Airborne Systems Research Articles

Polarimetric SAR Time Series Change Analysis Over Agricultural Areas

This article proposes a change detection and analysis technique for monitoring the phenological development of agricultural vegetation by means of multitemporal Polarimetric Synthetic Aperture Radar (PolSAR) acquisitions. The technique relies on the generalized eigendecomposition of the polarimetric covariance matrices of the individual acquisitions. It both quantifies the magnitude of the change between PolSAR images acquired at different times and also provides an interpretation of occurred change in terms of the modified polarization states. This makes the algorithm suitable for investigating scattering dynamics associated with the phenological development of agricultural vegetation. To aid the interpretation of the changes detected, a representation based on the polarization states affected by the change process is proposed. The technique is evaluated using part of the multitemporal AGRISAR 2006 campaign data set. This data set consists of 12 quad-polarimetric images acquired by the German Aerospace Center (DLR) E-SAR airborne system at L-band from April 2006 to August 2006 over the Demmin test site. It covers large parts of the development cycle of different crop types. As a part of the evaluation, reference ground measurements are used to facilitate the interpretation of the data. The evaluation focuses on five important crop types: wheat, barley, rape, maize, and sugar beet. The results show that the proposed technique is able to detect and characterize different types of changes related to distinct development states of different crop types as the plant growing, maturation, and drying processes.

Application of Multiphysical Domain Modeling in Electrical System Simulation

The energy structure change of more electric aircraft makes the aircraft airborne system more complicated. Each subsystem realizes the transmission, interaction, and conversion of energy and information through the dynamic coupling and coordinated control of electrical energy, mechanical energy, hydraulic energy, and thermal energy. This paper applies the multiphysical domain modeling method with the parameter identification according to the original model data. Based on the power conversion relationship of electrical equipment, it defines the port with the power potential variable and flow variable and is supplemented by the information control. It can show the dynamic characteristics, power conversion, and loss characteristics of the device itself. The models can conveniently perform the large-scale system integration, which not only can build the complex electrical equipment formed by multiphysical domain models with the series connection but also can build a complex power supply system formed by multiphysical domain models with the parallel connection.

In-Flight Multichannel Calibration for Along-Track Interferometric Airborne Radar

Multichannel calibration is essential for detecting moving targets and for estimating their positions and velocities accurately. This article presents a fast and efficient calibration algorithm for the along-track multichannel systems, in particular for space-time adaptive processing (STAP) techniques. The proposed algorithm corrects the phase and magnitude offsets of the receive channels and also takes into account the Doppler centroid variation (e.g., caused by atmospheric turbulences) along the slant range and the azimuth time. The knowledge of the Doppler centroid variation is especially important for an accurate clutter covariance matrix estimation, which is required by STAP for efficient clutter suppression. Important calibration parameters and offsets are estimated directly from the range-compressed training data. The proposed algorithm is evaluated based on real multichannel X-band radar data acquired with DLR's airborne system F-SAR and compared with the state-of-the-art digital channel balancing technique. The experimental results show the potential of the proposed calibration algorithm toward real-time applications.

GENERIC LINEAR ARRAY SCANNER MODELING OF SPECTRAL IMAGING SYSTEMS CONTAINING LIMITED METADATA

Abstract. The National Geospatial-Intelligence Agency (NGA) designed the Generic Linear Array Scanner (GLAS) model for geopositioning images from both airborne and spaceborne linear array scanning systems, including pushbroom, whiskbroom, and panoramic sensors. Providers of hyperspectral imagery (HSI) historically have not populated products with high fidelity metadata to support downstream photogrammetric processing. To demonstrate recommended metadata population and exploitation using the GLAS model, NGA has generated example HSI products using data collected by NASA’s EO-1 Hyperion sensor and provided courtesy of the U.S. Geological Survey. This paper provides novel techniques for: 1) generating reasonably accurate initial approximations for GLAS metadata as a function of per-image metadata consisting of only timing information and the latitude and longitude values of the four corners of the image; and 2) identifying a vector of adjustable parameters and reasonable values for its a priori error covariance matrix that enable corrections to the metadata during a bundle adjustment. The paper describes applying these techniques to fourteen overlapping Hyperion images of the Alps, running a bundle adjustment as a function of tie points and optional ground control points, and demonstrating superior results to the previous polynomial based approach as quantified by the 3D errors at several ground check points.

Dynamic Reliability Model for Airborne Systems Based on Stochastic Petri Net

The reliability of the airborne systems have a significant influence on the safety of aircraft. The modern airborne systems have a high degree of automation and integration, which lead to obvious dynamic failure characteristics. Namely, system failure is not only dependent on the combination of units' failures but also related to their sequence. A dynamic reliability method for modeling airborne systems is proposed based on the stochastic Petri nets. Stochastic Petri nets are applied in reliability modeling for typical dynamic structures including warm standby, cold standby and load sharing, which are widely used in airborne systems. In this way, the dynamic (time-dependent) failure behaviors of the airborne system can be represented. In terms of the stochastic Petri net based reliability model, a reliability analysis method based on Monte Carlo simulation is proposed by generating system life samples for system reliability parameter calculation. Finally, an electrical power system is used as a case to illustrate the application and effectiveness of the present approaches. The results show that the difference by using the present method and the analytical method is below 2×10-7, which can be neglected in practice.

Research on the Testability Design System of Airborne System

Research on the Testability Design System of Airborne System

Health Prediction of Airborne System Based on Grey Prediction

With the development of civil aircraft technology, the scale of civil aviation industry in China is expanding, and the service capacity is gradually improving. Civil aviation industry has become an important industry in China’s economic development. With the increasing complexity, integration and intelligence of aircraft, it is more and more difficult to diagnose and maintain the fault of airborne system. The traditional fault diagnosis and maintenance methods cannot meet the requirements of modern aircraft development. Especially in the field of aircraft maintenance, the thought of aircraft maintenance has gone through the transformation process from “preventive maintenance” to “reliability maintenance”, and the maintenance method of aircraft has gradually changed from the previous scheduled maintenance to the situation-based maintenance.

Editorial for Special Issue “Applications of Synthetic Aperture Radar (SAR) for Land Cover Analysis”

Synthetic aperture radar (SAR) imaging systems derive microwave data, from space or airborne (piloted and remote piloted), that provide opportunities for the interpretation of many characteristics of the terrain surface. The increasing number of satellites equipped with SAR data acquisition systems that are being launched with a range of wavelengths, polarizations, and operating characteristics are enabling a better understanding of the earth’s environment, for such activities as vegetation analysis, forest inventories, land subsidence, and urban analysis. In addition, airborne systems for remote piloted systems and ground-based systems are available. This Special Issue presents six quality scientific papers on typical applications of SAR technologies. They include methods for the determination of above ground biomass (AGB), crop mapping using data from an advanced X-band system developed in Japan, analysis of natural and human-induced slow-rate ground deformations in the region of Campania, in Italy, the location of landslides caused by natural phenomena based on SAR images derived from the Japanese high-resolution Advanced Land Observing Satellite-2 (ALOS-2), and monitoring the size of refugee camps and their environmental impacts caused by the displacement of people from Myanmar to the Cox’s Bazar District, around Kutupalong, in Bangladesh. The paper concludes with some comments on the future directions of developments in SAR systems.

Generalized Polarimetric Entropy: Polarimetric Information Quantitative Analyses of Model-Based Incoherent Polarimetric Decomposition

Model-based incoherent polarimetric decomposition is a frequently used technique to analyze multilook data of polarimetric synthetic aperture radars (POLSARs). The purpose of this study is to analyze and compare different model-based incoherent polarimetric decomposition algorithms from the polarimetric information change aspect. For the input of a model-based incoherent polarimetric decomposition algorithm, polarimetric entropy was used to represent the polarimetric information of a coherency matrix. For the output of a model-based incoherent polarimetric decomposition algorithm, there are usually several decomposed components. To quantitatively represent their total polarimetric information, a new concept, generalized polarimetric entropy, was proposed which generalized the concept of polarimetric entropy based on the information entropy additivity of information theory. Generalized polarimetric entropy consists of two parts named as polarimetric power entropy and polarimetric residual entropy, respectively. Polarimetric power entropy describes the distribution status of the Span values of all decomposed components. Polarimetric residual entropy represents the residual randomness of all decomposed components. With the three new concepts, eight model-based incoherent polarimetric decomposition algorithms were compared and analyzed. Two real POLSAR images, respectively, derived by the E-SAR airborne system of Germany and the GF-3 satellite of China were used for the experiments. Experimental results had illustrated several useful conclusions.

Object Tracking Algorithm based on Improved Siamese Convolutional Networks Combined with Deep Contour Extraction and Object Detection Under Airborne Platform

Abstract Object detection and tracking is an indispensable module in airborne optoelectronic equipment, and its detection and tracking performance is directly related to the accuracy of object perception. Recently, the improved Siamese network tracking algorithm has achieved excellent results on various challenging data sets. However, most of the improved algorithms use local fixed search strategies, which cannot update the template. In addition, the template will introduce background interference, which will lead to tracking drift and eventually cause tracking failure. In order to solve these problems, this article proposes an improved fully connected Siamese tracking algorithm combined with object contour extraction and object detection, which uses the contour template of the object instead of the bounding-box template to reduce the background clutter interference. First, the contour detection network automatically obtains the closed contour information of the object and uses the flood-filling clustering algorithm to obtain the contour template. Then, the contour template and the search area are fed into the improved Siamese network to obtain the optimal tracking score value and adaptively update the contour template. If the object is fully obscured or lost, the YoLo v3 network is used to search the object in the entire field of view to achieve stable tracking throughout the process. A large number of qualitative and quantitative simulation results on benchmark test data set and the flying data set show that the improved model can not only improve the object tracking performance under complex backgrounds, but also improve the response time of airborne systems, which has high engineering application value.

Road-Aided Along-Track Baseline Estimation in a Multichannel SAR-GMTI System

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel synthetic aperture radar (SAR) system, intended for ground moving target indication (GMTI) applications. First, an adaptive spectrum filtering technique is proposed to compensate the terrain interferometric phase caused by the cross-track baseline and then the ground clutter is rejected by applying the joint-pixel displaced phase center antenna (JPDPCA). After performing the moving target detection, the target radial velocity is estimated according to the azimuth position offset by exploiting the road-aided information. Finally, the along-track baseline is derived based on the subspace projection (SP). The effectiveness of the proposed method is validated by data from the experimental airborne system.

Two-level fusion big data compression and reconstruction framework combining second-generation wavelet and lossless compression

In view of the characteristics of big data, fuzziness, and real time of data acquisition and transmission in the fuzzy information system faced by aircraft health management, to reduce the load of airborne data processing and transmission system under the condition of limited airborne computing resources and strong time constraints, the data collected by the airborne system are first compressed, and the amount of data are reduced before transmission and reconstructed after transmission. In view of the situation that the compression ratio of primary data compression is too small and the compression time is too long for large-scale fuzzy systems to meet the transmission requirements of the system, this paper combines the advantages of lossy compression method which consumes less time and lossless compression method which has higher compression ratio, and innovatively proposes a two-level data compression and reconstruction framework combining lossy compression and lossless compression. The optimization analysis is carried out. Taking a real aero-engine health sample as an example, the validity, scientificity, and robustness of the proposed framework are verified by comparing with data compression and reconstruction algorithm based on redundant sparse representation and compressed sensing.

Portable and Easily-Deployable Air-Launched GPR Scanner

In recent years, Unmanned Aerial Vehicles (UAV)-based Ground Penetrating Radar (GPR) systems have been developed due to their advantages for safe and fast detection of Improvised Explosive Devices (IEDs) and landmines. The complexity of these systems requires performing extensive measurement campaigns in order to test their performance and detection capabilities. However, UAV flights are limited by weather conditions and battery autonomy. To overcome these problems, this contribution presents a portable and easily-deployable measurement setup which can be used as a testbed for the assessment of the capabilities of the airborne system. In particular, the proposed portable measurement setup replicates fairly well the conditions faced by the airborne system, which can hardly be reproduced in indoor GPR measurement facilities. Three validation examples are presented: the first two analyze the capability of the measurement setup to conduct experiments in different scenarios (loamy and sandy soils). The third example focuses on the problem of antenna phase center displacement with frequency and its impact on GPR imaging, proposing a simple technique to correct it.

Method of identification of technical condition of equipped train equipment

Due to the fact that the equipment of modern electric trains is functionally and technologically complicated, the relevance of creating airborne systems for predictive monitoring of the technical condition of trains to identify their actual and predicted technical condition is increasing. At present, it has not been possible to build automatic on-board systems for predictive monitoring of the technical condition of trains. One of the possible solutions to this problem can be considered the creation of on-board systems, the identification of the technical condition of equipment in which is carried out using neural network technologies. The article proposes a methodology for identifying the technical condition of electric train equipment using artificial neural network technologies, which allows real-time detection of the occurrence and development of malfunctions of electric train equipment with the display of information on the display in the driver’s cab. Taking into account the specifics of the problem being solved, the choice of a multilayer architecture of a direct distribution neural network is justified. All layers of the neural network are completely interconnected, while the number of neurons of the input and output layers of the network is determined, equal to the number of controlled parameters of the technical condition of the electric train and the number of its possible technical conditions, respectively. As a function of activation of network neurons, a logistic function was selected. A heuristic approach is used to train an artificial neural network.

The Dynamics of Vortex Rossby Waves and Secondary Eyewall Development in Hurricane Matthew (2016): New Insights from Radar Measurements

AbstractThe structure of vortex Rossby waves (VRWs) and their role in the development of a secondary eyewall in Hurricane Matthew (2016) is examined from observations taken during the NOAA Sensing Hazards with Operational Unmanned Technology (SHOUT) field experiment. Radar measurements from ground-based and airborne systems, with a focus on the NASA High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) instrument on the Global Hawk aircraft, revealed the presence of ~12–15-km-wavelength spiral bands breaking from the inner-core eyewall in the downshear-right quadrant. The vorticity characteristics and calculations of the intrinsic phase speeds of the bands are shown to be consistent with sheared VRWs. A new angular momentum budget methodology is presented that allows an understanding of the secondary eyewall development process with narrow-swath radar measurements. Filtering of the governing equations enables explicit insight into the nonlinear dynamics of scale interactions and the role of the VRWs in the storm structure change. The results indicate that the large-scale (scales > 15 km) vertical flux convergence of angular momentum associated with the VRWs dominates the time tendency with smaller effects from the radial flux term. The small-scale (scales ≤ 15 km) vertical term produces weak, but nonnegligible nonlinear forcing of the large scales primarily through the Reynolds and cross-stress components. The projection of the wave kinematics onto the low-wavenumber (0 and 1) fields appears to be the more significant dynamic process. Flight-level observations show secondary peaks in tangential winds in the radial region where the VRW forcing signatures are active, connecting them with the secondary eyewall formation process.

Complex engineered system health indexes extraction using low frequency raw time-series data based on deep learning methods

Abstract Data analysis methods based on deep learning are attracting more and more attention in the field of health monitoring, fault diagnosis and failure prognostics of complex systems, such as aircraft airborne systems and engines. In this study, several health monitoring methods proposed from deep learning are demonstrated based on a real data set from an airborne system of commercial aircraft, where Health Indexes (HIs) are derived based on the raw sensor data to characterize the health state of the system in-service. Determining the optimal degradation evaluation index is the key to further failure prognostics. So, a set of metrics to characterize the suitability of different of HI deriving methods has been proposed. This metrics includes monotonicity, prognosability, and trendability. The better HI selected can effectively characterize the health state of aircraft air conditioning system, which is helpful for further failure prognostics and converting the scheduled maintenance into condition-based maintenance.

A Lightweight System With Ultralow-Power Consumption for Online Continuous Impact Monitoring of Aerospace Vehicle Structures

Impact monitoring is important to the service safety of aerospace vehicle structures. The implementation of an impact monitoring system is essential because impacts must be monitored online continuously due to their rare, random, and transient nature. Therefore, an impact monitoring system needs to serve as an airborne system of the vehicle so that the system is required to be low in power consumption and lightweight. In this article, a piezoelectric-sensor-based impact monitoring system is proposed and implemented. The system can execute the complete monitoring process by itself, including acquiring sensor data, executing the monitoring algorithm, and storing the results. This monitoring process for each impact can be completed in 3.4 ms by the system. The static power consumption of the system is only 1.44 mW (the static current consumption is 0.38 mA). Furthermore, the system has 32 piezoelectric sensor channels and the weight is only 22 g. Therefore, a large-scale impact monitoring network can be easily constructed by using multiple systems. The system is evaluated on the composite stiffened skin of an unmanned aerial vehicle, and the impact monitoring accuracy rate reaches 96%.

A Lightweight and Low-Power UAV-Borne Ground Penetrating Radar Design for Landmine Detection.

This paper presents the development of a lightweight and low-power Ground Penetrating Radar (GPR) to detect buried landmines in harsh terrain, using an Unmanned Aerial Vehicle (UAV). Despite the fact that GPR airborne systems have been already used for a while, there has yet been no focus on the UAV autonomy, which depends on the payload itself. Therefore, the contribution of this work is the introduction of a lightweight and low-power consumption GPR system, which is based on the Stepped Frequency Continuous Wave (SFCW) radar principle. The Radio Frequency (RF) transceiver represents an improved implementation of the super-heterodyne architecture, which currently offers higher sensitivity. This is achieved by combining analog and digital processing techniques. The experimental results showed that the developed system can detect both metallic and plastic buried targets. Target detection with a scanning height up to about 0.5 m shows good applicability in an unstructured, harsh environment, which is typical of mined terrain. The proposed system still needs some improvements for a fully operational system regarding different aspects of scanning speeds and soil properties such as moisture content.

Determination of wear resistance and wear mechanisms of HVAF coat-ings based on Ni-Al composition depending on the technological modes of their deposition

Presented studies are related to the spheres of aviation and machine-building. World aviation technology experience has accumulated a huge amount of statistical material on the failure airborne systems and ground equipment due to the increased level of parts wear. That is why the issue of research and improvement of anti-wear properties aeronautical parts is one the components when considering the priority directions of ensuring the reliability of operation motor vehicles and friction units. The Ni3Al coating has been deposited on the medium carbon steel using high velocity air fuel deposition. It has been established that it is very sensible for modes of coating deposition and different chemical phenomenon have been detected. The structure of the obtained coating has been thoroughly researched on the electronic microscope. The obtained coating has been tested on the friction bench modeling the fretting process that is taking place in the couple of locking devices of self-propelled passengers stairs. The coating has also corrosion protection. The friction surfaces have been investigated on the electronic microscope and the wear mechanisms have been established. The optimal modes of the HVAF coting deposition from the viewpoint of wear resistance have been detected

Realization of the Airborne System for Moving Target Detection and Tracking Based on Quadrotor

In this paper, taking quadrotor as the research object, a kind of airborne vision system is presented to detect and track the moving target. Lower cost and better performance are the remarkable characteristics of the system. Firstly, the overall scheme of the detection and tracking system is designed. Secondly, a new image detection algorithm based on HSV color model and template matching is proposed, then according to image principle, the relative distance from quadrotor to target is calculated. In order to solve the time delay caused by image processing, the target position prediction is made by adding Kalman filter. Then, according to the position information of quadrotor and target, a segmented PID controller is designed to improve the tracking effect. Finally, the algorithm is applied to the actual flight of the quadrotor to verify its feasibility.