Radar Network Research Articles

Resource saving based dwell time allocation and detection threshold optimization in an asynchronous distributed phased array radar network

The resource optimization plays an important role in an asynchronous Phased Array Radar Network (PARN) tracking multiple targets with Measurement Origin Uncertainty (MOU), i.e., considering the false alarms and missed detections. A Joint Dwell Time Allocation and Detection Threshold Optimization (JDTADTO) strategy is proposed for resource saving in this case. The Predicted Conditional Cramér-Rao Lower Bound (PC-CRLB) with Bayesian Detector and Amplitude Information (BD-AI) is derived and adopted as the tracking performance metric. The optimization model is formulated as minimizing the difference between the PC-CRLBs and the tracking precision thresholds under the constraints of upper and lower bounds of dwell time and false alarm ratio. It is shown that the objective function is nonconvex due to the Information Reduction Factor (IRF) brought by the MOU. A cyclic minimizer-based solution is proposed for problem solving. Simulation results confirm the flexibility and robustness of the JDTADTO strategy in both sufficient and insufficient resource scenarios. The results also reveal the effectiveness of the proposed strategy compared with the strategies adopting the BD without detection threshold optimization and amplitude information.

Estimation of extreme precipitation events in Estonia and Italy using dual-polarization weather radar quantitative precipitation estimations

Abstract. Evaluating extreme rainfall for a certain location is commonly considered when designing stormwater management systems. Rain gauge data are widely used to estimate rainfall intensities for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable for low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. From the late 1990s, dual-polarization weather radar started to become operational in the national surveillance radar network in Europe, providing innovative quantitative precipitation estimation (QPE) based on polarimetric variables. This study circumvents Zh shortcomings by using specific differential-phase (Kdp) data from operational dual-polarization C-band weather radars. The rain intensity estimates based on a specific differential-phase data are immune to attenuation and less affected by hail contamination. In this study, for the first time, QPEs based on polarimetric observations by operational C-band weather radars and without any rain gauge adjustments are analyzed. The purpose is to estimate return periods for 1 h rainfall total computed from polarimetric weather radar data using non-adjusted QPEs based on R(Zh,Kdp) data and to compare the results with those derived using R(Zh) and rain gauge data. Only the warm period during the year is considered here, as most of the extreme precipitation events for such a duration occur for both places studied (Italy and Estonia) at this time. Limiting the dataset to warm periods also allows us to use the radar-based rainfall quantitative precipitation estimations, which are more reliable than the snowfall ones. Data from operational dual polarimetric C-band weather radar sites are used from both Italy and Estonia. Given climatologically homogeneous regions, this study demonstrates that polarimetric weather radar observations can provide reliable QPEs compared to single-polarization estimates with respect to rain gauges and that they can provide a reliable estimation of return periods of 1 h rainfall total, even for relatively short time series.

HF Radar Wind Direction: Multiannual Analysis Using Model and HF Network

HF radar systems have the potential to measure the wind direction, in addition to surface currents and wave fields. However, studies on HF radar for wind direction determination are rare in the scientific literature. Starting with the results presented in Saviano et al. (2021), we here expand on the reliability of the multiannual wind direction data retrieved over two periods, from May 2008 to December 2010 and from January to December 2012, by a network of three SeaSonde high-frequency (HF) radars operating in the Gulf of Naples (Central Tyrrhenian Sea, Western Mediterranean Sea). This study focuses on the measurements obtained by each antenna over three range cells along a coast–offshore transect, pointing to any potential geographically dependent measurement. The scarcity of offshore wind measurements requires the use of model-generated data for comparative purposes. The data here used are obtained from the Mediterranean Wind–Wave Model, which provides indications for both wave and wind parameters, and the ERA5@2km wind dataset obtained by dynamically downscaling ERA5 reanalysis. These data are first compared with in situ data and subsequently with HF-retrieved wind direction measurements. The analysis of the overall performance of the HF radar network in the Gulf of Naples confirms that the HF radar wind data show the best agreement when the wind speed exceeds a 5 m/s threshold, ensuring a sufficiently energetic surface wave field to be measured. The results obtained in the study suggest the necessity of wind measurements in offshore areas to validate the HF radar wind measurements and to improve the extraction algorithms. The present work opens up further investigations on the applications of wind data from SeaSonde HF radars as potential monitoring platforms, both in coastal and offshore areas.

3-D Object Detection for Multiframe 4-D Automotive Millimeter-Wave Radar Point Cloud

Object detection is a crucial task in autonomous driving. Currently, object-detection methods for autonomous driving systems are primarily based on information from cameras and lidar, which may experience interference from complex lighting or poor weather. At present, the four-dimensional (4D) (x, y, z, v) millimeter-wave radar can provide a denser point cloud to achieve 3D object-detection tasks that are difficult to complete with traditional millimeter-wave radar. Existing 3D object point-cloud detection algorithms are mostly based on 3D lidar, these methods are not necessarily applicable to millimeter-wave radars, which have sparser data and more noise and include velocity information. This study proposes a 3D object-detection framework based on a multi-frame 4D millimeter-wave radar point cloud. First, the ego vehicle velocity information is estimated by the millimeter-wave radar, and the relative velocity information of the millimeter-wave radar point cloud is compensated to the absolute velocity. Second, by matching between millimeter-wave radar frames, the multi-frame millimeter-wave radar point cloud is matched to the last frame. Finally, the object is detected by the proposed multi-frame millimeter-wave radar point cloud detection network. Experiments are performed using our newly recorded TJ4DRadSet dataset in a complex traffic environment. The results showed that the proposed object-detection framework outperformed the comparison methods based on the 3D mean average precision. The experimental results and methods can be used as the baseline for other multi-frame 4D millimeter-wave radar detection algorithms.

Coalition Game of Radar Network for Multitarget Tracking via Model-Based Multiagent Reinforcement Learning

Task assignment is crucial for multitarget tracking of the radar network and is mainly solved by centralized optimization methods, which results in the issues of robustness deficiency, high computational cost, and inflexible adaptability in complex environments. To overcome these issues, task assignment of the radar network for multitarget tracking is formulated as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiagent decision-making and learning</i> problem, where each radar node in the network acts as an intelligent agent that can make the tracking decision according to its task preference and interact with other agents for the sake of the best network utility. To describe the decision references of radar nodes for various tasks, the criterion for the design of the utility function is presented, and a utility function under this criterion is devised based on the quality of service framework. Then, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coalition game with transferrable utility</i> is developed for task assignment, where the utility of the coalition is completely transferred to all coalition members. The existence of the stable coalition partition of the developed game is analyzed theoretically, and the model-based multiagent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random Fourier features reinforcement learning</i> (RFFRL) algorithm is proposed to solve the optimal solution to the game in the high dimensional state space, which is proven to be converged at a Nash-stable coalition partition. Some numerical simulation results are provided to illustrate the effectiveness of the proposed algorithm in terms of tracking performance and resource conservation.

Self-Interference Leakage Estimation ‘N’ Cancellation Element: Design of an Active Self-Interference Cancellation Coupler

Monostatic radar systems as well as radars with separated but nearby-placed transmit and receive antennas are beneficial in terms of cost-efficiency and miniaturization, which is necessary for their integration into compact radar systems or mobile devices <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> as well as for radar networks that demand reciprocal transmission channels between the nodes <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> . However, this lack of separation between the transmit (Tx) and receive (Rx) paths due to miniaturization implies only week isolation between Tx and Rx.

SuperDARN Observations of the Two Component Model of Ionospheric Convection

AbstractWe use a 20 years database of Super Dual Auroral Radar Network (SuperDARN) observations to investigate the two component model of ionospheric convection. A convection pattern is included in the database if it is derived from at least 250 radar vectors and has a distribution of electric potential consistent with Dungey‐cycle twin vortex flow (a negative potential peak in the dusk cell and a positive potential peak in the dawn cell). We extract the locations of the foci of the convection cells from the SuperDARN convection patterns, and compare their dependencies on the north‐south component of the interplanetary magnetic field, IMF BZ, and the SuperMAG auroral electrojet index, SML. We use these parameters to define intervals of expected dayside or nightside dominated reconnection. Our results show that, under conditions favorable for dominant dayside reconnection, the dawn and dusk foci are shifted toward the dayside and that, under conditions favorable for dominant nightside reconnection, the dawn and dusk foci are shifted toward the nightside.

New Signal Processing Techniques for Phased-Array Oceanographic Radars: Self-Calibration, Antenna Grouping, and Denoising

Abstract Original techniques are proposed for the improvement of surface current mapping with phased-array oceanographic high-frequency radars. The first idea, which works only in bistatic configuration, is to take advantage of a remote transmitter to perform an automatic correction of the receiving antennas based on the signal received in the direct path, an adjustment that is designated as “self-calibration.” The second idea, which applies to both mono- and bistatic systems, consists in applying a direction finding (DF) technique (instead of traditional beamforming), not only to the full antenna array but also to subarrays made of a smaller number of sequential antennas, a method that is referred to as “antenna grouping.” In doing this, the number of sources can also be varied, leading to an increased number of DF maps that can be averaged, an operation that is designated as “source stacking.” The combination of self-calibration, antenna grouping, and source stacking makes it possible to obtain high-resolution maps with increased coverage and is found robust to damaged antennas. The third improvement concerns the mitigation of noise in the antenna signal. These methods are illustrated with the multistatic high-frequency radar network in Toulon and their performances are assessed with drifters. The improved DF technique is found to significantly increase the accuracy of radar-based surface current when compared to the conventional beamforming technique.

Search task oriented path planning method of airborne radar network

Reasonable planning the trajectory of the airborne radar network can decrease its repeated surveillance region, and improve the search efficiency. To complete a wider range of reconnaissance searches within a certain time, a search task oriented path optimization method of airborne radar network is proposed. Firstly, the single-platform search range by combing the radar equation is characterized, and the multi-radar area coverage function based on the rasterization idea is calculated. A trajectory optimization model with the goal of maximizing the search coverage function is constructed, by combining with the motion constraints of each node and adopting an intelligent method. Simulation results show that the present method can obtain higher search coverage within specified time.

Multi‐Scale Ionospheric Poynting Fluxes Using Ground and Space‐Based Observations

AbstractThree events are presented where high‐resolution measurements from the Swarm satellites coincided with excellent F‐region ionospheric coverage from The Super Dual Auroral Radar Network (SuperDARN). Large‐scale ionospheric convection patterns from SuperDARN, together with field‐aligned‐current patterns from he Active Magnetosphere and Planetary Response Experiment (AMPERE), provide information on quasi‐static ionospheric dynamics traversed by Swarm. Because the Swarm observations and orbital path coincided with favorable SuperDARN/AMPERE observing conditions, it was possible to filter the Swarm electric field observations into a quasi‐static component that agreed with the SuperDARN electric field. The residual electric field from Swarm is thus indicative of small‐ and mesoscale dynamics not captured by the convection and field‐aligned current (FAC) patterns. Calculations of the Poynting flux between the different instruments show that dynamics on small‐to mesoscales can be highly variable within structures like FACs. In the events shown, small‐ and medium‐scale Poynting fluxes occasionally dominate over those from large‐scale processes.

Weighted Average Consensus-Based Distributed Target Tracking over Radar Networks

Weighted Average Consensus-Based Distributed Target Tracking over Radar Networks

A multi-state target recognition method based on multi-classifier fusion

A multi-classifier target recognition algorithm based on fuzzy pattern recognition and fuzzy comprehensive evaluation theory is proposed, where the base classifiers are deployed as fuzzy pattern recognition classifiers, and the secondary classifiers are based on fuzzy comprehensive evaluation theory. On the basis of time-frequency domain analysis of target signals, fuzzy theories such as fuzzy pattern recognition, decision-making and comprehensive evaluation are synthetically applied to carry out first-level evaluation, where the extracted feature parameters are used as the input of target base classifiers. With the evaluation results as the input of second-level evaluation, target state recognition is performed on the multi-state human targets behind the walls in the ultra-wideband radar sensor network based on fuzzy evaluation. The results show that the recognition can efficiently discern the Multi-status human being behind the wall.

Climatology of HF Propagation Characteristics at Very High Latitudes From SuperDARN Observations

AbstractConventional forecasting of high‐frequency (HF, 3–30 MHz) radio wave propagation is based on a combination of ionospheric and propagation models. However, at very high latitudes this approach is seriously undermined by the intrinsically dynamic ionospheric conditions regularly perturbed by energetic particle precipitations and strong electric fields. From this perspective, the multi‐year observations of HF propagation characteristics by Super Dual Auroral Radar Network (SuperDARN) radars across auroral and polar cap regions represent a unique opportunity for systematic validation of the conventional approach, as well as for creating an empirical propagation model directly from the radar observations. Qualitative identification and quantitative characterization of the propagation modes requires an accurate knowledge of the vertical angle of arrival (elevation angle) across the high‐latitude part of the radar network. This information has become available only in recent years, facilitated by the development of reliable data‐based calibration techniques for SuperDARN interferometry. We present the solar‐cycle/seasonal/diurnal climatology of HF propagation characteristics at very high latitudes derived from two‐frequency observations by the Rankin Inlet SuperDARN radar.

Application of a Three-Dimensional Wind Field from a Phased-Array Weather Radar Network in Severe Convection Weather

In 2019–2020, an array weather radar (AWR) network consisting of seven X-band phased-array radars (PARs), with a detection spatial resolution of 30 m and a temporal resolution of 30 s, was built in the city of Foshan in China’s Guangdong Province. The detection time deviation in the same space is within 5 s. Through variational data assimilation, the three-dimensional wind field inside the storm can be obtained. This study selected instances of hail, thunderstorms, strong winds, and short-duration heavy precipitation in 2020 to conduct a detailed analysis. The results show the following: (1) The fine detection ability enables phased-array radars to detect the complete evolution process of convective storms, including development, strengthening, and weakening, providing a useful reference for judging the future variation trends of convective storms. (2) Through evolutionary analysis of the three-dimensional wind field, the dynamic mechanisms of storm strengthening and weakening could be obtained, which could serve as a reference to predict the development of storms. The gust wind index and convection index calculated based on the three-dimensional wind field could provide advanced warning for nowcasting. When the gust wind index was greater than 263, the probability of gale-force wind (above 17.0 m/s) was determined to be high. Moreover, the warning could be provided 10–20 min in advance. A convection index greater than 35 and the presence of concentrated contour lines were found to be conducive to the strengthening and formation of a convection, and the warning could be provided 20 min in advance. These results show that the application of PAR can provide important technical support for nowcasting severe convective weather.

Photonic generation of dual-band dual-chirp waveforms with anti-dispersion transmission.

A photonic approach for generating dual-band dual-chirp waveforms with the capability of anti-dispersion transmission is proposed. In this approach, an integrated dual-drive dual-parallel Mach-Zehnder modulator (DD-DPMZM) is adopted to realize single-sideband modulation of a RF input and double-sideband modulation of baseband signal-chirped RF signals. By properly presetting the central frequencies of the RF input and the bias voltages of DD-DPMZM, dual-band dual-chirp waveforms with anti-dispersion transmission can be achieved after photoelectronic conversion. A complete theoretical analysis of the operation principle is presented. Full experimental verification of the generation and anti-dispersion transmission of dual-chirp waveforms centered at 2.5 and 7.5GHz as well as 2 and 6GHz over two dispersion compensating modules with dispersion values equivalent to 120km or 100km standard single-mode fiber is successfully carried out. The proposed system features a simple architecture, excellent reconfigurability, and immunity to dispersion-induced power fading, which are highly desired in distributed multi-band radar networks with optical-fiber-based transmission.

The effect of altitude on the uncertainty of radar-based precipitation estimates over Switzerland

ABSTRACT Despite high spatial coverage, radar-based Quantitative Precipitation Estimates (QPE) are affected by different sources of errors. Regardless of various techniques to merge these estimates with rain gauges, some errors still remain in the final product. This study focused on the relationship between these errors and altitude for a radar-only product (RZC) and a cross-validated-merged-with-gauge product (CPC-CV) in Switzerland using 16 years of hourly data based on two log-transformed metrics (bias and scatter). For each site, the bias is a measure of the mean error and the scatter is a robust estimate of the dispersion of the error around the mean. The results showed that the bias has a negative Pearson correlation coefficient with altitude, while the scatter has a positive correlation for both RZC and CPC-CV products during the entire period. Considering the effect of the old and the new networks, the errors are generally reduced using the new radar network. However, the relationship between altitude and the errors is still present. Regarding the seasonality effect, the results showed a significant underestimation at low altitudes and the weakest bias correlation in winter, while many stations showed overestimation in summer. For scatter, the correlation with altitude was highest in summer, while it was low in winter. Moreover, for low-intensity precipitation (below 1.1 mm . h − 1 ) there is a negative correlation for bias and a positive correlation for scatter with the gauge altitudes in both products. Interestingly, for high intensities (above 7 mm . h − 1 ), a positive correlation for bias and a negative correlation for scatter was observed . Also, we added the bias of eight glaciers based on the snow depth measurements in addition to the gauge data. In conclusion, the results showed that almost 30% of the variance of the errors in both products can be statistically explained using the gauge altitudes as a single explanatory variable.

Asynchronous Track-to-Track Association Based on Pseudo Nearest Neighbor Distance for Distributed Networked Radar System

In radar network systems, target tracks reported from different radars need to be associated and fused, and the track-to-track association (TTTA) effect is a key factor that directly affects the performance of the entire system. In order to solve the problem of the low accuracy of TTTA in network radar systems with asynchronous unequal rates, an asynchronous TTTA algorithm based on pseudo nearest neighbor distance is proposed. Firstly, the calculation method of pseudo nearest neighbor distance between the track point and the track data set is defined, then the correlation degree between the two track data sets is obtained by using grey theory, and then the Jonker-Volgenant algorithm is combined with the classical allocation method to judge the TTTA. The algorithm does not need time domain alignment and can effectively avoid the accumulation and propagation of estimation errors. The simulation results show that the algorithm has a high average correct association rate and is less affected by the radar sampling period ratio, startup time, and noise distribution, and the average correct association rate for different movement types of target tracks remains above 99%. Furthermore, compared with other algorithms, this algorithm maintains a stable low level of the number of false associations and the maximum false association rates, and has strong robustness and advantages.

Semantic Feature-Enhanced Graph ATtention Network for Radar Target Recognition in Heterogeneous Radar Network

Radar target recognition (RTR), as a key technique of intelligent radar systems, has been widely investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Considering that most existing methods are based on a single radar or the homogeneous radar network, we extend RTR to the heterogeneous radar network to improve the robustness of RTR, which uses the radar cross Section (RCS) signals at low SNRs by further exploiting the frequency-domain information. In this article, a Semantic Feature-Enhanced Graph ATtention Network (SFE-GAT) is proposed, which extracts semantic features from both the source and transform domains via the long short-term memory (LSTM) and GAT layers, then fuses them in the semantic space using an attention mechanism, and further distills higher-level semantic features using a GAT layer before classification. Extensive experiments are carried out to validate that the proposed SFE-GAT model can greatly improve the RTR accuracy in the low SNR region.

Joint Selection of Transceiver Nodes in Distributed MIMO Radar Network with Non-Orthogonal Waveforms

Joint Selection of Transceiver Nodes in Distributed MIMO Radar Network with Non-Orthogonal Waveforms

Joint Power Allocation and Route Planning Scheme for Multitarget Tracking in Airborne Radar Network Under Multiuncertainty

In this article, a joint power allocation and route planning (JPARP) scheme is proposed for multitarget tracking in airborne radar network (ARN) in the presence of multiuncertainty. The main mechanism of the proposed scheme is to utilize the optimization technique to collaboratively plan route and allocate power for each airborne radar, with the aim of minimizing the power consumption of the ARN while satisfying the predetermined multiple target tracking (MTT) performance, inter-radar communication performance, and inter-radar safe distance. The Bayesian Cramer–Rao lower bound (BCRLB) and the communication data rate (CDR) are derived and adopted as metrics to evaluate the MTT performance and inter-radar communication performance, respectively. To ensure the robustness of the algorithm under multiuncertainty, we formulate the JPARP scheme as a chance-constraint programming (CCP) problem. To efficiently solve this no-convex and nondeterministic CCP problem, we relax it into an unconstrainted optimization problem. Then, a hybrid intelligent algorithm based on stochastic simulation and improved genetic algorithm (GA) is proposed to obtain a near-optimal solution. Simulation results are provided to demonstrate that the JPARP scheme is more effective in saving ARN power consumption and more robust under uncertainty than the conventional algorithm.