Side-looking Airborne Radar Research Articles

IMPROVE THE SAFETY OF AIR TRANSPORT, ESPECIALLY IN MILITARIZED TERRAIN, BY USE OF SIDE LOOKING AIRBORNE RADAR AND SPACE TIME ADAPTIVE PROCESSING

The paper explores the potential to enhance aviation safety, particularly in militarized regions, by outfitting aircraft with Side Looking Airborne Radar (SLAR) and employing space-time adaptive processing (STAP) algorithms. The research objective revolves around implementing a model of side-looking airborne radar and the corresponding STAP algorithms. This technology enables the detection of slow-moving targets amidst strong interference, encompassing both passive (clutter) and active (jammer) elements. Slow-moving targets relative to the aircraft's speed include tanks, combat vehicles, command vehicles, artillery, and logistical assets of enemy forces. The theoretical framework of space-time adaptive processing is presented, elucidating the sequential steps of the classical Sample Matrix Inversion Space-Time Adaptive Processing (SMI STAP) algorithm. The paper underscores the significance of characteristic parameters delineating a linear STAP processor. The proposed solution facilitates the detection of enemy combat measures and enhances aviation safety. It outlines a radar model installed beneath the aircraft's fuselage and elucidates algorithms for space-time adaptive processing of radar signals. The simulations conducted within the article were executed using the MATLAB environment. The simulation results indeed suggest that the proposed solution holds promise for deployment in equipping aircraft of one's own military and those engaged in operations within conflict zones. This paper stands as one of the few contributions in the literature addressing the augmentation of aircraft safety through radar and space-time adaptive processing.

Improved Threat Detection in Walk-Through Security Scanning Using an Optimized Polarimetric Target Decomposition Method

Reliable and convenient walk-through security scanning, which doesn't separate people or impede their movement, is an extremely challenging task. In this paper, a novel approach for a security check with an overhead observation and a polarimetric target decomposition is presented. The viewing angle of the scanner equals a side-looking airborne radar. However, it will be shown that the established polarimetric target decomposition methods of remote-sensing are not well suited for close-range radar imaging and need to be adapted due to the differences in the geometry of the imaging scenario. The usage of a multiple input and multiple output (MIMO) array and the shorter distance between array and target need a changed decomposition technique in order to distinguish between persons with or without worn threat objects. The differences between radar remote-sensing and close-range imaging scenarios are investigated. An optimized version of the Sato four-component polarimetric scattering decomposition is derived. The proposed close-range adjustment is applied to the model and investigated experimentally with a 4-to-12 GHz fully polarimetric MIMO imaging system. Polarimetric decomposition is carried out on defined test structures with known scattering mechanisms, as well as on mannequins and persons with different threat objects like guns or explosives. In test campaigns, promising results were achieved for a correct target decomposition in radar-based walk-through security scanning.

Beam-Space Reduced-Dimension 3D-STAP for Nonside-Looking Airborne Radar

The space–time adaptive processing (STAP) technique has achieved good clutter suppression performance for a side-looking airborne radar; however, it suffers from severe performance degradation in nonside-looking airborne radar. This is because the clutter distribution varies considerably with range. The 3D STAP can provide better performance compared with the traditional STAP methods in such a nonstationary clutter environment, but it requires high computational complexity and sample support. In this letter, the characteristic of azimuth–elevation–Doppler 3-D beam pattern for the planar array is explored, and a novel reduced-dimension scheme combined with this characteristic is proposed. We further developed three basic reduced-dimension structures according to this scheme. Furthermore, we also prove that the first and third structures are just the direct extension of the traditional 2-D generalized multibeam and joint-domain localized methods. The second one is an original structure that performs local reduced-dimension processing with three orthogonal planar windows. Numerical results verify that the proposed structures have significant advantages in reducing computational load and training sample numbers compared with existing 3D STAP methods. In particular, the second structure shows the best comprehensive performance in the bad samples environment.

Determination of ice water content (IWC) in tropical convective clouds from X-band dual-polarization airborne radar

Abstract. This paper presents a methodology for ice water content (IWC) retrieval from a dual-polarization side-looking X-band airborne radar. Measured IWC from aircraft in situ probes is weighted by a function of the radar differential reflectivity (Zdr) to reduce the effects of ice crystal shape and orientation on the variation in IWC – specific differential phase (Kdp) joint distribution. A theoretical study indicates that the proposed method, which does not require a knowledge of the particle size distribution (PSD) and number density of ice crystals, is suitable for high-ice-water-content (HIWC) regions in tropical convective clouds. Using datasets collected during the High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) international field campaign in Cayenne, French Guiana (2015), it is shown that the proposed method improves the estimation bias by 35 % and increases the correlation by 4 % on average, compared to the method using specific differential phase (Kdp) alone.

Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders

We present a method to detect maritime oil spills from Side-Looking Airborne Radar (SLAR) sensors mounted on aircraft in order to enable a quick response of emergency services when an oil spill occurs. The proposed approach introduces a new type of neural architecture named Convolutional Long Short Term Memory Selectional AutoEncoders (CMSAE) which allows the simultaneous segmentation of multiple classes such as coast, oil spill and ships. Unlike previous works using full SLAR images, in this work only a few scanlines from the beam-scanning of radar are needed to perform the detection. The main objective is to develop a method that performs accurate segmentation using only the current and previous sensor information, in order to return a real-time response during the flight. The proposed architecture uses a series of CMSAE networks to process in parallel each of the objectives defined as different classes. The output of these networks are given to a machine learning classifier to perform the final detection. Results show that the proposed approach can reliably detect oil spills and other maritime objects in SLAR sequences, outperforming the accuracy of previous state-of-the-art methods and with a response time of only 0.76 s.

The maiden flight of Hinotori-C: The first C band full polarimetric circularly polarized synthetic aperture radar in the world

Synthetic Aperture Radar (SAR) is a very powerful tool in microwave remote sensing due to its capability of all-weather and day-to-night time operation [1]. Carl Wiley invented SAR in 1951 to overcome the poor azimuth resolution in conventional Side-Looking Airborne Radar (SLAR), followed by a patent filed in 1954 [2]. Unlike SLAR, using the forward motion of the platform and the principle of Doppler beam sharpening, the azimuth resolution of a SAR is equal to half the antenna length and is independent of the range distance [3]. Since the invention of SAR, steered by the breakthrough in science and technology, many advanced SAR techniques have been proposed and realized. These techniques include, but are not limited to, spotlight SAR for a finer image resolution [4], scan SAR for a wider swath coverage [5], and the remarkable polarimetric [6] and interferometric [7] SAR techniques using multichannel SAR system [8], [9], [10] for advanced remote sensing applications. Over the years, SAR has been widely used in different types of application, particularly in Earth observation such as disaster damage assessment [11], land deformation observation [12], oceanography [13], terrain classification [14], target detection [15], and so on. The diversified applications of SAR have encouraged the rapid development of airborne and spaceborne SAR sensors.

Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images

This paper presents a system for the detection of ships and oil spills using side-looking airborne radar (SLAR) images. The proposed method employs a two-stage architecture composed of three pairs of convolutional neural networks (CNNs). Each pair of networks is trained to recognize a single class (ship, oil spill, and coast) by following two steps: a first network performs a coarse detection, and then, a second specialized CNN obtains the precise localization of the pixels belonging to each class. After classification, a postprocessing stage is performed by applying a morphological opening filter in order to eliminate small look-alikes, and removing those oil spills and ships that are surrounded by a minimum amount of coast. Data augmentation is performed to increase the number of samples, owing to the difficulty involved in obtaining a sufficient number of correctly labeled SLAR images. The proposed method is evaluated and compared to a single multiclass CNN architecture and to previous state-of-the-art methods using accuracy, precision, recall, F-measure, and intersection over union. The results show that the proposed method is efficient and competitive, and outperforms the approaches previously used for this task.

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

Traditional space-time adaptive processing (STAP) usually needs many independent and identically distributed (i.i.d) training datasets for estimating clutter covariance matrix (CCM). But this requirement is hardly satisfied in the heterogeneous clutter environments, which lead to an inaccurate estimation of CCM and accordingly degrade the performance of STAP significantly. To improve the performance of STAP in heterogeneous environments, a novel deterministic-aided (DA) single dataset STAP method based on sparse recovery technique (SR) is proposed in this paper. This presented algorithm exploits the property that the clutter components of side-looking airborne or spaceborne radar are distributed along the clutter ridge to estimate the CCM of the cell under test (CUT) without any secondary training data. The new method only uses a single CUT data to acquire a high-resolution angle-Doppler power spectrum using sparse recovery (SR) approach and then employs a new adaptive deterministic-aided generalized inner product (GIP) algorithm to recognize and select the clutter components in the CUT angle-Doppler power spectrum automatically. Subsequently, the CCM, which is used to construct the weights of STAP filter, can be effectively estimated by the selected clutter components to fulfill the final STAP filter processing. Simulation results verify the effectiveness of the proposed detection method.

Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders.

In this work, we use deep neural autoencoders to segment oil spills from Side-Looking Airborne Radar (SLAR) imagery. Synthetic Aperture Radar (SAR) has been much exploited for ocean surface monitoring, especially for oil pollution detection, but few approaches in the literature use SLAR. Our sensor consists of two SAR antennas mounted on an aircraft, enabling a quicker response than satellite sensors for emergency services when an oil spill occurs. Experiments on TERMA radar were carried out to detect oil spills on Spanish coasts using deep selectional autoencoders and RED-nets (very deep Residual Encoder-Decoder Networks). Different configurations of these networks were evaluated and the best topology significantly outperformed previous approaches, correctly detecting 100% of the spills and obtaining an score of 93.01% at the pixel level. The proposed autoencoders perform accurately in SLAR imagery that has artifacts and noise caused by the aircraft maneuvers, in different weather conditions and with the presence of look-alikes due to natural phenomena such as shoals of fish and seaweed.

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation.

This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images.

Sparsity-Based Non-Stationary Clutter Suppression Technique for Airborne Radar

Conventional space–time adaptive processing (STAP) technique can achieve perfect performance when applied to side-looking airborne radar (SLAR), where the clutter is relatively stationary, whereas it suffers significant degradation for non-SLAR due to severe range dependence, especially when range ambiguity is present. In theory, the range-dependent clutter is mainly located at the near range and can be eliminated via elevation non-adaptive or adaptive beamforming. However, the pure near-range clutter portion utilized for calculating elevation adaptive weights cannot be obtained due to range ambiguity in practice. In this paper, a novel method to adaptive extract the near-range clutter via sparsity-based technique is presented and the non-stationary clutter potion can be effectively eliminated by elevation adaptive beamforming. With this technique, the residual clutter becomes stationary, and thus, the corresponding STAP performance will be significantly improved.

Robust STAP algorithm based on knowledge‐aided SR for airborne radar

Space–time adaptive processing (STAP) for airborne radar employs training snapshots to estimate a clutter covariance matrix (CCM). However, the resulting estimated covariance matrix can be corrupted by a target-like signal (outlier), which leads to a further target self-nulling phenomenon. When these outliers are dense, STAP performance degradation is most severe. To cope with this problem, a novel robust STAP algorithm based on knowledge-aided sparse recovery (SR) is proposed, which can eliminate the influence of dense outliers on target detection. This algorithm exploits the property that the clutter components of side-looking airborne radar are distributed along the clutter ridge, which is used to distinguish clutter components and outliers. Each snapshot is decomposed by the SR method, and then the similar degree function is defined to recognise and select clutter components via a threshold. Subsequently, the CCM is estimated by the select clutter components. Therefore, this algorithm can select appropriate coefficients and space–time steering vectors to assess clutter accurately. Monte Carlo experiments affirm that the proposed algorithm has advantages in robustness and target detection over other conventional STAP algorithms [e.g. samples matrix inverse-STAP, prolate spheroidal wave function-generalised inner product, and SR-STAP algorithms] in heterogeneous clutter environments.

Fusion of active and passive remote sensors for optimized operational airborne oil spill monitoring

ABSTRACT Airborne remote sensing has long proven to be an indispensable mean for monitoring oil spills and supporting response activities. At this stage, state-of-the-art remote sensing equipment onboard the aircraft allows (1) to conduct spatio-temporally flexible surveys with regard to marine pollution and vessel traffic, (2) quick assessments of spill sites, and (3) deterrence of potential polluters. After three decades of sensor development and optimization the oil spill community can benefit today from a number of well-established remote sensors like side-looking airborne radar, infrared/ultraviolet line scanners, visible line scanners, microwave radiometers, and scanning/nadir-pointing laser fluorosensors. From the mid 1990s onwards, several approaches to handling of remotely sensed oil spill data have been studied. However, potential benefits of sensor fusion have been almost disregarded, although sensor fusion was known as a potential mean to increase the quality of a multi-sensor application comp...

Monitoring wetlands deterioration in the Cameroon coastal lowlands: implications for management

The problems of coastal wetlands hazards and management are increasingly apparent in the face of rapid population growth in coastal zones worldwide. This has significantly heightened scientific interest in spatio-temporal analysis of the Global Environmental Change (GEC) effects in the coastal zones, particularly of the sandy, swampy depositional coastal environment characteristic of the humid tropical coasts. Evolved with massive sedimentation of the Holocene period which saw the drowning of the mouths of coastal rivers along the humid tropical coastlines, the Cameroon costal lowlands of interest in this paper describes extensive wetlands from the Limbe area to the west and the Douala area to the east, which today is characterized by a lagoon system. This locality is easily the most rapidly urbanized area in Cameroon witnessing extensive spatial expansion in the Limbe, Tiko and Douala areas, which has provoked certain observable environmental problems with the conversion of the wetlands, The area now suffer in form of poor land reclamation practices, highway construction, impaired water quality, dismal domestic sewage disposal, heavy effluent discharge, subsidence etc. This paper utilizes the opportunity offered by remote sensing tools to monitor the wetlands over space and time to cover regional and micro scales. SLAR (Side Looking Airborne Radar) imagery permits quick and accurate mapping of coastline detail, while coverage of changing patterns of features over space and time is enhanced for management implications.

Short-Range Clutter Suppression for Airborne Radar by Utilizing Prefiltering in Elevation

Space-time adaptive processing (STAP) techniques have achieved good performance when applied to side-looking airborne radar (SLAR) where the ground clutter is relatively stationary, but due to severe range dependence, the performance is not so good in non-SLAR particularly when the pulse repetition frequency is high enough to induce range ambiguity. Because the short-range clutter Doppler for non-SLAR varies rapidly with range while the long-range clutter Doppler varies gently, at the presence of range ambiguity, the clutter in different ambiguous ranges does not coincide. The clutter range dependence mitigation for the case of range ambiguity is the major problem that this letter intends to solve. A subarray synthesis algorithm with prefiltering in elevation is presented. By this technique, the ambiguous short-range clutter is eliminated before STAP; therefore, the clutter range dependence is alleviated, and then, the STAP performance will be improved greatly.

A predictive penetrative fracture mapping method from regional potential field and geologic datasets, southwest Colorado Plateau, U.S.A.

Abstract Some aquifers of the southwest Colorado Plateau, U.S.A., are deeply buried and overlain by several impermeable units, and thus recharge to the aquifer is probably mainly by seepage down penetrative fracture systems. This purpose of this study was to develop a method to map the location of candidate deep penetrative fractures over a 120,000 km2 area using gravity and aeromagnetic anomaly data together with surficial fracture data. The resulting database constitutes a spatially registered estimate of recharge location. Candidate deep fractures were obtained by spatial correlation of horizontal gradient and analytic signal maxima of gravity and magnetic anomalies vertically with major surficial lineaments obtained from geologic, topographic, side-looking airborne radar, and satellite imagery. The maps define a sub-set of possible penetrative fractures because of limitations of data coverage and the analysis technique. The data and techniques employed do not yield any indication as to whether fractures are open or closed. Correlations were carried out using image processing software in such a way that every pixel on the resulting grids was coded to uniquely identify which datasets correlated. The technique correctly identified known deep fracture systems and many new ones. Maps of the correlations also define in detail the tectonic fabrics of the southwestern Colorado Plateau.

Correlation of lineaments to ground water inflows in a bedrock tunnel.

Lineaments derived from three image types (1:80,000 black and white, 1:58,000 color infrared, and 1:250,000 side-looking airborne radar) were compared to water-bearing features within a 9.6 km section of tunnel being constructed through foliated crystalline metamorphic bedrock in a glaciated region of eastern Massachusetts. Lineaments drawn by three observers during two independent trials (N = 9137) were reduced to three sets (one per image type) of coincident lineaments (N = 794). Thirty-five coincident lineaments crossed the tunnel. Nineteen discrete flow zones, each producing > or = 19 L/min, were identified in the tunnel and used to quantify the reliability of lineament analysis as a method of predicting water-bearing features in glaciated metamorphic Thirteen (68%) of the flow zones correlate with coincident lineaments, six zones correlate with more than one image type, and one zone correlates with all three image types. Overall, without additional corroborating evidence, it is difficult to interpret in advance which lineaments will result in a successful correlation with water-producing zones in the subsurface and which ones will not. Most of the observed flow (80%) correlates with northwest-trending coincident lineaments; however, the majority of the flow (67%) associated with these lineaments is produced from structures that strike to the north or northeast. In addition, only 15 of the 35 coincident lineaments correlate with the flow zones, indicating that 20 lineaments are not associated with any appreciable flow. Six flow zones are undetected by the lineament analysis.

Aerial-photointerpretation of landslides along the Ohio and Mississippi rivers

Abstract A landslide inventory was conducted along the Ohio and Mississippi rivers in the New Madrid Seismic Zone of southern Illinois, between the towns of Olmsted and Chester, Illinois. Aerial photography and field reconnaissance identified 221 landslides of three types: rock/debris falls, block slides, and undifferentiated rotational/translational slides. Most of the landslides are small- to medium-size, ancient rotational/translational features partially obscured by vegetation and modified by weathering. Five imagery sources were interpreted for land-slides: 1:250,000-scale side-looking airborne radar (SLAR); 1:40,000-scale, 1:20,000-scale, 1:6,000-scale, black and white aerial photography; and low altitude, oblique 35-mm color photography. Landslides were identified with three levels of confidence on the basis of distinguishing characteristics and ambiguous indicators. SLAR imagery permitted identification of a 520 hectare mega-landslide which would not have been identified on medium-scale aerial photography. The leaf-off, 35-mm color, oblique photography provided the best imagery for confident interpretation of detailed features needed for smaller landslides.

STAP with medium PRF mode for non-side-looking airborne radar

Space-time adaptive processing (STAP) has been widely discussed for airborne radar systems to improve the system performance of detecting targets. This is especially true for airborne early warning (AEW) radar, which should find long-range and small radar cross section (RCS) targets such as the stealth aircraft and missiles. However, in existing airborne radar literature, STAP is mainly considered for clutter and jamming rejection in side-looking airborne radar (SLAR) applications. There have been fewer discussions on airborne radar with non-side-ways looking array radar (non-SLAR). The STAP of non-SLAR such as forward looking array radar is also very important and can not be avoided for airborne radar to detect targets in all directions. The STAP of the non-SLAR is studied here. A scheme has been proposed, which is processed by the way of STAP combined with multiple staggered medium pulse repetition frequencies (PRFs). We further study the selection of PRFs in order to make the scheme more available for non-SLAR radar. We analyze two typical non-SLAR cases, i.e., inclined-sideways looking array and forward looking array. We examine this scheme by comparing the performances of three processing systems under the criteria of range-velocity blind zone minimization. Computer simulation results show the multiple-PRFs STAP scheme is feasible for non-SLAR and can be applied to phased-array AEW radar systems.

Ocean Oil Slick Detection Experiments with a Side-Looking Airborne Radar and a Satelliteborne Synthetic Aperture Radar

Ocean Oil Slick Detection Experiments with a Side-Looking Airborne Radar and a Satelliteborne Synthetic Aperture Radar