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Abstract This article introduces a novel microstrip slot antenna array (SAA) configuration for radar applications. The proposed antenna is specifically designed for operation in the K-band, spanning from 23 to 24.3 GHz. The antenna structure comprises two substrates: the feed network and ground plane are on the bottom substrate, and the radiating slots are on the top layer of the first substrate. The incorporation of a unique grid feed configuration, featuring 50 Ohm center excitation for the first time, improves the feed mechanism of the microstrip SAA. This innovation contributes to achieving a compact size and high gain. To enhance the side lobe level, the design incorporates a substrate-integrated waveguide-backed cavity, which significantly reduces surface waves. The SAA consists of 25 radiating elements with a gain of 14 dBi. In the elevation and azimuth planes, the half-power beamwidths are measured at 12.1° and 69.1°, respectively. The proposed antenna array’s measured impedance bandwidth ranges from 23.15 to 24.75 GHz, guaranteeing a reflection coefficient (S11) of less than − 10 dB. The suggested antenna's applicability for automotive multi-input multi-output radar has been validated.

We propose a 10-GHz 2 2 phased-array radio frequency (RF) receiver with an 8-bit linear phase and 15-dB gain control range using 65-nm complementary metal-oxide-semiconductor technology. An 8 X 8 phased-array receiver module is implemented using 16 2 X 2 RF phased-array integrated circuits. The receiver chip has four single-to-differential low-noise amplifier and gaincontrolled phase-shifter (GCPS) channels, four channel combiners, and a 50-Ω driver. Using a novel complementary bias technique in a phase-shifting core circuit and an equivalent resistance-controlled resistor-inductor-capacitor load, the GCPS based on vector-sum structure increases the phase resolution with weighting-factor controllability, enabling the vector-sum phase-shifting circuit to require a low current and small area due to its small 1.2-V supply. The 2 X 2 phased-array RF receiver chip has a power gain of 21 dB per channel and a 5.7-dB maximum single-channel noise-figure gain. The chip shows 8-bit phase states with a 2.39 degree root mean-square (RMS) phase error and a 0.4-dB RMS gain error with a 15-dB gain control range for a 2.5 degree RMS phaseerror over the 10 to 10.5-GHz band.

Abstract Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.

Abstract Radar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness.

Abstract Beamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters’ outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam.

Abstract To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios.

Abstract The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction equations are continuous‐time and the update equations are discrete‐time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete‐time non‐linear filter problems, when prediction equations and update equations are discrete‐time. In order to assess the performance of the filters, the authors consider the non‐linear dynamics for a re‐entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re‐entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re‐entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins‐Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter.

Abstract Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short‐time Fourier transform time‐varying filtering, short‐time fractional Fourier transform time‐varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms.

Aiming at the quantitative evaluation requirements of radar second-order intermodulation false alarm (SIFA) effect, a radar SIFA effect model is established from the field-circuit coupling mechanism, and the parameter test method of the model is given. Taking a certain type of radar as the test object, the SIFA effect test is carried out by using the method of electromagnetic injection equivalent substitution irradiation. The results show that the tested radar will produce a SIFA signal higher than the selected sensitive level when the frequency difference of dual-frequency electromagnetic interference (EMI) is within 3 MHz and the frequency offset is within ± 200 MHz. Using the model parameters of the SIFA interference effect measured in the experiment, it is assumed that they do not change with the interference field strength. Combined with the SIFA interference field strength of the tested radar and the single frequency blocking critical interference field strength, the effect model can evaluate the degree of radar SIFA interference.

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

Abstract The Alps are a natural barrier for avian broad‐front migration in Central Europe. While most birds that approach the Alps are deflected and circumvent the mountains, some choose to make the crossing. Here, they are funnelled and channelled in valleys, leading to high bird densities. Many Alpine valleys are suitable locations for wind farms, potentially creating a conflict between wind energy production and bird conservation. Collisions can be reduced by temporarily shutting down wind turbines. This however requires timely coordination, either by locally monitoring migration intensity or by extrapolating and forecasting migratory fluxes from other sites. However, little is known about the timing and intensity of bird migration in valleys of the central Alps, especially during spring migration. This study presents a 2‐year quantification of avian migration across the Alps. We collected terrestrial radar data at three sites: two located in Alpine valleys and one in the lowland, close to the northern foothills of the Alps. We found high migration traffic rates (MTR) during both migration seasons in the Alpine valleys, with outstanding numbers of migrants during the spring season. The strong alignment of the flight directions with the main orientation of alpine valleys highlights the importance of valleys and the connected passes in channelling migratory fluxes through the Alps. However, extrapolating migration intensities and forecasting peak migration events for inner Alpine sites is difficult, likely due to how migratory patterns and activity are influenced by the complexity of the local topography and the associated dynamic wind and weather conditions. Instead, we call for year‐round on‐site monitoring of migration intensities and strategies tailored to the local context to reduce the risk of bird strikes at wind turbines in the Alps.

Abstract In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open‐set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi‐RNN). RBi‐RNN utilises input reconstruction and employs a joint training strategy incorporating cross‐entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter‐class latent representation distances while minimising intra‐class disparities. By incorporating an open‐set recognition method based on extreme value theory, RBi‐RNN adapts to open‐set scenarios. Simulation results demonstrate the superiority of RBi‐RNN over conventional models in both closed‐set and open‐set scenarios. In open‐set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses.

Aiming at the current situation of lack of environment and equipment in radar training, a radar operation training method based on the combination of virtual and real is proposed. Firstly, the connotation of the concept of virtual-real combination is analyzed. Secondly, the interaction methods of combining physical radar and virtual environment and virtual radar and physical environment are studied. Finally, combined with the specific training requirements, the application of real-real, real-virtual, virtual-real, and virtual-virtual radar interaction methods in radar training is analyzed in the same scenario.

Rain gauge networks provide direct precipitation measurements and have been widely used in hydrology, synoptic-scale meteorology, and climatology. However, rain gauge observations are subject to a variety of error sources, and quality control (QC) is required to ensure the reasonable use. In order to enhance the automatic detection ability of anomalies in data, the novel multi-source data quality control (NMQC) method is proposed for hourly rain gauge data. It employs a phased strategy to reduce the misjudgment risk caused by the uncertainty from radar and satellite remote-sensing measurements. NMQC is applied for the QC of hourly gauge data from more than 24,000 hydro-meteorological stations in the Yangtze River basin in 2020. The results show that its detection ratio of anomalous data is 1.73‰, only 1.73% of which are suspicious data needing to be confirmed by experts. Moreover, the distribution characteristics of anomaly data are consistent with the climatic characteristics of the study region as well as measurement and maintenance modes of rain gauges. Overall, NMQC has a strong ability to label anomaly data automatically, while identifying a lower proportion of suspicious data. It can greatly reduce manual intervention and shorten the impact time of anomaly data in the operational work. HIGHLIGHTS Quantitative and fusion application of radar and satellite data were addressed for the quality control (QC) of hourly rain gauge data.; Precipitation quality anomaly events were defined to improve the traceability of QC.; A phased QC strategy was adopted to reduce the misjudgment risk caused by the uncertainty of remote-sensing measurements.;

Abstract Since 2021, a new surge in discrete aurora detections at Mars has been observed by the Emirates Mars Ultraviolet Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) Hope Orbiter as EMUS started to regularly obtain synoptic auroral images with a high sensitivity. Here we report on a fortuitous conjunction between EMM and Mars Express (MEX) using far ultraviolet (FUV) imaging of discrete aurora by EMM EMUS, in situ measurements of suprathermal electrons by the MEX Analyzer of Space Plasma and Energetic Atoms Electron Spectrometer (ELS), and topside radar sounding of the nightside ionosphere by the MEX Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS). In this event, EMM EMUS imaged a clear discrete aurora signature around moderately strong crustal magnetic fields on the nightside near the dusk terminator, 11 min before which MEX MARSIS measured a prominent local enhancement of the peak electron density in the nightside ionosphere and MEX ELS observed an in situ enhancement of suprathermal electrons at the corresponding location. A remarkable geographic agreement is found between the enhancements of the aurora, ionosphere, and suprathermal electrons, suggesting that the enhanced ionization and auroral emission are caused concurrently by precipitating suprathermal electrons. Subsequent images indicate that the discrete aurora slightly changed its shape in 15 min and mostly disappeared in a few hours. The MEX MARSIS measurements of the auroral ionosphere display overlapping ionospheric and surface echoes indicative of horizontal gradients of the peak electron density. Analysis of the overlapping echoes implies that the auroral ionosphere and electron precipitation could be highly structured with horizontal spatial scales on the order of several tens of km. MEX MARSIS also observed a non-auroral ionospheric enhancement with a wider spatial extent than the local auroral enhancement, suggesting alternative sources of the enhanced nightside ionosphere such as plasma transport. The comparison between the ionospheric structures measured by MEX MARSIS, suprathermal electron flux measured by MEX ELS, and discrete auroral emission imaged by EMM EMUS underscores the complexity of the auroral and non-auroral nightside ionospheres. This motivates further investigations of their sources, transport, and connections to the magnetotail dynamics of Mars.

The data fusion of radar and automatic dependent surveillance-broadcast(ADS-B) is the effective method to surveille the 'black flights' and flying birds. However, the tracking performance of the two sensors has large differences and is easy to be fluctuated, which will bring a decline in fusion accuracy. A data fusion method of radar and ADS-B based on track quality assessment is proposed. Firstly, the effects of local track accuracy, data update times and sensor measurement errors on local track quality on corresponding assessment factors are analyzed and quantified. And then, these assessment factors are combined to calculate the quality weighting factors of the local track, and the asynchronous track fusion processing of radar and ADS-B is completed based on the distributed fusion structure. Finally, the feasibility and effectiveness of the proposed method is verified with simulation experiment and application. The results show that the proposed fusion method can effectively improve the fusion accuracy, and the tracking errors are better than the traditional algorithms when the sensor tracking performance fluctuates.

Equivalent-time sampling can improve measurement or sensing systems because it enables a broader frequency band and higher delay resolution for periodic signals with lower sampling rates than a Nyquist receiver. Meanwhile, a digital down-conversion (DDC) technique can be implemented using a straightforward radio frequency (RF) circuit. It avoids timing skew and in-phase/quadrature gain imbalance instead of requiring a high-speed analog-to-digital converter to sample an intermediate frequency (IF) signal. Therefore, when equivalent-time sampling and DDC techniques are combined, a significant synergy can be achieved. This study provides a parameter design methodology for optimal equivalent-time sampling using DDC.

Abstract The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications.

Abstract Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced.

Abstract Mars' polar ice deposits are thought to preserve a record of climate throughout their evolution. In addition to the large north polar layered deposits (NPLD) at Mars' north pole, smaller ice deposits are preserved in craters nearby. These outlying deposits were potentially formed by the same mechanisms that drive NPLD formation, or may represent more local mechanisms. Distinguishing between these possibilities would help elucidate the spatial homogeneity of Martian climate processes. Here, we analyzed SHARAD radar depth profiles from 34 locations across the NPLD and 5 locations within the Korolev crater ice deposit using Fourier transform analysis and dynamic time warping to quantitatively assess the similarity between the internal layered stratigraphy of the two deposits. We identify broad stratigraphic similarities between the Korolev deposit and the NPLD, suggesting they likely formed due to the same climate forcing mechanism, with local variability also observed across the NPLD.

Abstract Conflict game theory is employed to analyze the countermeasure relationship between radar systems and jammers. To address this issue, a novel utility function and a Conflict Power Allocation Game (CPAG) algorithm are introduced for determining the Nash Equilibrium (NE) solution of the game. The utility function enables the calculation of optimal power response functions for both the radar system and the jammer, with the NE solution of the CPAG algorithm obtained through numerical computation. Simulation results demonstrate the convergence of the proposed CPAG algorithm to the NE solution, validating its efficacy. Finally, an analysis of the relationship between the utility function and pricing factors is conducted based on the proposed CPAG algorithm and simulation outcomes.

Abstract In this letter, a novel algorithm is proposed to design symbol‐level waveform for dual‐functional radar‐communication (DFRC) systems with low range sidelobe. Different from current schemes design waveform at the block level, a phase response constraint is introduced at the symbol level to provide an additional degree of freedom to decrease the range sidelobe in the pulse compression procedure, which is highly desired in radar systems. In particular, the phase response at the target direction is constrained to be similar to the given reference phase, and the matching error between the designed beampattern and the desired one is minimized subject to the constant modulus constraint. Furthermore, to guarantee the quality of service for communication, constructive interference is exploited at the symbol level for each communication user. An alternating direction method of multipliers algorithm is also proposed to solve the resulting nonconvex optimization problem with tractable subproblems solved sufficiently by the manifold optimization and standard quadratic problem. Numerical simulation results demonstrate the performance of the proposed method.

Introduction and GoalIn recent years, problems caused by human interference in the Salmas Plain, such as an increase in the irrational use of surface water resources, an increase in the process of groundwater discharge, and a change in land use have caused pressure on the groundwater resources of the region; hence, land subsidence is at least a natural response to the decrease in groundwater level. Therefore, it is important to study and identify subsidence areas in the region.Materials and MethodsIn this research, groundwater information and Sentinel 1 radar images were used. Groundwater information was obtained from the regional water company of West Azarbaijan province. In the present research, the DInSAR algorithm was used to measure the ground surface displacement, and the phase difference of the SAR signals was determined using the repeated-pass interference methods. Finally, by using the manual of differential radar interferometry with synthetic aperture and 52 even numbers of Sentinel 1 image, the subsidence in the Salmas Plain was determined between 2014 and 2015 and 2018 and 2019. Validation of the radar interferometry method using Qareqeshlaq Geodynamic station data, as well as groundwater level changes, underground water drop and field observations was conducted.Results and DiscussionThe results of the subsidence study using the DInSAR method showed that the average annual displacement rate is about 5 cm. Investigation of groundwater changes also indicated a decreasing trend in most of the wells. The wells located in the eastern and southeastern regions (e.g., Kangarlu, Qareqeshlaq, Yushanlu, etc.) have the highest amount of groundwater drop. In the study period (2014 to 2019), where the wells with an increase in groundwater level were affected by their proximity to the Zola dam reservoir and the water supply channels. The wells of Qezeljeh, East Qareqeshlaq, and Maidan Doab have the highest water level reductions, but they do not have sufficient water supply from the dam. Mahlam, West Tazeshahr, and Ian wells, which are in the western part of the plain and close to the dam lake, respectively, have a suitable increase. Checking the subsidence maps of the Salmas plain showed that most of the subsidence is concentrated in the southern areas, especially in the southeast of the plain, in comparison to other parts of the plain the depth of alluvium is also greater in these areas. This subsidence trend continued from 2014 to 2019 and this trend is also prevalent in the final map. During this period, the average water level changes in the Salmas plain aquifer had a downward trend, which was in harmony with land subsidence. By reviewing the groundwater drop map, it was found that the areas with the highest drop coincided with the subsidence areas. More drop in underground water indicated more water extraction. Therefore, the discharge of underground water in the region has caused subsidence.Conclusion and SuggestionsBefore adopting any operational plan and policy for implementation, it is the most important task to identify the exact location of land subsidence in any area, in particular residential areas. Sentinel1 radar images are sufficiently capable of solving this problem at all stages; however, a longer observation time would be more useful for decision making and implementation. Land subsidence in the Salmas plain is fully confirmed by the change of the underground water level and field visits. According to the DInSAR approach, the average displacement rate due to subsidence in the region was 5 cm, which can be fully verified in terms of observations of changes in the groundwater level. The piezometric data show a reduction of in the water level in most wells compared to the past, which depicts the occurrence of excessive extraction of groundwater. In addition, field surveys have been conducted in different areas of the Salmas Plain and the subsidence areas have been recorded. The process of subsidence in the studied area is ongoing, and the attention and care of the relevant authorities is needed to reduce the damage caused by this phenomenon and adjust the possible damages. It is recommended that people should be aware of the consequences of excessive harvesting and recommend alternative crops with a low annual demand for water and early yields.

Abstract Unmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro‐Doppler information from radars. Classifiers capable of exploiting differences in micro‐Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro‐Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro‐Doppler signatures. This is followed by the description of a purpose‐built validation radar that has been developed specifically to gather high‐fidelity experimental drone micro‐Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro‐Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro‐Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro‐Doppler signatures for training classification algorithms.

Abstract For the integration detection of near space hypersonic weak targets by high pulse repetition frequency (PRF) radar, a novel method named Multi‐Hypothesis Fuzzy‐Matching Radon transform (MHFM‐RT) is proposed for the near space hypersonic target detection and tracking. For remote hypersonic target detection, to avoid range ambiguity, current radars always use a low PRF mode, which limit the number of pulse accumulations. Using the high PRF mode, the fuzzy folding will appear in the target range measurements when target trajectory crosses range fuzzy intervals. Therefore, there is a contradiction between range ambiguity and energy accumulation. The proposed method is used to match the fuzzy measurements, so as to realise the correct integration in the condition of range ambiguity. Firstly, considering the need of range ambiguity resolution, the mode of staggered PRF is used. Secondly, the first frame measurements are periodically extended for multiple‐fuzzy hypothesis. Finally, the weak target track is accumulated in MHFM‐RT domain, and the signal integration and ambiguity resolution can be realised simultaneously. The proposed method expands the Variable‐Diameter‐Arc‐Helix Radon transform (VDAH‐RT) method to fuzzy folding conditions. Compared with the existing methods, for 7‐scan measurements non‐coherent integration, the detection sensitivity of the proposed method is about 0.5–1 dB higher than that of the IMM hybrid filter algorithm, and about 1 dB higher than that of the RHT‐TBD approach, and it needs less storage space and has higher detection probability.

Abstract With the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed.

In this paper, an attempt is made to design an antenna to be used in different radar and telecommunication applications. Paying attention to antenna radiation is on the agenda And studies have been done on antenna radiation as well as their radiation pattern.Considering that the feature of the pattern with the vane feature is used in antennas with radar applications, it is also used in various telecommunication applications. In this article, an attempt has been made to increase the antenna's fan beam properties The designed antenna has two radiation elements And in the 3.5 GHz frequency band, it consists of a two-layer structure. The radiation elements are placed in the upper layer and the feeding network is located in the lower layer. The built dual antenna has an impedance bandwidth of 3.8-3.4 GHz.The maximum gain Hz is 6/76 dBi and the half power beam width is 44˚ and 104˚ in H- and E- planes.respectively which shows that this is fan beam antenna. The overall dimensions of the antenna are 105 × 32/3 ×3/708 mm square. The provided antenna is recommended for use in radar systems and 5G systems in the 3/5 GHz frequency band. According to the laboratory test of this antenna, it can be said that the theoretical and practicical results are in good agreement...

In the ground attack mission, the attack plan needs to be formulated and distributed to the attack aircraft based on the threat source intelligence data obtained in advance with the help of the tactical mission planning system. The passive radar reconnaissance and direction finding system can calculate the location information of threat sources and provide it to the task planning system by processing the received signal. When the enemy radar is a frequency diverse array system rather than a phased array system, the bending characteristics of the beam of the frequency diverse array radar can deceive the passive direction finding system. On the basis of analyzing the basic structure of frequency diverse array radar, this article compares the phase plane characteristics of phased array and frequency diverse array, and takes the interferometer direction finding system as an example to study the deception effect of frequency diverse array radar compared to phase method direction finding. The deception simulation results of several nonlinear FDA arrays show that the virtual transmitter intersection coordinate deviation of log-FDA is greater than that of sin-FDA. When the x-axis coordinate of the interferometer is xG=300 km, the virtual transmitter intersection coordinates gradually approach the actual position of the FDA array as the y-axis coordinate of the interferometer increases. The intersection offset is log-FDA>sin -FDA>reciprocal-FDA.

Abstract Building on previous radar‐based human activity recognition (HAR), we expand the micro‐Doppler signature to 6 domains and exploit each domain with a set of handcrafted features derived from the literature and our patents. An adaptive thresholding method to isolate the region of interest is employed, which is then applied in other domains. To reduce the computational burden and accelerate the convergence to an optimal solution for classification accuracy, a holistic approach to HAR optimisation is proposed using a surrogate model‐assisted differential evolutionary algorithm (SADEA‐I) to jointly optimise signal processing, adaptive thresholding and classification parameters for HAR. Two distinct classification models are evaluated with holistic optimisation: SADEA‐I with support vector machine classifiers (SVM) and SADEA‐I with AlexNet. They achieve an accuracy of 89.41% and 93.54%, respectively. This is an improvement of ∼11.3% for SVM and ∼2.7% for AlexNet when compared to the performance without SADEA‐I. The effectiveness of our holistic approach is validated using the University of Glasgow human radar signatures dataset. This proof of concept is significant for dimensionality reduction and computational efficiency when facing a multiplication of radar representation domains/feature spaces and transmitting/receiving channels that could be individually tuned in modern radar systems.

Abstract Radar‐based human activity recognition is considered as a competitive solution for the elderly care health monitoring problem, compared to alternative techniques such as cameras and wearable devices. However, raw radar signals are often contaminated with noise, clutter, and other artifacts that significantly impact recognition performance, which highlights the importance of prepossessing techniques that enhance radar data quality and improve classification model accuracy. In this study, two different human activity classification models incorporated with pre‐processing techniques have been proposed. The authors introduce wavelet denoising methods into a cyclostationarity‐based classification model, resulting in a substantial improvement in classification accuracy. To address the limitations of conventional pre‐processing techniques, a deep neural network model called Double Phase Cascaded Denoising and Classification Network (DPDCNet) is proposed, which performs end‐to‐end signal‐level classification and achieves state‐of‐the‐art accuracy. The proposed models significantly reduce false detections and would enable robust activity monitoring for older individuals with radar signals, thereby bringing the system closer to a practical implementation for deployment.

Abstract Millimetre‐wave radar has been widely used in health monitoring and human activity recognition owing to its improved range resolution and operation in a variety of environmental conditions. With the MIMO antenna array, 4D radar is increasingly employed in autonomous driving, while its application in assisted living is recent and therefore the value added compared to the increase in signal processing and hardware requirements is still an open question. A model for 4D Time‐division multiplexing (TDM) multiple‐input‐multiple‐output (MIMO) frequency‐modulated Continuous wave radar is established using the human activities from the HDM05 motion capture dataset. The simulator produces an end‐to‐end simulation, including four human motions (jumping Jack, kick, punch, and walk), signal time of flight, noise, MIMO signal processing, and classification. Different pre‐processing and point cloud‐based methods are compared to obtain an average classification accuracy of 90% with PointNet. This study simulates a specific 4D TDM MIMO radar configuration to benchmark signal pre‐processing algorithms, which can also assist other researchers to generate range‐Doppler‐time (range‐Doppler time) point cloud data sets for human activities testing different radar configurations, array configurations, and activities saving valuable time in human resources and hardware development before prototyping to assess expected performances.

With the increased carrier frequencies envisioned for future radar and communication systems, transmissions in the near-field propagation region have raised an increased interest. In order to evaluate the performance of such systems, accurate propagation models are required to predict efficiently the received powers at each node, and draw correct conclusions. In this paper, we develop new radar propagation models based on ElectroMagnetic (EM) theory, helping to clarify in which cases popular models from the literature, namely the radar equation and the Geometrical Optics (GO) approximation, are valid. The target is modelled as a flat square or curved rectangular plate, enabling to compute analytically the scattered EM fields, and evaluate accurate Radar Cross-Sections (RCS). With a flat plate, the novel model directly highlights the link between the radar equation and the GO approximation, used e.g. in ray-tracing applications. Radar cross-section measurements are then performed in order to validate the models with multiple target geometries, e.g. square plates, sphere and cylinder. Finally, the impact of such modelling is evaluated by considering popular automotive scenarios, within the stochastic geometry framework.

In China's Tianwen-2 mission, a dual-frequency radar will be carried out to detect the asteroid 2016 HO3. The dual-frequency radar can acquire the information of the asteroid's surface and internal structure. There are few studies about the radar detection of the asteroid 2016 HO3 and it is unclear what information can be acquired from the radar images. In this article, the radar images of the asteroid under nadir and oblique observations are simulated with the two-scale model of the Kirchhoff approximation/small-perturbation method. The radar images at 900 and 150 MHz are simulated to study the backscattering from the asteroid's surface and the detectability of the internal structure in the asteroid. The influences of the asteroid characteristics, such as the shape, surface roughness, internally embedded object, and the loss tangent of the media, on the radar images and the detectability of internal structure are discussed under nadir and oblique observations. The simulations provide a reference for future data analysis in the Tianwen-2 mission.

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults’ mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations – in particular, the micro-Doppler signature and skeletal point estimates – is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.

Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.

Monopulse methods have been successfully used in radar to determine the direction of arrival of a reflected signal, giving good results when a single isolated target is involved. However, when two nearby targets need to be detected and their angular position determined, this method fails. We show that it is possible to train and use a neural network to recognize two nearby targets. This neural network is then used to determine the angle of arrival of the reflected signal with sufficient accuracy, even when the distance of the targets is less than the beamwidth. We show the results on an eight-beam antenna model by measuring the elevation using an amplitude monopulse and neural network method. While using monopulse, we can reliably discriminate and determine the angular position of two targets only when their distance is larger than one beamwidth. The neural network method works reliably even for targets at distances much smaller.

The passive and active L-band system (PALS) is an airborne microwave instrument suite that combines a separate radar and radiometer airborne instrument with a common antenna. PALS has been used for salinity, wind speed, and soil moisture (SM) measurements, which is critical for calibration and validation of the SM active passive satellite brightness temperature measurements. The combined 1.413 GHz radiometer and 1.26 GHz radar measurements can provide high-accuracy SM measurements over vegetated areas. The new upgraded version of the PALS instrument employs a novel single-chain receiver topology and combines the radar and radiometer instruments. The reduced-noise single-chain receiver PALS instrument was deployed on a DC-3 aircraft for SM measurement in the summer of 2019.

This study introduces the novel communication topology, namely RadRCom, integrating radar and relay-assisted communication systems for single antenna configuration as a proof of concept. While simultaneous radar and communication operations within the same spectrum gain momentum, our work advances this concept by incorporating relay assistance, particularly crucial in applications like vehicle-to-anything (V2X) communication. The inclusion of relays significantly enhances communication system performance, addressing challenges such as interference management between radar, relay, and communication nodes. This topology attracts three design challenges such as optimal radar waveform, relay parameters and communication system parameters. However, the key bottlenecks are the interference from radar to the relay and communication receiver and similarly the one from communication transmitter and relay node to the radar. Therefore, the work addresses these challenges simultaneously meeting the quality of service. Our proposed RadRCom system optimizes radar waveform and relay parameters to improve signal-to-interference noise ratio (SINR) at the radar and mean square error (MSE) of data transmission. We introduce two frameworks for parameter design, i.e, radar-centric and relay-centric ones. We take a sub-optimal iterative approach to address the computational complexity. Numerical simulations are performed to evaluate the performances of the proposed RadRCom system with the proposed algorithms.

As an increasing number of people suffer from sleep disorders, such as insomnia or sleep apnea, sleep monitoring and management using consumer devices have gained increasing attention from research communities. As sleep quality is closely related to sleep structure based on hypnograms, the classification of sleep stages over the course of the night is important for accurate sleep monitoring. We present sleep stage classification using a smartphone equipped with ultra-wideband (UWB) radar. We focused on the development of easily accessible sleep monitoring system for the general population by placing the smartphone on a table near a bed, which is commonly used during sleep. We collected 509 nights of UWB radar and nocturnal in-laboratory polysomnography (PSG) data from various participants, including patients with apnea, using a customized Samsung Galaxy smartphone with a UWB radar chip placed on a table near the bed. A combination of 1D convolutional neural network and transformer architecture was proposed in this study, and a domain adaptation technique was applied to train the model with both large-scale respiratory signals from open database PSGs and UWB radar data to boost the performance by overcoming the lack of UWB radar data. With 5-fold validation, an epoch-by-epoch comparison between the predicted and expert-annotated four sleep stages (Wake, REM sleep, light sleep, and deep sleep) resulted in 0.76 of accuracy and 0.64 of Cohen’s kappa. This study demonstrated that sleep stages can be monitored with substantial accuracy by simply placing a smartphone on a bedtable, making it highly usable and reliable in real use cases.

In recent years, the Poisson multi-Bernoulli mixture (PMBM) filter has been established among the state-of-the art methods in target tracking. We present a method for including target-provided measurements in said filter, both when using it to track extended objects and point targets. We use messages from the Automatic identification system as an example of target-provided measurements, and radar and LiDAR as examples of exteroceptive sensors. In the point target case, we utilize several different kinematic models in parallel through the interacting multiple models framework, and compare the presented method to several common trackers and other PMBM filter configurations. The results show that our method outperforms similar methods when target-provided measurements are available. The point target variant is also shown to work in a closed-loop collision avoidance experiment in a maritime environment, demonstrating its feasibility for use in real-world applications. For the extended object tracking case, we expand upon the Gaussian process PMBM filter. The extended object method is evaluated on both simulated and experimental data, and is shown to improve the tracking performance when including target-provided measurements in comparison to when it only uses exteroceptive measurements.

For the problem of power allocation between a distributed multistatic radar network and a smart jammer, the application of non-cooperative game theory is employed to address the issue in this paper. Consequently, three scenarios of power allocation games are examined. The first two game scenarios, characterized by information asymmetry, are categorized under the Stackelberg game framework, while the final scenario, with information symmetry, is classified as a non-cooperative game. Through the power allocation analyses of the three game scenarios, it is observed that both the radar system and the jammer possess a first-mover advantage. Additionally, the existence and uniqueness of the Nash equilibrium in the games are demonstrated. Based on the best response strategies within the games, three corresponding power allocation game algorithms are proposed. Ultimately, the convergence and performance comparison of the three power allocation game algorithms are validated through simulation experiments.

This paper develops a novel stochastic geometry model to investigate design tradeoffs in a large-scale radar-aided millimeter-wave cellular network to eliminate the beam training overhead. In the proposed system, each base station comprises two sub-systems: the sensing sub-system, where radar localizes users, and the communication sub-system, where directional antennas serve the detected users. Both sub-systems operate simultaneously to eliminate the beam training overhead and reduce misalignment errors. The system is modeled under realistic fading conditions with scatterers and interferers in the environment. System parameters, such as the density of mobile users and undesired clutter, radar cross-section fluctuations, radar search duration, antenna directivity, and bandwidth are considered in the analytical model. Two scenarios are studied; the first considers antenna misalignment for the communication sub-system, where error magnitude depends on the radar sub-system accuracy. The other scenario considers perfect alignment for benchmarking. It is demonstrated that the radar search duration, which controls the radar antenna beamwidth, is a crucial parameter for optimizing the system throughput. Moreover, the beamwidth of the communication antenna exhibits a tradeoff in which a narrow beam yields higher gain and reduces interference but increases the misalignment error impact. The results demonstrate that if the beamwidths of both sub-systems are carefully chosen, the proposed system can eradicate the beam training overhead without compromising the system’s performance, in addition to significantly enhancing the average total system throughput.

In this paper, we propose an S/X-band shared-aperture array antenna with a mutual complementary design to improve the electromagnetic (EM) transparent characteristics. A unit-cell of the proposed antenna includes one dual-loop element for the S-band and $3\times 3$ dual-loop elements for the X-band. To configure the shared-aperture structure in a limited space, the S-band element is stacked on top of the X-band elements. To solve the practical engineering problems of the shared-aperture antennas, novel design techniques such as using a mutual complementary structure, a coupling compensation array, an interface layer, and an antenna modularization are employed. To verify the antenna feasibility, the fabricated unit-cell extends into a $4\times 4$ unit-cell array. The fractional bandwidth of the reflection coefficients for the proposed array are 14.7% and 15% in the S- and X-bands, respectively. In the S-band, as the steering direction of the main beam increases from 0° to 45°, the maximum gain decreases from 14.6 dBi to 11.8 dBi. In the X-band under the same conditions, the maximum gain varies from 26.6 dBi to 25.3 dBi.

This article aimed to monitor vegetation using C-band radar data at a subdaily time step. To this end, radar measurements using tower-mounted antennas with a 15-min time step, along with physiology-related information (sapflow and micrometric dendrometry), were acquired quasi-continuously from March 2020 to December 2021 in an olive orchard located near Marrakech, Morocco. The article focused on temporal coherence, whose clear diurnal cycle (highest at night and lowest at the end of the afternoon) had been highlighted over tropical and boreal forests in previous studies. The results showed that coherence was highly sensitive to: wind-induced movement of scatterers, since coherence was lowest when wind speed was highest in late afternoon, and vegetation activity, especially its water dynamics, since the morning coherence drop coincided with the onset of sapflow and the daily evapotranspiration cycle, as well as the good agreement between the temporal drop rate of coherence and the daily residual variation in trunk circumference (i.e., deviation from long-term trend). Finally, coherence remained high for temporal baselines of several days, showing that sentinel-1 data (when both satellites are operational) may be well suited for such studies, especially with acquisitions made during morning passes, when wind speed is low. These results open perspectives for monitoring tree crop physiology using high-revisit-time radar observations.

Abstract Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter.

Abstract 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 The millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K‐band) and 77 GHz (W‐band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported.

Abstract 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 The presence and importance of small drones (unmanned aerial vehicles) are increasing. With traditional radar detection the target radar cross section and its statistical properties are needed in order to design the detector and to assess detection performance. The authors make a statistical analysis of the radar cross section of two small fixed‐wing drones using measured data from typical flights. Using data from a typical flight instead of measurements or electromagnetic calculations as a function of aspect angle is a convenient method. Work results of the authors in mean values, medians and standard deviations, in analyses of common target probability distributions and in decorrelation times for the radar cross section, are necessary for design and assessment of the detection performance. The authors suggest a new target probability distribution which agrees better with the measured data than the others tested. The authors also note significant differences between the drones.