer detail of terrain, complementing and fusing both information sources. The sensory elements of the autonomous robots are in the visual spectrum as well as in the near-infrared spectrum, together with Lidar and radar information. This enrichment will provide a high-density map of the soil to the final user to improve crops, irrigation, seeding and other agricultural processes. The methodology to fuse data and create high-density maps will be deep learning techniques. The system will be validated in real fields with the use of real sensors to measure the data given by satellites and robots’ sensors.

An Environmental Study on Tornado Path-Length, Longevity, and Width

A dataset maintained by the Storm Prediction Center (SPC) of 6300 tornado events from 2009–2015, consisting of radar-identified convective modes and near-storm environmental information obtained from Rapid Update Cycle and Rapid Refresh model analysis grids, has been augmented with additional radar information related to the low-level mesocyclones associated with tornado longevity, path-length, and width. All EF2–EF5 tornadoes, in addition to randomly selected EF0–EF1 tornadoes, were extracted from the SPC dataset, which yielded 1268 events for inclusion in the current study. Analysis of that data revealed similar values of the effective-layer significant tornado parameter for the longest-lived (60+ min) tornadic circulations, longest-tracked (≥ 68 km) tornadoes, and widest tornadoes (≥ 1.2 km). However, the widest tornadoes occurring west of –94° longitude were associated with larger mean-layer convective available potential energy, storm-top divergence, and low-level rotational velocity. Furthermore, wide tornadoes occurred when low-level winds were out of the southeast resulting in large low-level hodograph curvature and near-surface horizontal vorticity that was more purely streamwise compared to long-lived and long-tracked events. On the other hand, tornado path-length and longevity were maximized with eastward migrating synoptic-scale cyclones associated with strong southwesterly wind profiles through much of the troposphere, fast storm motions, large values of bulk wind difference and storm-relative helicity, and lower buoyancy.