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Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Additionally, FCM clustering is utilized for symbol mapping reconstruction. Both simulation and experimental results show that the proposed method significantly suppresses constellation diagram divergence and phase rotation, increases the adaptive cancellation gain and signal-to-noise ratio (SNR), and in the meantime reduces the computation cost. [ABSTRACT FROM AUTHOR]

The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual‐frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis‐derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high‐latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user‐friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation. Plain Language Summary: This paper summarizes the creation of a new, gridded data set containing precipitation, atmospheric water vapor content, and atmospheric river (AR) information from May 2014 to December 2022. The precipitation and water vapor data are both remotely‐sensed data products using the Global Precipitation Measurement Core Observatory Microwave Imager (GMI). The AR algorithm detection is more sensitive to moisture transport than global algorithms and is thus better suited to colder and drier environments. We temporally match and interpolate all data to a common grid and provide examples of the merged data to illustrate the seasonality and impacts of AR presence on precipitation. Lastly, we demonstrate the utility of this data set by investigating the assumptions used to estimate precipitation from GMI measurements. The data set confirms that ARs are associated with warmer temperatures, higher water vapor content, and more intense precipitation rates. Key Points: A new data set is presented that collocates satellite‐derived precipitation rates and water vapor with reanalysis‐derived Atmospheric rivers (ARs)Higher precipitation rates and water vapor content are coincident with AR events in the high‐latitude regionsAn example application illustrates warmer, more moist conditions during precipitating AR events that are present in the GPROF database [ABSTRACT FROM AUTHOR]

A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (<5 m/s) were due probably to the swell effects. This study proves that the two-scale pBRDF matrix is suitable for the applications of multiple types of active instruments and can consistently simulate the ocean surface passive and active signals. [ABSTRACT FROM AUTHOR]

This paper details the design and implementation of a harmonic frequency-modulated continuous-wave (FMCW) radar system, specialized in detecting harmonic tags and achieving precise range estimation. Operating within the 2.4–2.5 GHz frequency range for the forward channel and 4.8–5.0 GHz for the backward channel, this study delves into the various challenges faced during the system's realization. These challenges include selecting appropriate components, calibrating the system, processing signals, and integrating the system components. In addition, we introduce a single-layer passive harmonic tag, developed specifically for assessing the system, and provide an in-depth theoretical analysis and simulation results. Notably, the system is characterized by its low power consumption, making it particularly suitable for short-range applications. The system's efficacy is further validated through experimental evaluations in a real-world indoor environment across multiple tag positions. Our measurements underscore the system's robust ranging accuracy and its ability to mitigate self-interference, showcasing its significant potential for applications in harmonic tag detection and ranging. [ABSTRACT FROM AUTHOR]

This paper presents a simple and fast closed-form solution approach for two-dimensional (2D) target localization using range difference (RD) measurements. The formulation of the localization problem is derived using a pentagram array. The target position is determined using passive radar measurements (RDs) between the target and the ( N + 1 = 10 ) receivers' locations. The method facilitates the problem of target position and can be used as a counter-parallel method for spherical interpolation (SI) and spherical intersection (SX) methods in time difference of arrival (TDOA) and radar systems. The performance of the method is examined in 2D target localization using numerical analysis under the distribution of receivers in the pentagram array. The simulations are conducted using four different far-distance targets and comparatively large-area distributed receivers. The RD measurements were distorted by two different values of Gaussian errors based on ionosphedriec time delays of 20 and 50 nsec owing to the different receivers' positions. The findings highly verified the validity of the method for addressing the problem of target localization. Additionally, a theoretical accuracy study of the method is given, which solely relies on the RD measurements. [ABSTRACT FROM AUTHOR]

Starting December 2019, a novel coronavirus (SARS-CoV-2) spread among humans across the world. From 2020 onward, farmed mink were found susceptible to the virus. In this paper, we describe the Dutch surveillance system and the added surveillance components for early detection of SARS-CoV-2 outbreaks and their results in Dutch mink farms. In the Netherlands, a surveillance system was in place in which mink farmers could submit carcasses for postmortem evaluation and could contact a telephone helpdesk for veterinary advise. Through this system, the first SARS-CoV-2 outbreak in two mink farms was detected in April 2020. Immediately, the Dutch Ministry of Agriculture commissioned a consortium of statutory and research institutes to intensify the surveillance system. The program consisted of both passive surveillance, i.e., mandatory notifications and active surveillance components, i.e., serological screenings and weekly risk-based sampling of dead mink for early detection of new SARS-CoV-2 infections. When one of the surveillance components indicated a suspicion of a possible SARS-CoV-2 infection, follow-up samplings were conducted and at confirmation, all mink were culled. During 2020, 67 out of 124 mink farms that were under surveillance became infected with SARS-CoV-2 (54%). Of these, 31 were detected based on clinical signs (passive surveillance of clinical signs) and 36 were detected through active surveillance. From the mink farms with a new SARS-CoV-2 outbreak that was detected through the surveillance, in 19% of the farms (n = 7), the mink never showed any clinical signs of SARS-CoV-2 and might have been missed by the passive notification system. This study underlines the added value of a surveillance system that can quickly be intensified. The subsequent combination of both passive and active surveillance has shown to be effective in the early detection of emerging pathogens, which is important to minimize the risk of zoonotic spill-over. [ABSTRACT FROM AUTHOR]

The authors study the problem of compressive target detection in a single‐frequency network (SFN)‐based multistatic passive radar system (MS‐PRS) consisting of multiple illuminators of opportunity (IOs) and one receiver. Firstly, a generalised likelihood ratio test (GLRT)‐based SFN‐based compressive subspace detector (SFN‐CSD) is derived by exploiting the sparsity of the target echoes for the case of known noise variance. When the noise variance is unknown, an SFN‐based unknown‐noise (UN) compressive subspace detector is proposed, referred to as the SFN‐UNCSD. Moreover, closed‐form expressions of the probability of false alarm and detection of the proposed detectors are deriived. It is proved that the SNF‐UNCSD has a constant false alarm rate (CFAR) property. Finally, numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors. [ABSTRACT FROM AUTHOR]

In this paper, the possibility of improving target detection performance in passive bistatic radar by exploiting a frequency agile (FA) signal is investigated, namely frequency agile signal-based passive bistatic radar (FAPBR) coherent integration. Since the carrier frequency of each pulse signal is agile, FAPBR coherent integration suffers from the problems of random range and Doppler phase fluctuations. To tackle these challenges, a novel FA signal coherent integration target detection scheme for PBR is proposed. In particular, the phase quadratic difference principle is presented for eliminating Doppler phase hopping. Then, frequency rearrangement is adopted to compensate for random range phase fluctuation while obtaining the high-range-resolution profiles (HRRPs) of the detecting target. Further, we innovatively present a sliding-range ambiguity decoupling (S-RAD) method to remove the range ambiguity effect in the case of the high pulse repetition frequency (HPRF). Compared with the existing methods, the proposed method can effectively mitigate Doppler phase hopping without requiring prior target velocity information, offering improved coherent integration performance in frequency agile signals with reduced computational complexity. Moreover, it successfully corrects the range ambiguity issue caused by HPRF. Finally, a series of simulation results are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this study, a passive radar system that detects flying targets is developed in order to solve the problems associated with traditional flying target detection systems (i.e., their large size, high power consumption, complex systems, and poor battlefield survivability). On the basis of target detection, the system uses the multipath signal (which is usually eliminated as an error term in navigation and positioning), enhances it by supporting information, and utilizes the multi-source characteristics of ordinary omnidirectional global navigation satellite system (GNSS) signals. The results of a validation experiment showed that the system is able to locate a passenger airplane and obtain its flight trajectory using only one GNSS receiving antenna. The system is characterized by its light weight (less than 5 kg), low power consumption, simple system, good portability, low cost, and 24/7 and all-weather work. It can be installed in large quantities and has good prospects for development. [ABSTRACT FROM AUTHOR]

Mixed-phase clouds are not well represented in climate and weather forecasting models, due to a lack of the key processes controlling their life cycle. Developing methods to study these clouds is therefore essential, despite the complexity of mixed-phase cloud processes and the difficulty of observing two cloud phases simultaneously. We propose in this paper a new method to retrieve the microphysical properties of mixed-phase clouds, ice clouds and supercooled water clouds using airborne or satellite radar and lidar measurements. This method extends an existing variational method developed for ice clouds retrieval using lidar, radar and passive radiometers. We assume that the attenuated lidar backscatter β at 532 nm is more sensitive to particle concentration and is consequently mainly sensitive to the presence of supercooled water. In addition, radar reflectivity Z at 95 GHz is sensitive to the size of hydrometeors and hence more sensitive to the presence of ice crystals. Consequently, in the mixed-phase the supercooled liquid droplets are retrieved with the lidar signal and the ice crystals with the radar signal, meaning that the retrieval rely strongly on a priori and errors values. This method retrieves then simultaneously the visible extinction for ice αice and liquid αliq particles, the ice and liquid water contents IWC and LWC, the effective radius of ice re,ice and liquid re,liq particles and the ice and liquid number concentrations Nice and Nliq. Moreover, total extinction αtot, total water content TWC and total number concentration Ntot can also be estimated. As the retrieval of ice and liquid is different, it is necessary to correctly identify each phase of the cloud. To this end, a cloud phase classification is used as input to the algorithm and has been adapted for mixed-phase retrieval. The data used in this study are from DARDAR-MASK v2.23 products, based on the CALIOP lidar and CPR radar observations, respectively from the CALIPSO and CloudSat satellites belonging to the A-Train constellation launched in 2006. Airborne in situ measurements performed on the 7th April 2007 during the ASTAR campaign and collected under the track of CloudSat-CALIPSO are compared to the retrievals of the new algorithm to validate its performance. Visible extinctions and water contents properties derived from in situ measurements and the retrievals showed similar trends and are globally in good agreement. The mean percent error between the retrievals and in situ is 39 % for αliq, 398 % for αice, 49 % for LWC and 75 % for IWC. It is also important to note that the temporal and spatial collocations are not always optimal, that the sensibility of remote sensing and in situ are not the same and that in situ measurements uncertainties are between 25 % and 60 %. [ABSTRACT FROM AUTHOR]

The use of WiFi signals for sensing purposes has attracted a lot of interest from both the radar and communications communities and several techniques have been explored. In the attempt of meeting the requirements for small sensor size, compactness, and easy deployment, the authors consider reference‐free approaches, namely approaches that do not require a good copy of the transmitted waveform to be available at the radar receiver. To this end, the authors first resort to a passive radar‐based processing scheme that only exploits the invariant a priori known initial portion of the physical layer protocol data unit, that is, the PHY Preamble, and its limitations in practical applications is investigated. Specifically, the authors show that, with this approach, an accurate time, phase, and frequency synchronization is essential and a possible strategy is investigated. As an alternative solution the authors consider a forward scatter radar‐based approach where only the amplitude modulation of the received signal is exploited to detect the presence of a moving target thus avoiding the need to know the transmitted signal. For the first time, the authors comparatively investigate advantages and drawbacks of the two reference‐free approaches and present practical strategies to handle the limitations observed. The results are reported for experimental tests with people and drones using WiFi transmissions in the 2.4 and 5 GHz band. [ABSTRACT FROM AUTHOR]

The paper focuses on the results of experimental research on the possibility of passive space object detection using a single radio telescope from the European Low‐Frequency Array (LOFAR) network of astronomical radio telescopes. Commercial digital television (DVB‐T) transmitters were used as illuminators of opportunity in this radar system. In the conducted experiments, one LOFAR radio telescope served both as a surveillance receiver and reference receiver in a passive radar system. The greater part of the LOFAR telescope array was used to observe a space object, while a small part of the array was directed towards the illuminator of opportunity to record the reference signal. One of the most important problems to overcome with utilising the LOFAR radio telescope in such a solution was the effective suppression of the direct‐path component in a surveillance signal coming from the illuminator of opportunity, which was relatively close to the LOFAR radio telescope. The results regarding the passive detection of the International Space Station, included in the paper, confirm the possibility of observing the space object flying in Low‐Earth Orbit using the LOFAR telescope or another receiving system with a similar antenna array. [ABSTRACT FROM AUTHOR]

This paper proposes a simple non‐adaptive approach for the suppression of direct signal and clutter contributions in a passive radar system based on orthogonal frequency division multiplexing transmissions, exploiting the properties of a reciprocal filter (RF). The use of a RF allows to circumvent the limits posed by the waveform ambiguity function, producing a data‐independent time‐invariant response to a stationary point‐like target echo at the output of the range compression stage. This feature enables simple clutter cancellation strategies, based on the subtraction of delayed portions of the surveillance signal, according to the conventional moving target indication methodologies. The proposed processing scheme consists of a range compression stage based on a RF, followed by a Single Canceller. Its performance is investigated against a simulated and an experimental data set, for the case of a digital video broadcasting‐terrestial‐based passive radar scenario, and it is compared to an alternative scheme based on a conventional matched filter and adaptive cancellation techniques. The proposed approach is shown to yield a perfect cancellation of the clutter returns from a stationary scene under ideal conditions. Moreover, it proves to be robust to real‐world conditions, despite its considerably limited computational load compared to adaptive clutter cancellation techniques. [ABSTRACT FROM AUTHOR]

Recently, several constellations of broadband low‐Earth orbit communication satellites, such as Starlink or OneWeb, have started to be deployed to provide global internet access. Based on performed analyses, these emerging constellations are considered promising candidates as space‐based transmitters of opportunity for passive radar applications due to their advantageous characteristics in terms of global coverage, high bandwidth (i.e. better resolution), high received power on Earth surface (i.e. increased maximum range), predictable trajectory, and network density and robustness. However, they also pose certain challenges for the development of passive radar systems due to the necessity to track the satellites with a narrow beam reference antenna, their fast‐relative dynamics which induce range and Doppler migration, and the use of non‐standardised signals with little publicly available information, which in addition are not optimised for radar usage. In this article, these capabilities and challenges are analysed, putting a special focus on the estimation of the achievable performance and on the experimental characterisation of the transmitted signals. To this end, the proposed passive radar architectures and the first developed prototypes are also presented. Showing the potential capabilities of these emerging illuminators of opportunity, the authors aim to promote further development of this technology for multiple applications. [ABSTRACT FROM AUTHOR]

This article puts forward a ship target detection and localisation technique for GNSS‐based passive multistatic radar. The approach capitalises on the large spatial diversity offered by navigation satellite constellations to provide maritime awareness using short integration time windows, thus bypassing the shortcomings suffered by approaches relying on long dwells typically considered in satellite‐based passive radar systems. The proposed approach entirely operates on the Cartesian plane and it is able to provide in a single stage the detection and localisation of the target in the surveyed area. Theoretical and simulated performance analysis are provided to illustrate as the proposed approach can outperform conventional two‐stages (i.e. bistatic detection then localisation) procedures, with particular regard to the case of significant radar cross section scintillation, likely experienced in the system under consideration. The effectiveness of the approach has been verified via experimental data acquired in a few scenarios of interest comprising opportunistic targets belonging to different types. [ABSTRACT FROM AUTHOR]

Due to limitations in computing power, the passive radar based on GNSS signal may not be able to use all the GNSS signals, requiring to make signal selection. Based on CRLB, a new radiation sources adaptive selection method for passive radar is proposed. With the goal of minimizing the CRLB of GNSS signals and the number of GNSS signals as constraints, an adaptive selection model is established. Simulation experiments were conducted using the selection model and particle filter algorithm. Experimental results show that the calculation accuracy of this algorithm is slightly lower than the tracking results obtained using all six satellites, but the calculation time is greatly reduced, reducing the requirement for radar computing power. [ABSTRACT FROM AUTHOR]

Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the development of hardware and software platforms capable of efficiently processing signals from passive bistatic radar has emerged as a research focus in this field. This research investigates the signal processing flow of passive bistatic radar based on its characteristics and devises a parallel signal processing scheme under graphic processing unit (GPU) architecture for computation-intensive tasks. The proposed scheme utilizes high-computing-power GPU as the hardware platform and compute unified device architecture (CUDA) as the software platform and optimizes the extensive cancellation algorithm batches (ECA-B), range Doppler and constant false alarm detection algorithms. The detection and tracking of a single target are realized on the passive bistatic radar dataset of natural scenarios, and experiments show that the design of this algorithm can achieve a maximum acceleration ratio of 113.13. Comparative experiments conducted with varying data volumes revealed that this method significantly enhances the signal processing rate for passive bistatic radar. [ABSTRACT FROM AUTHOR]

Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy. [ABSTRACT FROM AUTHOR]

The rapidly increasing number of satellites in Earth's orbit motivates the development of Space Domain Awareness (SDA) capabilities using wide field-of-view sensor systems that can perform simultaneous detections. This work demonstrates preliminary orbit determination capability for Low Earth Orbit objects using the 36 ° × 36 ° field-of-view of the Murchison Widefield Array (MWA) at commercial Frequency Modulated (FM) frequencies (transmitters in 88 - 108 MHz range). Non-coherent passive radar techniques with the MWA produce spatially smeared detections, due to time averaging in the MWA's standard signal processing chain. The work develops methods to extract time-stamped measurements of a satellite's angular coordinates from these data. The developed method was tested on observations of 32 satellite passes and the extracted measurements were used to perform orbit determination for the targets using a least-squares fitting approach. The target satellites span a range in altitude and Radar Cross Section, providing examples of both high and low signal-to-noise detections. The estimated orbital elements for the satellites are validated against the publicly available Two Line Element (TLE) updates provided by the Space Surveillance Network (SSN) and the preliminary estimates are found to be in close agreement. The work also tests for re-acquisition for one target using the orbital elements and finds the trajectory predicted by the method to coincide within 0.2 ° cross-track and 0.3 ° in-track for a subsequent pass, reduced to approximately 0.1 ° cross-track (less than one kilometre) if two passes are used to predict the subsequent pass (using simple two-body propagation). The median uncertainty in the angular position for objects in LEO (range less than 1000 km) is found to be 860 m in the cross-track direction and 780 m in the in-track direction, which are comparable to the typical uncertainty of ∼ 1000 m in the publicly available TLE information. The techniques, therefore, demonstrate the MWA to be capable of being a valuable contributor to the global SDA community. Based on the understanding of the MWA SDA system, this paper also briefly describes methods to mitigate the impact of FM-reflecting LEO satellites on radio astronomy observations, and how maintaining a catalog of FM-reflecting LEO objects is in the best interests of both SDA and radio astronomy. [ABSTRACT FROM AUTHOR]

—In this article a novel method for satellite identification is presented for picosatellites. Utilizing apriori knowledge of the transmitted signals the cross-correlation of the received signal and a known transmission is calculated, from the results the Doppler shift is estimated and the satellite’s Doppler curve can be matched against the measurements of NORAD. Using this method an omnidirectional antenna can be used instead of a high gain directional antenna, and the gain difference is compensated by the processing gain of the algorithm described. The practicality of the algorithm is demonstrated through the mission of MRC-100 PocketQube. [ABSTRACT FROM AUTHOR]

Passive bistatic radar (PBR)-based moving target detection (MTD) has benefited greatly from multi-frequency (MF) integration, which can effectively improve the detection capability of weak targets. However, with the increase in the coherent processing interval (CPI) and carrier-frequency separation, Doppler spread will appear in the range-Doppler maps (RDMs) over different frequency bands, which severely limits the processing gain of MF integration. In this paper, a novel MTD algorithm is proposed to achieve both long-time integration and quasi-coherent MF integration. More specifically, the proposed method consists of two main steps, where a modified Radon–Fourier transform (RFT), termed as MF-based RFT (MF-RFT), is, firstly, used to eliminate the Doppler spread via designing a sequential of MF-based Doppler filter banks. Following the MF-RFT, a phase-compensation-based method is also developed to further remove the residual phase errors. This method involves formulating an optimization problem based on the minimum-entropy criterion and employing a particle swarm optimization (PSO) algorithm to solve it, after which quasi-coherent MF integration can be achieved with robustness. Both numerical results and field test results based on digital video broadcasting-satellite (DVB-S) signals demonstrate that the proposed algorithm outperforms the existing methods in the scenario of weak MTD. [ABSTRACT FROM AUTHOR]

Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of the reference signal being contaminated by target echoes due to factors such as wide beamwidths, beam pointing errors, or site errors in passive bistatic radar (PBR). Such contamination can lead to reference signal impurity and subsequently degrade the performance of traditional object detection methods. To overcome this challenge, a novel target detection method centered around the reference signal's purification is presented. The proposed method involves the modeling, analysis, localization, and purification of the impure reference signal. First, a new signal model incorporating the impure reference signal is established, followed by a comprehensive analysis of its impact on target detection. Then, leveraging the valuable information hidden in the impure reference signal, target echoes within the impure reference signal are localized and reconstructed. Finally, the reconstructed target echoes are subtracted from the original impure reference signal to purify the reference signal. Based on the purified reference signal, traditional target detection is performed to detect the expected targets. The core advantage of this method lies in its efficient reconstruction and removal of the impure terms within the reference signal. The effectiveness of the proposed method is demonstrated through a series of simulation results. [ABSTRACT FROM AUTHOR]

Forward scattering radar has been widely used to classify human movements. To reduce concern of prolonged radiation exposure, Wi-Fi based passive scattering radar with bistatic topology is proposed. The movements include walking, bending, sitting, and kneeling. The enveloping algorithm applied to Wi-Fi based passive forward scattering radar signals has produced a comparable pattern to the ones from the active configuration. The features extracted from the frequency spectrum are also similar for both radar topologies. These led to comparable performance when classifying using artificial neural network. Hence, WiFi can effectively reduce the cost of radar sensors. [ABSTRACT FROM AUTHOR]

The first closure study involving passive microwave and submillimetre measurements of ice clouds with the consideration of oriented particles is presented, using a unique combination of polarised observations from the ISMAR spectrallike radiometer, two radars with frequencies of 35 and 95 GHz, and a variety of in-situ instruments. Of particular interest to this study are the large V-H polarised brightness temperature differences measured from ISMAR above a thick frontal ice cloud. Previous studies combining radar and passive submillimetre measurements have not considered polarisation differences. Moreover, they have assumed particle habits a-priori. We aim to test whether the large V-H measurements can be simulated successfully by using an atmospheric model consistent with in-situ microphysics. An atmospheric model is constructed using information from the in-situ measurements, such as the ice water content, the particle size distribution, and the mass and shape of particles, as well as background information obtained from dropsonde profiles. Columnar and dendritic aggregate particle models are generated specifically for this case, and their scattering properties are calculated using the Independent Monomer Approximation under the assumption of horizontal orientation. The scattering properties are used to perform polarised radiative transfer simulations using ARTS to test whether we can successfully simulate the measured large V-H differences. Radar measurements are used to extrapolate the 1D microphysical profile to derive a time-series of particle size distributions which are used to simulate ISMAR brightness temperatures. These simulations are compared to the observations. It is found that particle models that are consistent with in-situ microphysics observations are capable of reproducing the brightness temperature depression and polarisation signature measured from ISMAR at the dual-polarised channel of 243 GHz. However, it was required that a proportion of the particles were changed in order to increase the V-H polarised brightness temperature differences. Thus we incorporated mm-sized dendritic crystals, as these particles were observed in the probe imagery. At the second dual-polarised channel of 664GHz, the brightness temperature depressions were generally simulated at the correct locations, however the simulated V-H was too large. This work shows that multi-frequency polarisation information could be used to infer realistic particle shapes, orientations, and representations of the split between single crystals and aggregates within the cloud. [ABSTRACT FROM AUTHOR]

Passive bistatic radar research is essential for accurate 3D target tracking, especially in the presence of missing or low-quality bearing information. Traditional extended Kalman filter (EKF) methods often introduce bias in such scenarios. To overcome this limitation, we propose employing the unscented Kalman filter (UKF) for handling the nonlinearities in 3D tracking, utilizing range and range-rate measurements. Additionally, we incorporate the probabilistic data association (PDA) algorithm with the UKF to handle cluttered environments. Through extensive simulations, we demonstrate a successful implementation of the UKF-PDA framework, showing that the proposed method effectively reduces bias and significantly advances tracking capabilities in passive bistatic radars. [ABSTRACT FROM AUTHOR]

We propose a method of multisignal adaptive spatial filtering, which generalizes the classical method of adaptive spatial filtering of a single signal with a known arrival direction. Additionally, the formulas for the modified Capon direction-finding method are found. Reliability of the relationships, which are obtained on the basis of the maximum-likelihood method, is confirmed by the limiting transitions and the results of numerical simulation for typical signal-reception conditions in radio-monitoring and passive radar systems. [ABSTRACT FROM AUTHOR]

Aquatic non-indigenous species (NIS) threaten biodiversity, ecosystem functions, and the economy worldwide. Monitoring NIS is of immediate concern to identify newly arriving species, assess the efficacy of mitigation measures, and report long-term indicators of introduction, spread, and impacts. The challenges associated with conventional methods of specimen collection and morphological identification have led to the development of alternative methods, such as DNA-based methods, which could offer rapid and cost-effective detection of NIS. Depending on whether a few (targeted monitoring) or many species (passive monitoring) are being monitored, environmental DNA (eDNA) can infer presence-absence and relative abundances, enabling informed decisions and actions to be made based on patterns of detection. Compared to more conventional methods, eDNA tools can increase the levels of detection and sensitivity for rare and elusive species, which is even more noticeable for some taxa when using targeted monitoring. The use of DNA-based tools not only minimizes the onus on taxonomic expertise and reduces resource demands but can also be more sensitive and cost-efficient in detecting NIS, thus proving its value as an early warning tool. As nucleic acid (DNA/RNA) methods advance rapidly for NIS detection, there must be a balance between method sensitivity, logistical requirements, and associated costs, which must be factored into future management decisions. While there are many complementary reviews available, our aim is to emphasize the importance of incorporating eDNA tools into NIS surveys and to highlight the available opportunities in this field. [ABSTRACT FROM AUTHOR]

To address the problem that the traditional generalized cross correlation (GCC) method in ultra-short baseline (USBL) positioning systems has a poor delay estimation accuracy in a low signal-to-noise ratio environment or complex noise background, a generalized quadratic cross correlation (GQCC) time delay estimation algorithm based on signal preprocessing and fourth-order cumulants is proposed. The noisy signal was first preprocessed using singular value decomposition and wavelet denoising. Then, the delay was calculated using an algorithm that combined the GQCC and the fourth-order cumulant. The results of the simulation and sea trial demonstrate that the proposed method is superior to the GCC method and the GQCC method and may significantly increase the positioning accuracy of the USBL system. This technique can offer a fresh technological perspective for weak signal detection and passive positioning of small targets in ocean detection. [ABSTRACT FROM AUTHOR]

In this study, an anti‐deception jamming target tracking algorithm based on the data‐level fusion is proposed in a distributed radar network. First, the consensus covariance intersection (CCI) algorithm is proposed to improve tracking performance by introducing the consensus theory to the field of covariance intersection fusion. Second, unscented Kalman filter is utilised to implement the preprocessing of distributed filtering. Then, two distributed target discrimination schemes are developed for non‐collaborative and collaborative false targets respectively. For non‐collaborative false targets, according to the characteristics that estimated spatial locations of the false target from different radars are relatively scattered, a false target elimination method based on nearest neighbour algorithm is proposed. For collaborative false targets, after equipped with passive radars that are less vulnerable to interference, the consensus algorithm is introduced to convey the real target information to the unconnected active radars, thus achieving an effect of global anti‐deception jamming. Finally, the CCI algorithm is deployed for distributed fusion of physical target tracking, so as to improve the overall estimation performance. [ABSTRACT FROM AUTHOR]

Passive radar is an interesting approach in the context of non-cooperative target detection. Because the signal source takes advantage of the so-called illuminator of opportunity (IoO), the deployed system is silent, allowing the operator cheap, portable, and practically undetectable deployments. These systems match perfectly with the use of antenna arrays to take advantage of the additional gains provided by the coherent combination of the signals received at each element. To obtain these benefits, linear processing methods are required to enhance the system's performance. In this work, we summarize the main beamforming methods in the literature to provide a clear picture of the current state of the art. Next, we perform an analysis of the benefits and drawbacks and explore the chance of increasing the number of antenna elements. Finally, we identify the major challenges to be addressed by researchers in the future. [ABSTRACT FROM AUTHOR]

The problem of compressive detection of multiple targets in a passive bistatic radar system consisting of one illuminator of opportunity and one receiver is considered. Specifically, a measurement matrix is designed to remove the direct‐path and multi‐path interference and obtain the compressive observations simultaneously. Considering that the degree of sparsity, that is, the number of targets, is unknown, an orthogonal matching pursuit (OMP)‐based detector, referred to as the ‖rk‖∞‐based OMP detector, is proposed by analysing the distribution characteristics of the infinite norm of the residual. It is shown that the test statistic of the proposed detector leads to an order statistic, and then a closed‐form expression of probability of false alarm and detection of the proposed detector is provided. Finally, numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors. [ABSTRACT FROM AUTHOR]

The most common source of information about sea ice conditions is remote sensing data, especially images obtained from synthetic aperture radar (SAR) and passive microwave radiometers (PMR). Here we introduce an adaptive fusion scheme based on Graph Laplacians that allows us to retrieve the most relevant information from satellite images. In a first test case, we explore the potential of sea ice classification employing SAR and PMR separately and simultaneously, in order to evaluate the complementarity of both sensors and to assess the result of a combined use. Our test case illustrates the flexibility and efficiency of the proposed scheme and indicates an advantage of combining AMSR‐2 89 GHz and Sentinel‐1 data for sea ice mapping. Plain Language Summary: The Earth's land and ocean surface is monitored from space using different sensors mounted on various satellite platforms. Each type of sensor has its advantages and limitations. Combining data from different sensors can potentially solve ambiguities in information retrievals associated with the use of only a single sensor. Here, we apply a multi‐sensor fusion scheme that can be used for various data combinations in order to extract relevant information. The main goal of this work is to explore the potential of simultaneously applying two sensors for sea ice mapping and monitoring, namely synthetic aperture radar and passive microwave radiometers, in order to improve the separation of sea ice types. Key Points: We propose an adaptive scheme for the fusion of passive microwave radiometers and synthetic aperture radar data for sea ice classificationWe demonstrate the flexibility and efficiency of the proposed scheme on a test case by evaluating the performance of various data scenariosWe illustrate the advantages and limitations of applying the data of each sensor simultaneously and separately [ABSTRACT FROM AUTHOR]

In this work, a multistage target motion compensation solution for long integration times and the direction of arrival processing in geostationary-satellite-based passive radars is presented. Long integration processing intervals are considered to compensate for the associated propagation loss, but during this time target dynamics can extend the backscattering in more than one range or Doppler cell. To control the gain-processing reduction, a combination of detection, tracking, feature extraction, and filtering techniques is designed to provide automatic adaptation to each unknown target dynamic in the area of interest. The proposed methodology is validated with real data acquired by the passive radar demonstrator developed by the University of Alcalá (IDEPAR), and the results confirm that target monitoring exploiting digital video broadcasting-satellite (DVB-S) signals is clearly improved. [ABSTRACT FROM AUTHOR]

Traditional target tracking is carried out based on the point measurements extracted from the radar resolution cells. This is not suitable for situations of low signal-to-noise ratio (SNR). In this paper, we aim to investigate the problem of the joint detection and tracking (JDT) of a target by directly using the received signals of passive multi-static radar without feeding the signals to matched filters. To this end, a novel likelihood function is proposed exploiting the statistical properties of coherent processing between the reference and surveillance signals. With such a likelihood function, the particle Bernoulli filter is employed to perform direct JDT (DJDT) of the target. A remarkable feature of the proposed method is that it is able to achieve satisfactory performance when the SNR of received signals is low. Furthermore, the proposed method cannot only achieve the existence and kinematic state of the target, but also the time-varying SNR of each receiver, which serves as an important input for sensor adjustment. The performance of the proposed method is verified via simulations. [ABSTRACT FROM AUTHOR]

Radar automatic target recognition based on high-resolution range profile (HRRP) has become a research hotspot in recent years. The current works mainly focus on closed set recognition, where all the test samples are assigned to the training classes. However, radar may capture many unknown targets in practical applications, and most current methods are incapable of identifying the unknown targets as the 'unknown'. Therefore, open set recognition is proposed to solve this kind of recognition task. This paper analyzes the basic classification principle of both recognitions and makes sure that determining the closed classification boundary is the key to addressing open set recognition. To achieve this goal, this paper proposes a novel boundary detection algorithm based on the distribution balance property of k-nearest neighbor objects, which can be used to realize the identification of the known and unknown targets simultaneously by detecting the boundary of the known classes. Finally, extensive experiments based on measured HRRP data have demonstrated that the proposed algorithm is indeed helpful to greatly improve the open set performance by determining the closed classification boundary of the known classes. [ABSTRACT FROM AUTHOR]

For the problem of traditional passive radar target detection, the cross-correlation method is a popular solution. By processing the reference signal and the target echo signal coherently, the method is able to extract the target parameters, such as distance, velocity, and azimuth. However, the estimation performance is limited by the low signal-to-noise ratio. Therefore, a new cross-correlation algorithm for passive radar target detection is proposed in this paper. The echo signal received by the echo antenna and the reference signal received by the reference antenna are processed by differential operations to transform the single signal into multiple signals. Then, the differential reference signal is used to eliminate the signal phase in the differential echo signal. With the two-dimensional Fourier transform, the coherent accumulation of multiple signals is realized based on matched filtering of the single signal. This makes the secondary accumulation of the target signal coherent, while the interference part can only obtain incoherent secondary accumulation. In this way, the signal-to-noise ratio for target detection can be greatly improved. The paper presents the theoretical derivation of the algorithm and the quantitative analysis of its performance. The results of both simulated and measured data show that the detection performance is greatly improved compared with the traditional method. [ABSTRACT FROM AUTHOR]

Passive radars and multistatic passive radars in particular are gaining increasing attention. It is due to their effectiveness in detecting, tracking and identifying targets. Multistatic passive radars are used in remote sensing observations of the Earth's ionosphere, meteors, space junk, etc. Another application is the detection and tracking of manmade objects and vehicles in the atmosphere or on the surface of the Earth. The current paper discusses a hardware design approach applicable to the front-end of a multistatic passive radar receiver, namely a compound preselector. The preselector is a very important part of the radio receiver in cases where there are strong neighbouring signals in the radio band of operation. Such strong signals may overload the receiver or the low noise preamplifier and render the whole system dysfunctional. This article elaborates on a two-component preselector having very high rejection ratio to nearby emitters, still keeping the receiver simple and reliable. The authors have registered several utility models on innovations in multistatic passive radars at Patent Office of Republic of Bulgaria. [ABSTRACT FROM AUTHOR]

We analyze the coherence degree of the radar signals reflected from the passive-interference sources with different spectral characteristics determined by different wind velocities. The entropy of the distribution of eigenvalues of the interference correlation matrix is used as a quantitative measure of the coherence degree of interference. It is shown that the entropy value can be used for estimating the maximum coefficient of the passive-interference suppression. The dependences of the coefficient of the passive-interference suppression on the rms width of the interference spectrum for various values of the process sampling length and the wavelength are given. The coherence of an additive mixture of the signals, which are reflected from the interference and the target, is estimated. It is shown that the coherence measure of radar signals in the form of the entropy of the distribution of the signal over eigensubspaces of its correlation matrix allows one to estimate the maximum subinterference visibility. [ABSTRACT FROM AUTHOR]

Due to millimeter‐wave (MMW) has a strong ability to penetrate clothing, MMW holographic imaging technology can conduct a non‐contact inspection of the human body's surface. Therefore, it is of great significance to study the security inspection equipment and target recognition technology based on millimeter wave imaging. In this paper, an active and passive hybrid millimeter wave imaging target recognition method based on multi‐feature fusion is proposed, which improves the ability of the imaging system to identify different dangerous targets. First, active and passive millimeter wave imaging techniques are discussed, the reconstruction algorithm of active millimeter wave holographic imaging is derived in detail, and the measured optical photos and data of active and passive imaging are obtained. Secondly, the image preprocessing technology applied to active and passive millimeter wave imaging is studied, which can effectively highlight the target, eliminate background interference, and clearly describe the target contour. Then the methods of image feature extraction, data feature extraction and multi‐feature fusion are studied. On this basis, a multi‐feature fusion method based on weighted series fusion is proposed to obtain the fusion feature vector form of active and passive MMW imaging. Finally, this paper proposed the target recognition method applied to millimeter wave imaging, and concludes that the fusion feature vector is better than the original feature vector, which provides an idea for the fusion of active and passive millimeter wave imaging at the feature level. It also provides a theoretical basis for the application of security equipment. [ABSTRACT FROM AUTHOR]

The tracking of underwater targets represents a basic unit in underwater surveillance for offering effective search and rescue operations. However, the tracking of the target is a major constraint in finding the underwater target. This paper devises a model for underwater target tracking considering radar signals. Radar signals are subjected to the image reconstruction process to make them suitable for further processing. The gridding is the next step, which is utilized for generating the grids to focus on each region in a precise manner. The features like Ridgelet transform and Local Gabor XOR Pattern (LGXP) are produced. Data augmentation is the next process, which helps to reduce the size of data by performing resizing. At last, the tracking of targets is performed from each grid with a Deep neuro fuzzy network (DNFN). Here, DNFN training is performed with Exponential-Competitive Swarm Optimization (E-CSO), which is derived by integrating Exponential Weighted Moving Average (EWMA) and Competitive Swarm Optimization (CSO). The proposed E-CSO-based DNFN offered high efficiency with a maximal detection rate of 98.8%, minimal Mean square error of 0.012, and Mean Absolute Percentage Error (MAPE) of 17.883. [ABSTRACT FROM AUTHOR]

This paper presents the first successful drone detection results using a 5G network as a source of illumination in a passive radar system. Furthermore, a novel adaptive strategy for signal integration is shown. The proposed approach is based on the Rényi entropy. It allows one to select time frames with a densely allocated downlink channel both in the time and frequency domains. The resource allocation is strongly related to a network load and has a crucial influence on 5G-based passive radar range resolution and detection capabilities. The proposed technique was validated using simulated and real-life signals, confirming the possibility of detecting unmanned aerial vehicles (UAVs) in 5G-network-based passive radars. Moreover, the proposed methodology can be directly used in passive radar systems where the illuminating signal duration and bandwidth are content-dependent, and the radar resolution may vary significantly. [ABSTRACT FROM AUTHOR]

China's BeiDou navigation system has been completed and is currently in operation. As the newest constellation, BD-3 is composed of 30 satellite configurations. The application of BD-3 has improved rapidly, however, using the BD-3 signal as a signal resource in the global navigation satellite system (GNSS) passive radar domain to monitor deformation has not been proven to be feasible. The authors of his paper designed a BD-3 passive radar model and carried out a proof-of-concept experimental demonstration for deformation monitoring. As a result, a method based on the B3I signal as a signal resource for GNSS radar (GNSS-R) deformation monitoring is proven to be feasible. The experimental scenarios include laboratory simulation and proof of concept field displacement deformation. The test error reaches a value of 0.023 m for a simulation scenario, 0.0435 m for a field scene of deformation based on a target translation measurement, and 0.1011 m for a field scene of deformation based on a target rotation measurement. [ABSTRACT FROM AUTHOR]

In order to improve the signal-to-clutter-noise ratio (SCNR) of received signal, this letter studies the illuminator scheduling problem in the detection process with the aim of maximizing SCNR time series. In order to use the illuminator with long detection time as much as possible and get high SCNR in the detection process, we propose a neighbourhood search illuminator scheduling method. Firstly, we study the characteristics of radar cross-sections (RCS) of target and sea surface under different incident directions and polarizations, and analyse the regions of high target RCS and low sea surface RCS. Then the coordinate transformation method is used to get the scheduling sequence within the detection range. Based on the global scheduling sequence, the neighbouring frames are searched to obtain the illuminator sequence of the randomly appearing target. Simulation results show that the proposed method can effectively improve the received signal SCNR 15 dB on average. [ABSTRACT FROM AUTHOR]

The received signal of passive radar based on mobile communication signals contains direct and multipath interference (DMI) signals from multiple co-channel base stations (CCBS). The direct signal spatial spectrum of each CCBS should be obtained first to eliminate the co-channel interference. The performance of the traditional spatial spectrum estimation algorithms based on uniform linear array (ULA) is related to the number of array elements. In the complex co-channel interference environment, the array requires an ultra-large number of array elements and the spatial spectrum estimation resolution is poor. This paper proposes a method for estimating the direct signal spatial spectrum of the CCBS by fusing coprime array and compressive sensing. Firstly, an augmented virtual array signal is constructed by using the second-order statistics of the received signals of the coprime array and then the compressive sensing algorithm is used to estimate the spatial spectrum of the direct signal of the CCBS. The proposed method can achieve higher resolution and higher-degrees-of-freedom (DOFs) performance than traditional ULA by using the same number of array elements. The effectiveness of the proposed method is verified by numerical simulation analysis and experimental data. [ABSTRACT FROM AUTHOR]

The utilization of polarization information is a hot topic in the bistatic radar field. Bistatic full-polarization scattering dataset and polarization decomposition theories are urgently needed. We have established an indoor bistatic full-polarization scattering measurement system to investigate the target polarization scattering characteristics (PSCs), and a dataset is obtained. Due to its excellent performance, bistatic Huynen target parameters (BHTPs) are selected as the PSCs analysis tool. Then, a new BHTP extraction algorithm is presented, which directly extracts BHTPs from the measured Sinclair matrix. It is a state-of-the-art algorithm for BHTP extraction because of its easy implementation, robustness, and clear physical meanings. Experiment results demonstrate that the proposed algorithm can extract BHTPs accurately. [ABSTRACT FROM AUTHOR]

Classification becomes one of the important elements in the forward scatter radar (FSR) micro-sensors network. This classification performance is dependent on the target’s profile behaviour and the network’s surrounding; and one of the factors that cause the reduction of classification probability is the presence of ground clutter. As the volume of clutter increases, their masking effect becomes greater and may result in more significant errors in target classification. Hence, to reduce misclassification in the FSR sensor network, a new clutter reduction technique based on the ground clutter model is proposed. Simulated ground clutter is modeled based on the estimated signal to clutter ratio (SCR) of the received signal. The clutter effect is diminished by eliminating simulated like-clutter from the receiving signals. The result shows improvement in the classification accuracy, especially for the minimum value of the SCR and this new technique uses only one database which will shorten the processing time and reduce the overall database’s size. [ABSTRACT FROM AUTHOR]