Over-the-horizon Radar Research Articles

On the Use of SuperDARN Ground Backscatter Measurements for Ionospheric Propagation Model Validation

AbstractPrior to use in operational systems, it is essential to validate ionospheric models in a manner relevant to their intended application to ensure satisfactory performance. For Over‐the‐Horizon radars (OTHR) operating in the high‐frequency (HF) band (3–30 MHz), the problem of model validation is severe when used in Coordinate Registration (CR) and Frequency Management Systems (FMS). It is imperative that the full error characteristics of models is well understood in these applications due to the critical relationship they impose on system performance. To better understand model performance in the context of OTHR, we introduce an ionospheric model validation technique using the oblique ground backscatter measurements in soundings from the Super Dual Auroral Radar Network (SuperDARN). Analysis is performed in terms of the F‐region leading edge (LE) errors and assessment of range‐elevation distributions using calibrated interferometer data. This technique is demonstrated by validating the International Reference Ionosphere (IRI) 2016 for January and June in both 2014 and 2018. LE RMS errors of 100–400 km and 400–800 km are observed for winter and summer months, respectively. Evening errors regularly exceeding 1,000 km across all months are identified. Ionosonde driven corrections to the IRI‐2016 peak parameters provide improvements of 200–800 km to the LE, with the greatest improvements observed during the nighttime. Diagnostics of echo distributions indicate consistent underestimates in model NmF2 during the daytime hours of June 2014 due to offsets of −8° being observed in modeled elevation angles at 18:00 and 21:00 UT.

Range-Doppler image classification for interference detection in over-the-horizon radar

Sky-wave over-the-horizon radar (OTHR), as a useful tool for beyond-line-of-sight target detection, is inevitably affected by radio frequency interference (RFI) and transient interference (TSI). Conventional interference detection is usually based on radar signal processing. This paper proposes to detect the interference by classifying the range-Doppler (RD) image into three categories, i.e. RFI, TSI, and NoI (No Interference), according to the texture differences. Unlike the conventional way, RD image classification has direct reflection of the interference's influence on target detection. To design the RD image classifier, we develop signal models to construct the simulated database, use real data to construct the real database, and employ various classification theories, e.g. deep learning, traditional machine learning, feature fusion, and ensemble learning. Experimental results show that the RD image classifier is an effective and promising tool for interference detection. Strong interference can be accurately detected, while weak interference needs more study in the future.

Impact of Plasma Bubbles on OTHR Shortwave Propagation in Different Backgrounds

Plasma bubbles represent notable ionospheric irregularities primarily observed in low latitudes, characterized by plasma depletions exhibiting large spatial scales, which can make a significant impact on the propagation of OTHR (over-the-horizon radar) waves. Firstly, we constructed a three-dimensional model of plasma bubbles, which is modulated by Gaussian function distribution in the horizontal direction, and then we analyzed the impact of EPBs (Equatorial Plasma Bubbles) on the ray path of OTHR shortwaves. When radio waves propagate through EPBs with different RMS ΔN/N, there is a significant difference in the propagation path of OTHR waves. For the EPB with an RMS ΔN/N of 75%, radio waves exhibit more pronounced refraction than those with lower RMS values, the focusing effect of radio waves is more obvious, and the focusing point is relatively lower. In terms of different seasons, OTHR shortwaves propagating through EPBs exhibit different degrees of refraction. In addition, radio waves show the effect of inward focusing in different seasons: the focusing effect is the most pronounced in spring, followed by autumn, then summer, and the weakest in winter. For different solar activities, the impact of EPBs on OTHR shortwaves is more significant in the high-solar-activity year.

A sea–land clutter classification framework for over-the-horizon radar based on weighted loss semi-supervised generative adversarial network

Deep convolutional neural network has made great achievements in sea–land clutter classification for over-the-horizon radar (OTHR). The premise is that a large number of labeled training samples must be provided for a sea–land clutter classifier. In practical engineering applications, it is relatively easy to obtain label-free sea–land clutter samples. However, the labeling process is extremely cumbersome and requires expertise in the field of OTHR. To solve this problem, we propose an improved generative adversarial network, namely weighted loss semi-supervised generative adversarial network (WL-SSGAN). Specifically, we propose a joint feature matching loss by weighting the middle layer features of the discriminator of semi-supervised generative adversarial network (SSGAN). Furthermore, we propose the weighted loss of WL-SSGAN by linearly weighting the standard adversarial loss of SSGAN and the joint feature matching loss. The classification performance of WL-SSGAN is evaluated on sea–land clutter datasets. The experimental results show that WL-SSGAN can improve the performance of the fully supervised classifier with only a small number of labeled samples by utilizing a large number of unlabeled sea–land clutter samples. Further, the proposed weighted loss is superior to both the adversarial loss and the feature matching loss. Additionally, we compare WL-SSGAN with conventional semi-supervised classification methods and demonstrate that WL-SSGAN achieves the highest classification accuracy.

High‐Latitude Off‐Great Circle Propagation Associated With the Solar Terminator

AbstractLarge‐scale ionospheric gradients associated with the solar terminator can deflect high frequency (HF) radio waves to off‐great circle paths during the morning and evening, negatively impacting technologies reliant on HF radio wave propagation. For example, geolocation algorithms used by scientific and military over‐the‐horizon radars (OTHRs) generally assume on‐great circle propagation, and thus lateral deviations from the great‐circle path can lead to positioning errors. In this study, radio wave propagation is simulated via 3D numerical ray traces though an empirical, high‐latitude model ionosphere initialized for a variety of times of the day and year to explore and quantify high‐latitude off‐great circle propagation associated with the solar terminator. Analysis of these simulations show large scale east‐west ionospheric gradients due to the solar terminator can cause lateral deviations in north‐directed propagation paths exceeding 20° at sunrise and sunset depending on radio wave frequency, though the largest portion of received signal power tends to experience maximum deflections of 5°. An exploration of the dependence of propagation direction on deflection shows that propagation paths parallel to the solar terminator tend to experience the largest deflections. Since the solar terminator at high latitudes is at an angle with respect to north in the winter and summer, propagation paths oriented west or east of north can experience larger deflections than north oriented paths at sunrise and sunset during these times of year. Impacts of these diurnal deflections on the operation of OTHR and scientific radar are discussed, as well as possible strategies for mitigating them.

Transient Interference Excision and Spectrum Reconstruction with Partial Samples Using Modified Alternating Direction Method of Multipliers-Net for the Over-the-Horizon Radar.

Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise serious grating lobes using conventional methods and thus degrade target detection performance. This article proposes a modified Alternating Direction Method of Multipliers (ADMM)-Net to reconstruct the target and clutter spectrum of sparse OTHR signals so that target detection can be performed normally. Firstly, transient interferences are identified based on the sparse basis representation and then excised. Therefore, the processed signal can be seen as a sparse OTHR signal. By solving the Doppler sparsity-constrained optimization with the trained network, the complete Doppler spectrum is reconstructed effectively for target detection. Compared with traditional sparse solution methods, the presented approach can balance the efficiency and accuracy of OTHR signal spectrum reconstruction. Both simulation and real-measured OTHR data proved the proposed approach's performance.

CLIMF2: A climatological model of the ionospheric F2 layer. Part 1: Model methodology

The ionospheric F2 layer is of key importance to systems dependent on the propagation of radio waves through the ionosphere, including over-the-horizon (OTH) radar, high frequency (HF) communications, global navigation satellite systems (GNSS), and low-frequency radio astronomy. The currently accepted F2 layer model, the International Reference Ionosphere (IRI), does not provide the variance in the parameters used to describe the layer and furthermore, was developed from a limited dataset. The newly developed model, CLIMF2, can be used in conjunction with IRI to overcome these limitations, and thereby improve applications such as the climatological modelling of OTH radar performance. CLIMF2 is an empirical climatological model of the F2 layer parameters derived from a global network of vertical incidence sounder (VIS) observations, consisting of 267 sites with data from the 1950s to 2021. CLIMF2 has the added functionality of providing the variance in the foF2 and hmF2 parameters.

A sea clutter suppression algorithm for over-the-horizon radar based on dictionary learning and subspace estimation

Detecting a ship target by over-the-horizon radar (OTHR) is seriously affected by sea clutter. This paper proposes a clutter suppression algorithm based on dictionary learning and subspace estimation (DLSE). First, the K-singular value decomposition (K-SVD) is used for dictionary learning and sparse representation of the sea clutter. Then, the orthogonal matching pursuit (OMP) is used to reconstruct the sea clutter component to suppress the outliers. Finally, the clutter covariance matrix is calculated using the reconstructed clutter spectrum, and the clutter subspace of the covariance matrix is estimated to achieve clutter suppression. In this paper, the K-SVD is improved to preserve the phase information of the complex signal. The sea clutter spectrum, after sparse representation and reconstruction, is not impacted by outliers in neighboring cells. Therefore, the clutter covariance matrix has a better estimate accuracy than existing subspace decomposition algorithm. The adaptive parameter estimate method also enhances the adaptability of the DLSE. The experimental results of real data demonstrate that the DLSE is as sophisticated as existing subspace decomposition algorithms while having a higher output signal-to-clutter-plus-noise ratio (SCNR) and peak-to-side lobe ratio (PSLR), improving about 2-5 dB. Results of outlier suppression attest to the robustness of the DLSE. Under various coherent integration times (CIT) and frequencies, the DLSE has the best clutter suppression performance among the comparison algorithms.

Global Simulations of Multi‐Frequency HF Signal Absorption for Direct Observation of Middle Atmosphere Temperature and Composition

AbstractThis paper presents the first numerical study on a new concept for the direct measurement of D‐region absorption in the high‐frequency (HF) band. Numerical simulations based on the Appleton‐Hartree and Garrett equations of refractive index are presented. Electron temperature as a result of HF radio pumping of the ionosphere is included in the calculations using proper numerical formulation. Both O‐ and X‐mode radio wave polarizations are taken into consideration. A global map of HF absorption in the northern hemisphere is calculated. Detailed calculations of HF radio wave absorption as it propagates through the lower atmosphere are presented. The effect of several parameters on the amount of absorption is calculated. The best frequencies to be used for the purpose of this study are discussed. A machine learning model is developed and the capability of the model in estimation of D and E‐region constituents includes N2, O, O2, as well as T and Ne is examined. Such a technique can also lead to global mapping of HF absorption and improve OTHR (over‐the‐horizon‐radar) performance.

Evolution of Over-the-Horizon Radar in Australia From Humble Origins to Operational Capabilities

The successful realization of high frequency (HF) over-the-horizon radar (OTHR) is one of the more significant radar developments since World War II. The first project to develop OTHR in Australia commenced in 1974 and was called Jindalee. The operational outcome of that project carries this iconic name in the Jindalee Operational Radar Network (JORN). JORN consists of three operational OTHR systems that deliver wide-area surveillance over Australia's northern approaches as a key element of an integrated defense surveillance system. JORN is one of Australia's quietest achievers and is widely recognized in a global sense as the frontier of OTHR capability. The now Defence Science and Technology Group pioneered the development of OTHR in Australia from humble origins in the 1960s and continues to lead innovation in HF radar systems to the present day. This article reviews the evolution of OTHR in Australia including the most recent phases of JORN. Through the lens of JORN as an exemplar Australian program, it reflects on pivotal factors contributing to the successful transition of use-inspired scientific innovation to defense operational capability.

Spherical Earth Based Measurement Modeling for Practical OTHR Target Tracking

We revisit the problem of modeling of the over-the-horizon radar (OTHR) target tracking. The accuracy of target kinematic and measurement models are critical factors affecting tracking performance. In most of the existing literature, a flat Earth OTHR measurement model is used. This has two obvious drawbacks: (1) a state vector with components of ground range, range-rate, bearing, and bearing-rate cannot be used directly in common kinematic models such as the nearly constant velocity (NCV), nearly constant acceleration (NCA), nearly constant turn (NCT), and a combination of these. (2) The curvature of Earth is ignored, which affects the measurement accuracy of the OTHR. We develop a spherical Earth based measurement model. First, the target kinematic state is modeled in a two-dimensional tracker coordinate frame (T frame), which is suitable for the common kinematic models. Second, the measurement modeling is completed using the spherical Earth model, which fully considers the curvature of Earth among the receiver, transmitter, and target. Furthermore, the inverse mapping for track initiation is derived. Finally, the effectiveness of the developed model is demonstrated by a non-maneuvering target moving with the NCV motion and a maneuvering target moving with a sequence of NCV, NCT, and NCV motions. The interacting multiple model (IMM) algorithm is used for state estimation of the maneuvering target. The unscented Kalman filter (UKF), cubature Kalman filter (CKF), and quadrature Kalman filter (QKF) are compared for state estimation.

Annual Characteristic Analysis of Ionosphere Reflection From Middle-Latitude HF Over-the-Horizon Radar in the Northern Hemisphere

The dynamically changing ionospheric conditions are the biggest impediment to realizing ideal performance in long-range for over-the-horizon radar (OTHR). Most related studies focus on developing the signal processing techniques and antenna arrays design. However, ionosphere clutter derives from the interaction between radar signal and the ionosphere, it exhibits different behaviors in three dimensions. OTHR should have the ability to sense when and where (in range) a certain type of ionosphere clutter appears before proposing the appropriate suppression scheme. Therefore, this report aims at identifying the various types of ionosphere clutter in Earth’s middle-latitude regions and characterizing them in terms of temporal, Doppler, azimuth, range and overall amplitude distribution domain, thereby establishing a multi-dimensional information profile. An experimental OTHR has been developed to monitor the surrounding oceans in middle-latitude area, we collected a sufficient real radar return data for two years running and then selected data specifically designed to observe ionosphere clutter. Then, we identify the various types of ionosphere clutter and catalogue their multi-dimensional characteristics. A knowledge of where a certain type of ionosphere clutter appears and pertinent information are essential for developing the appropriate clutter mitigation scheme in real time.

Data Augmentation and Classification of Sea–Land Clutter for Over-the-Horizon Radar Using AC-VAEGAN

In the sea-land clutter classification of sky-wave over-the-horizon-radar (OTHR), the imbalanced and scarce data leads to a poor performance of the deep learning-based classification model. To solve this problem, this paper proposes an improved auxiliary classifier generative adversarial network (AC-GAN) architecture, namely auxiliary classifier variational autoencoder generative adversarial network (AC-VAEGAN). AC-VAEGAN can synthesize higher quality sea-land clutter samples than AC-GAN and serve as an effective tool for data augmentation. Specifically, a one-dimensional convolutional AC-VAEGAN architecture is designed to synthesize sea-land clutter samples. Additionally, an evaluation method combining both traditional evaluation of GAN domain and statistical evaluation of signal domain is proposed to evaluate the quality of synthetic samples. Using a dataset of OTHR sea-land clutter, both the quality of the synthetic samples and the performance of data augmentation of AC-VAEGAN are verified. Further, the effect of AC-VAEGAN as a data augmentation method on the classification performance of imbalanced and scarce sea-land clutter samples is validated. The experiment results show that the quality of samples synthesized by AC-VAEGAN is better than those synthesized by the state-of-the-art GAN-based methods, and the data augmentation method with AC-VAEGAN is able to improve the classification performance in the case of imbalanced and scarce sea-land clutter samples.

Societal Applications of HF Skywave Radar

After exploratory research in the 1950s, HF skywave ‘over-the-horizon’ radars (OTHR) were developed as operating systems in the 1960s for defence missions, notably the long-range detection of ballistic missiles, aircraft, and ships. The potential for a variety of non-defence applications soon became apparent, but the size, cost, siting requirements, and tasking priority hindered the implementation of these societal roles. A sister technology—HF surface wave radar (HFSWR)—evolved during the same period but, in this more compact form, the non-defence applications dominated, with hundreds of such radars presently deployed around the world, used primarily for ocean current mapping and wave measurements. In this paper, we examine the ocean monitoring capabilities of the latest generation of HF skywave radars, some shared with HFSWR, some unique to the skywave modality, and explore some new possibilities, along with selected technical details for their implementation. We apply state-of-the-art modelling and experimental data to illustrate the kinds of information that can be generated and exploited for civil, commercial, and scientific purposes. The examples treated confirm the relevance and value of this information to such diverse activities as shipping, fishing, offshore resource extraction, agriculture, communications, weather forecasting, and climate change studies.

Main lobe interference suppression method of Skywave OTHR based on fast-time STAP

For the sky-wave over-the-horizon radar (OTHR), how to effectively suppress the radio frequency interference (RFI)from the main-lobe direction is a problem. To solve this problem, this paper proposes a new space-fast time adaptive processing (fast-time STAP) which merges the matched filter coefficients used for pulse compression and the time steering vector of fast-time STAP. To compare with conventional methods, the method improves the match between the space-time steering vector of fast-time STAP and the real target signal, while the RFI and the noise from RFI are effectively suppressed by the interference-plus-noise covariance matrix which is composed of raw data. At the same time, the signal-to-noise ratio is improved. Compared with the traditional method, this method can be better applied to practical engineering. The results of computer simulation and experiments with real radar data verify the effectiveness of the proposed method.

Multi-channel track-before-detect approach for airborne target with OTH radar

It is a common requirement for over-the-horizon (OTH) radar to detect the sea and airborne targets in the same direction simultaneously. The long coherent integration time (CIT) is needed to detect the sea target and the weak moving target such as aircraft. However, the range walk effect (RWE) will occur within the long integration time, leading to a serious degradation of the airborne target detection and tracking. To settle this problem, a new approach based on the multi-channel track-before-detect (MC-TBD) is proposed in this paper. More specifically, to improve the detection performance of the airborne target with unknown speed, multiple processing channels corresponding to different speed ranges are set under the MC-TBD framework. The received data are processed in each channel in parallel. In each channel, the constraints of the speed range are utilized to determine the number of pulses in the CIT of the airborne target. Then, coherent and noncoherent integration processing are performed sequentially based on the moving target detection (MTD) and track-before-detect (TBD). Finally, the detection results and trajectories of all channels are fused to achieve the final detection and tracking of the airborne target. Simulation results show the superiority of the proposed MC-TBD approach over the existing methods in terms of the performance of detection and tracking for airborne target with OTH radar under the background of simultaneous detection of airborne and sea targets in the same direction.

Seasonal Variation in Land and Sea Surface Backscatter Coefficients at High Frequencies

Over the horizon radars (OTHR) rely on the propagation of high frequency (HF) radio waves via the ionosphere to successfully achieve their designated missions. Backscatter sounders (BSS) are environmental over-the-horizon radars which may be used to assess the ionospheric propagation conditions. However, high power observed by a BSS may be due to either good ionospheric propagation, a high surface backscatter coefficient, or a combination of both. Hence, an understanding of the surface backscatter coefficients and their temporal variation is essential to fully understand the ionospheric propagation conditions. A database of surface backscatter coefficients over a decade was created using backscatter ionogram data from four backscatter sounders in Australia. The temporal variations in the backscatter coefficients were investigated and it was found that the land backscatter coefficients were relatively constant over time, while the sea backscatter coefficients showed significant seasonal variation.

Joint Target and Ionosphere Parameter Estimation in Over-the-Horizon Radar

Target localization, especially the estimation of target altitude, is a challenging task in over-the-horizon radar (OTHR) because of the narrow signal bandwidth as well as the complexity and uncertainty involved in the ionosphere conditions. This task becomes further complicated when the height of the ionosphere layer varies over time. Therefore, it is important to jointly estimate the instantaneous height of the ionosphere and the target altitude as well as other motion parameters. In this article, we achieve these objectives by analyzing the Doppler frequencies of the target local-multipath signals and the clutter. We reveal that the change of the ionosphere height can either enhance or deteriorate the performance of target parameter estimation depending on its direction of motion relative to the target’s motion profile. In addition, it is found that the received target and clutter Doppler signatures follow the chirp signal profile at the OTHR receiver. Based on these observations, we develop a general framework that achieves joint target and ionosphere parameter estimation and accounts for the velocity and accelerations of both target and ionosphere layer. Unlike existing time-frequency-based strategies for target localization and tracking in OTHR, where the Doppler signatures only directly determine target vertical velocity and the target altitude is estimated indirectly, the proposed model enables direct estimation of target altitude and ionosphere parameters. The parameter estimation problem in the proposed strategy is analytically derived and the effectiveness is verified using extensive simulation results.

Reference source‐aided coordinate registration method for over‐the‐horizon radar using ensemble learning

Since solving the path of radio wave propagation by an analytic method in the process of coordinate registration (CR) for skywave over-the-horizon radar (OTHR) is difficult, a reference source-aided CR method that uses ensemble learning is proposed. First, the OTHR wave propagation principle is briefly described, and the ionospheric channel characteristics implied by reference sources and target information are explained, which provides a theoretical basis for modelling. Then, multiple machine learning models, such as random forest, support vector machine and elastic network, are used to build a mapping network between known information and the actual ground distance of the targets. Moreover, the prediction ability and correlation of each machine learning model are evaluated. Finally, with stacking ensemble learning, machine learning models with better performance to solve CR problems are effectively combined as base learners. The models develop the advantages of each base learner to achieve a perception of the ionospheric environment and accurate OTHR CR. The simulation results show that the proposed method can mine the ionospheric channel propagation characteristics and effectively reduce the CR error of the targets of interest. When the information provided by 4 reference sources 200 km away from the target is used, the average error of target positioning is 13 km.

High Frequency Land Backscatter Coefficients Over Northern Australia and the Effects of Various Surface Properties

Over-the-horizon radars (OTHRs) utilize the refractive properties of the ionosphere to illuminate targets beyond the Earth’s horizon. Consequently, the performance of this type of radar is highly dependent on the ionosphere. Reliable models of the radar ground backscatter are required to accurately assess the ionospheric propagation conditions and, thus, the expected performance of OTHRs. The ground backscatter coefficient characterizes the amount of radiation scattered back from a surface toward a receiver per unit area. While the backscatter coefficient of the sea is well understood and may be calculated from theory if the sea state is known, the backscatter coefficient of land at high frequencies is not well understood. To calculate the land backscatter coefficients over Northern Australia, a methodology that compares observed backscatter ionograms to those synthesized using high-frequency (HF) radio wave ray-tracing techniques through model ionospheres was developed. Maps of the backscatter coefficients across Northern Australia were produced. The effects of surface properties, including topography, soil moisture, and vegetation cover on the backscatter coefficients, were investigated. A weak positive correlation between the backscatter coefficient and the soil moisture and surface roughness was observed; however, it was found that the vegetation had the largest effect on the backscatter coefficient.