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        <title>Frontiers in Signal Processing | Radar Signal Processing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/signal-processing/sections/radar-signal-processing</link>
        <description>RSS Feed for Radar Signal Processing section in the Frontiers in Signal Processing journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-03T21:35:34.09+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1792985</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1792985</link>
        <title><![CDATA[Editorial: MmWave technologies as opportunistic ISAC for environmental monitoring]]></title>
        <pubdate>2026-02-13T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Congzheng Han</author><author>Jonatan Ostrometzky</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1688944</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1688944</link>
        <title><![CDATA[Improving aerial target detection for 3D radar based on a two-stage CFAR method with adaptive clutter distribution estimation]]></title>
        <pubdate>2025-12-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tran Vu Hop</author><author>Tran Cao Quyen</author><author>Nguyen Van Loi</author>
        <description><![CDATA[This study deals with the problem of enhancing aerial target detection for 3D radar. A novel approach which incorporates both signal and data processing is introduced. In order to increase the target’s SNR (signal-to-noise ratio), two consecutive transmit beams are used; for each, three beams are received simultaneously. All received beams are then processed. A two-stage constant false alarm rate (CFAR) algorithm is proposed for improving target detection. At the first-stage CFAR, the global CA-CFAR is applied to identify all possible target candidates (plots). Then, unsupervised machine learning is used to separate interference regions. For each interference region, the truncated probability density function of interference is estimated, and then a local CFAR (second-stage CFAR) is applied to reduce false plots while retaining target plots. The proposed approach is an extension of that given in recent publications. Tests on a 3D surveillance radar show the effectiveness of the proposed approach on aerial target detection in comparison with previous methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1667789</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1667789</link>
        <title><![CDATA[4DRadarRBD: 4D mmWave radar-based road boundary detection in autonomous driving]]></title>
        <pubdate>2025-11-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuyan Wu</author><author>Hae Young Noh</author>
        <description><![CDATA[IntroductionDetecting road boundaries, the static physical edges of the available driving area, is important for safe navigation and effective path planning in autonomous driving and advanced driver-assistance systems. Traditionally, road boundary detection in autonomous driving relies on cameras and LiDAR. However, they are vulnerable to poor lighting conditions, such as nighttime and direct sunlight glare, or prohibitively expensive for low-end vehicles.MethodsThis paper introduces 4DRadarRBD, the first road boundary curve detection method based on 4D mmWave radar, which is cost-effective and robust in complex driving scenarios. The main idea is that road boundaries (e.g., fences, bushes, roadblocks) reflect millimeter waves, thus generating point cloud data for the radar. To overcome the challenge that the 4D mmWave radar point clouds contain many noisy points, we initially reduce noisy points via physical constraints for road boundaries and then segment the road boundary points from the noisy points by incorporating a distance-based loss which penalizes for falsely detecting the points far away from the actual road boundaries. In addition, we capture the temporal dynamics of point cloud sequences by utilizing each point’s deviation from the vehicle motion-compensated road boundary detection result obtained from the previous frame, along with the spatial distribution of the point cloud for point-wise road boundary segmentation.ResultsWe evaluated 4DRadarRBD through real-world driving tests and achieved a road boundary point segmentation accuracy of 93%, with a median distance error of up to 0.023 m and an error reduction of 92.6% compared to the baseline model.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1622256</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1622256</link>
        <title><![CDATA[Correlation of precipitable water vapor and heavy rainfall over Cyprus using GNSS sensors network]]></title>
        <pubdate>2025-09-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Despina Giannadaki</author><author>Christina Oikonomou</author><author>Haris Haralambous</author>
        <description><![CDATA[The application of technological systems to monitor and provide nowcasting, forecasting and early warning of convective storms, such as Medicanes (hurricane-like cyclonic systems in the Mediterranean Sea), particularly on a short-term temporal and small-scale spatial context, is crucial to a wide spectrum of societal sectors including public safety, protection of agricultural production and protection of infrastructures. Weather forecast updates in Numerical Weather Prediction (NWP) models suffer from two main problems: a) The updates on impending weather conditions, including alerts for precipitation, are issued every 6 h b) These updates may not represent the real weather conditions near the area of interest. Increasing the spatial and temporal coverage by meteorological radars can help to face these issues; however, it is a very costly solution, involving high initial purchase costs, installation expenses, and ongoing maintenance. Alternative low-cost solutions, such as GNSSs (Global Navigation Satellite Systems) are necessary to enhance the continuous atmospheric sensing of various parameters in near-real time including water vapor, temperature, and pressure, by analyzing the signals received from GNSS satellites. The rapid spatiotemporal variations of Precipitable Water Vapor (PWV) in the low atmosphere comprises one more challenge to NWP models forecasting accuracy. Though many studies have evidenced continuous reinforcement of PWV before the heavy rainfall, there is still a great difficulty to determine a rigid relationship between rainfall and PWV that could be incorporated to a nowcasting model. In this context, the present study aims to investigate the possible correlation between PWV and heavy rainfall, during 81 selected heavy and extreme precipitation events occurring during 2022-2024 over Cyprus Island. To achieve this, we exploited both GNSS and ERA5 (the fifth generation ECMWF atmospheric Reanalysis) PWV data and rainfall observations over 12 meteorological stations of Cyprus. An increase in PWV before most heavy rain events was found with the time-lag of PWV peaks from the heavy rain onset having a range from one to six hours in most events. The Correlation Coefficient R, between maximum PWV peaks and the related maximum precipitation peaks shows a very high correlation (R = 0.85) over the mountainous region of the island and a satisfactory correlation both in coastal and all Cyprus regions (R = 0.5).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1468789</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1468789</link>
        <title><![CDATA[Water vapor density field estimation using commercial microwave link attenuation combined with temperature measurements]]></title>
        <pubdate>2025-01-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Itay Bragin</author><author>Yoav Rubin</author><author>Pinhas Alpert</author><author>Jonatan Ostrometzky</author>
        <description><![CDATA[Accurate water vapor density (WVD) measurement is critical for weather models, health risk management, and industrial management among many other applications. A number of machine-learning based algorithms (e.g. support vector machine) for estimating water vapor density at a reference weather station using the received signal level values measured at a commercial microwave link has been proposed in the past, and also was expanded to include a combination of three commercial microwave links with temperature measurements to achieve a higher estimation accuracy (with respect to the root mean square error at a given location). In this paper, we leverage on the preliminary potential presented, and propose enhanced machine learning models that utilize a larger number of CMLs combined with temperature data inside a given area to estimate a reference weather station humidity measurements. We then show how the presented approach can be expanded to estimate the water vapor density field - taking into consideration the elevation via the humidity-elevation profile. The models were evaluated using data from 32 weather stations and 505 CMLs in Germany, with performance assessed through root mean square error (RMSE) and correlation coefficients (CC). The enhanced models achieved a mean RMSE of 0.587 g/m³ for WVD field estimation, outperforming prior approaches as well as can be used as "virtual weather stations" - to estimate the water vapor density values in locations where no actual weather stations exist.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1291878</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1291878</link>
        <title><![CDATA[Intensity estimation after detection for accumulated rainfall estimation]]></title>
        <pubdate>2024-02-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Taeer Weiss</author><author>Tirza Routtenberg</author><author>Jonatan Ostrometzky</author><author>Hagit Messer</author>
        <description><![CDATA[This work focuses on optimizing the estimation of accumulated rain from measurements of the attenuation level of signals from commercial microwave links (CMLs). The process of accumulated rain estimation is usually based on estimation after detection, where it is first determined whether there is rain for a specific period, and then the accumulated rain at the detected rainy period is estimated. Naturally, errors in detection affect the accuracy of the consequent accumulated rain estimation. Traditionally, the detection and the estimation steps are designed independently. The detection threshold is arbitrarily set at the lowest level that would be declared as rain, without considering its effect on the accuracy of the accumulated rain estimation. This study applies a novel method that sets a detection threshold to optimize estimation after detection and apply it for accumulated rain estimation. It is based on optimizing a post-detection estimation risk function that incorporates both the estimation and detection-related errors; this essentially takes into consideration the coupling of the detection and the estimation stages and thus optimizes the overall accumulated rainfall estimation. The proposed approach is applied to actual CML attenuation measurements taken from a cellular network in Gothenburg, Sweden. This demonstrates that the proposed method achieves better accuracy for accumulated rain estimation compared with the detection threshold being set independently.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1244530</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1244530</link>
        <title><![CDATA[Phase-only array transmit beamforming without iterative/numerical optimization methods]]></title>
        <pubdate>2023-08-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Danilo Orlando</author><author>Alfonso Farina</author>
        <description><![CDATA[In this letter, we address the problem of phase-only transmit beamforming to generate a wide beam with an almost flat mainlobe for phased arrays. Instead of resorting to time-demanding optimization procedures, the proposed method is grounded on the Fourier analysis and exploits the fact that radiation pattern can be written as the Fourier transform of the aperture illumination function. In this context, we consider a complex linear frequency modulated illumination function and derive the equations allowing for a control of the beam width. The related computational complexity is linear in the number of the array elements. The numerical examples show the effectiveness of the proposed method in forcing the desired beam shape with good sidelobes’ properties and also in comparison with an iterative competitor.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1198205</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1198205</link>
        <title><![CDATA[4DEgo: ego-velocity estimation from high-resolution radar data]]></title>
        <pubdate>2023-06-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Prashant Kumar Rai</author><author>Nataliya Strokina</author><author>Reza Ghabcheloo</author>
        <description><![CDATA[Automotive radars allow for perception of the environment in adverse visibility and weather conditions. New high-resolution sensors have demonstrated potential for tasks beyond obstacle detection and velocity adjustment, such as mapping or target tracking. This paper proposes an end-to-end method for ego-velocity estimation based on radar scan registration. Our architecture includes a 3D convolution over all three channels of the heatmap, capturing features associated with motion, and an attention mechanism for selecting significant features for regression. To the best of our knowledge, this is the first work utilizing the full 3D radar heatmap for ego-velocity estimation. We verify the efficacy of our approach using the publicly available ColoRadar dataset and study the effect of architectural choices and distributional shifts on performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1093203</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1093203</link>
        <title><![CDATA[Data-driven airborne bayesian forward-looking superresolution imaging based on generalized Gaussian distribution]]></title>
        <pubdate>2023-05-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hongmeng Chen</author><author>Zeyu Wang</author><author>Yingjie Zhang</author><author>Xing Jin</author><author>Wenquan Gao</author><author>Jizhou Yu</author>
        <description><![CDATA[Airborne forward-looking radar (AFLR) has been more and more impoatant due to its wide application in the military and civilian fields, such as automatic driving, sea surveillance, airport surveillance and guidance. Recently, sparse deconvolution technique has been paid much attention in AFLR. However, the azimuth resolution performance gradually decreases with the complexity of the imaging scene. In this paper, a data-driven airborne Bayesian forward-looking superresolution imaging algorithm based on generalized gaussian distribution (GGD- Bayesian) for complex imaging scene is proposed. The generalized gaussian distribution is utilized to describe the sparsity information of the imaging scene, which is quite essential to adaptively fit different imaging scenes. Moreover, the mathematical model for forward-looking imaging was established under the maximum a posteriori (MAP) criterion based on the Bayesian framework. To solve the above optimization problem, quasi-Newton algorithm is derived and used. The main contribution of the paper is the automatic selection for the sparsity parameter in the process of forward-looking imaging. The performance assessment with simulated data has demonstrated the effectiveness of our proposed GGD- Bayesian algorithm under complex scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1197240</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1197240</link>
        <title><![CDATA[RIS-aided integrated sensing and communication: a mini-review]]></title>
        <pubdate>2023-05-05T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Mirza Asif Haider</author><author>Yimin D. Zhang</author>
        <description><![CDATA[Integrating sensing and communication (ISAC) is a cutting-edge technology aimed at achieving high-resolution target sensing and high data-rate communications using a shared spectrum. This innovative approach optimizes the usage of the radio spectrum with no or minimal level of mutual interference. The capability of reconfigurable intelligent surface (RIS) to control the environment and provide additional degrees of freedom is driving the development of RIS-aided ISAC. In this mini-review, we provide an overview of the current state-of-the-art of RIS-aided ISAC technology, including various system configurations, approaches, and signal processing techniques.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.1074053</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.1074053</link>
        <title><![CDATA[Decimation keystone algorithm for forward-looking monopulse imaging on platforms with uniformly accelerated motion]]></title>
        <pubdate>2023-01-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ze-Sen Li</author><author>Yue-Li Li</author>
        <description><![CDATA[Forward-looking imaging for maneuvering platforms has garnered significant interest in many military and civilian fields. As the maneuvering trajectory in the scanning period can be simplified as the constant acceleration maneuver, monopulse imaging is applied to enhance the azimuthal resolution of the forward-looking image. However, the maneuver causes severe range migration and Doppler shift; this often results in range location error due to the space-varying Doppler shifts and the failure of angle estimation. We propose a decimation keystone algorithm based on the chirp-Z transform (CZT). First, the pulse repetition frequency (PRF) is decimated with an integer; thus, the azimuthal sampling sequence is decimated into many sub-sequences. Then, the linear range walk correction (LRWC) is performed on each sub-sequence using the keystone transform, significantly reducing the influence of the change of Doppler-ambiguity-number on range location. Further, the sub-sequences are regrouped as one sequence, and the range curvature due to the acceleration is compensated in the frequency domain. Finally, the varying Doppler centroid in each coherent processing interval (CPI) is analyzed and compensated for the sum-difference angular measurements. Simulation results demonstrate the effectiveness of the proposed algorithm for forward-looking imaging under constant acceleration maneuvers and the feasibility of range location error correction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.977475</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.977475</link>
        <title><![CDATA[Editorial: Women in signal processing]]></title>
        <pubdate>2022-08-16T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Hagit Messer</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.864538</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.864538</link>
        <title><![CDATA[Road Users Classification Based on Bi-Frame Micro-Doppler With 24-GHz FMCW Radar]]></title>
        <pubdate>2022-05-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rudi Coppola</author><author>Sajid Ahmed</author><author>Mohamed-Slim Alouini</author>
        <description><![CDATA[This study shows an approach for classifying road users using a 24-GHz millimeter-wave radar. The sensor transmits multiple linear frequency–modulated waves, which enable range estimation and Doppler-shift estimation of targets in the scene. We aimed to develop a solution for localization and classification, which yielded the same performance when the sensor was fixed on ground or mounted on a moving platform such as a car or quadcopter. In this proposed approach, classification was achieved using supervised learning and a set of hand crafted features independent of relative speed between the target and sensor. The proposed model is based on obtaining micro-Doppler information; only one receiver is used. Therefore, in addition to the target reflectivity, no geometrical information is used. For our study, we selected three classes: pedestrians, cyclists, and cars. We then illustrated distinctive micro-Doppler features for each class based on simulations, which we compared with real-world data. Our results confirm that a limited set of low-complexity features yields high accuracy scores when the target’s trajectory does not excessively deviate from the radar’s radial direction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.835743</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.835743</link>
        <title><![CDATA[Optimization of Network Throughput of Joint Radar Communication System Using Stochastic Geometry]]></title>
        <pubdate>2022-04-28T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Shobha Sundar Ram</author><author>Shubhi Singhal</author><author>Gourab Ghatak</author>
        <description><![CDATA[Recently joint radar communication (JRC) systems have gained considerable interest for several applications such as vehicular communications, indoor localization and activity recognition, covert military communications, and satellite based remote sensing. In these frameworks, bistatic/passive radar deployments with directional beams explore the angular search space and identify mobile users/radar targets. Subsequently, directional communication links are established with these mobile users. Consequently, JRC parameters such as the time trade-off between the radar exploration and communication service tasks have direct implications on the network throughput. Using tools from stochastic geometry (SG), we derive several system design and planning insights for deploying such networks and demonstrate how efficient radar detection can augment the communication throughput in a JRC system. Specifically, we provide a generalized analytical framework to maximize the network throughput by optimizing JRC parameters such as the exploration/exploitation duty cycle, the radar bandwidth, the transmit power and the pulse repetition interval. The analysis is further extended to monostatic radar conditions, which is a special case in our framework. The theoretical results are experimentally validated through Monte Carlo simulations. Our analysis highlights that for a larger bistatic range, a lower operating bandwidth and a higher duty cycle must be employed to maximize the network throughput. Furthermore, we demonstrate how a reduced success in radar detection due to higher clutter density deteriorates the overall network throughput. Finally, we show a peak reliability of 70% of the JRC link metrics for a single bistatic transceiver configuration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.822285</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.822285</link>
        <title><![CDATA[CRLBs for Location and Velocity Estimation for MIMO Radars in CES-Distributed Clutter]]></title>
        <pubdate>2022-03-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Neda Rojhani</author><author>Maria Sabrina Greco</author><author>Fulvio Gini</author>
        <description><![CDATA[In this article, we investigate the problem of jointly estimating target location and velocity for widely separated multiple-input multiple-output (MIMO) radar operating in correlated non-Gaussian clutter, modeled by a complex elliptically symmetric (CES) distribution. More specifically, we derive the Cramér–Rao lower bounds (CRLBs) when the target is modeled by the Swerling 0 model and the clutter is complex t-distributed. We thoroughly analyze the impact of the clutter correlation and spikiness to provide accurate performance estimation. Index terms—Cramér–Rao lower bounds (CRLBs), MIMO radar, location and velocity estimation, performance analysis, complex elliptically symmetric (CES) distributed, and complex t-distribution.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.847980</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.847980</link>
        <title><![CDATA[CW Doppler Radar as Occupancy Sensor: A Comparison of Different Detection Strategies]]></title>
        <pubdate>2022-03-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gianluca Gennarelli</author><author>Vittorio Emanuele Colonna</author><author>Carlo Noviello</author><author>Stefano Perna</author><author>Francesco Soldovieri</author><author>Ilaria Catapano</author>
        <description><![CDATA[Indoor occupancy sensing is a crucial problem in several application fields that have progressed from intrusion detection systems to automatic control of lighting, heating, air conditioning and many other presence-related loads. Continuous wave Doppler radar is a simple technology to face this problem due to its capability to detect human body movements (e.g., walk, run) and small chest wall vibrations associated to the cardiorespiratory activity. This work deals with a radar prototype operating at 2.4 GHz as a real-time occupancy sensor. The emphasis is on data processing approaches devoted to extract useful information from raw radar signal. Three different strategies, designed to detect human presence in indoor environments, are considered and the main goal is the assessment and comparison of their performance against experimental data collected in controlled conditions. The first strategy is based on the analysis of the standard deviation of the radar signal in time-domain; whereas the second one exploits the histogram of the time-varying signal amplitude. Finally, a third strategy based on an energy measure of the received signal Doppler spectrum is considered. The proposed detection algorithms are optimized through a set of calibration measurements and their performances and robustness are assessed by laboratory trials.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.822894</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.822894</link>
        <title><![CDATA[Learning Resource Allocation in Active-Passive Radar Sensor Networks]]></title>
        <pubdate>2022-02-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zenon Mathews </author><author>Luca Quiriconi</author><author>Christof Schüpbach</author><author>Peter Weber</author>
        <description><![CDATA[Recent advances in Passive Coherent Location (PCL) systems make combined active and passive radar sensor networks very attractive for both military and civilian air surveillance. PCL systems seem promising as cost-effective gap fillers of active radar coverage especially in alpine terrain and also as covert early warning sensors. However, PCL systems are sensitive to changes of Transmitters of Opportunity (ToO). Many approaches for energy-efficient target detection have been proposed for active radar sensor networks. However, energy-efficiency and topology optimization of combined active-passive radar sensor networks in realistic scenarios have been poorly studied until today. We here propose an unsupervised learning approach for topology optimization and energy-efficient detection in combined active-passive radar sensor networks. The interdependence of active and passive sensors in the network and the given target scenario is naturally accounted for by our approach. Optimal power budget and detection sectors of active radars and the most useful ToOs for each PCL sensor are simultaneously learned over time. This is a critical contribution for minimizing the need for active radar power budget and PCL computational resources. The power budget of active radars is minimized in a way that the added value of PCL sensors is fully exploited. We also demonstrate how our approach dynamically relearns to achieve robust performance when changes in the ToO of PCL sensors occur. We test our approach in a simulation suite for active-passive radar sensor networks using real-world air surveillance data and ToOs under real-world topographical conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2021.781777</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2021.781777</link>
        <title><![CDATA[Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight]]></title>
        <pubdate>2021-12-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dilan Dhulashia</author><author>Nial Peters</author><author>Colin Horne</author><author>Piers Beasley</author><author>Matthew Ritchie</author>
        <description><![CDATA[The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2021.782182</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2021.782182</link>
        <title><![CDATA[Persymmetric Adaptive Union Subspace Detection]]></title>
        <pubdate>2021-12-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liyan Pan</author><author>Yongchan Gao</author><author>Zhou Ye</author><author>Yuzhou Lv</author><author>Ming Fang</author>
        <description><![CDATA[This paper addresses the detection of a signal belonging to several possible subspace models, namely, a union of subspaces (UoS), where the active subspace that generated the observed signal is unknown. By incorporating the persymmetric structure of received data, we propose three UoS detectors based on GLRT, Rao, and Wald criteria to alleviate the requirement of training data. In addition, the detection statistic and classification bound for the proposed detectors are derived. Monte-Carlo simulations demonstrate the detection and classification performance of the proposed detectors over the conventional detector in training-limited scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2021.742441</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2021.742441</link>
        <title><![CDATA[Handling Radar Cross-Section Performance in Monitoring Vital Signs Under Constraint Conditions]]></title>
        <pubdate>2021-10-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Faheem Khan</author><author>Saleh M. Sherazi</author><author>Naeem Khan</author><author>Imran Ashraf </author><author>Fahad Khan</author>
        <description><![CDATA[Two vital signs including heartbeat and respiratory rate are monitored in this work under two constraint situations; namely noise disturbance and intermittent observations. The existing scheme for finding, measuring and monitoring vital signs was Fourier Transform which could not deal with non-stationary process. As an alternative, the Wavelet Transform is used in this work which is equally applicable to both stationary and non-stationary processes. Additionally, the loss of output data may result in crucial implications in observing vital signs. Formerly, only un-interrupted data has been amalgamated in tracing vital signs. A novel adaptive ARMA-based scheme is proposed to obtain optimum estimated results in the presence of the above two critical scenarios. Simulation results obtained on real (practical) data show that the ARMA-based model produces similar vital signs as shown by clean and un-distorted data. It is shown that the proposed ARMA-based algorithm improves the breathing rate accuracy by 0.3% and heart rate accuracy by 2.5% as compared to the existing AR-based vital signal reconstruction algorithm.]]></description>
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