Radar Research Articles

Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective

This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.

Frequency dependence of reflections on radar landmarks

Purpose. Reducing the dispersion of radar reflections from local objects, with multi-frequency sensing, to solve the problem of orientation by radar reflections from objects. Methodology. Reflections from local objects in the entire frequency range of the radar station (RS) at the same radio engineering position were measured by three independent radars of the same type on different days and at different antenna elevations. The deviation of the radar stations in the position did not exceed 500 meters. Coordinates (azimuth, range) of reflections of several separate local objects were allocated for each radar. The average values of reflections from local objects and their dispersion in the frequency range were calculated. Using various algorithms, individual frequencies were sampled and the reflected signals were averaged at these frequencies. The decrease in the dispersion of the reflected signal from the number of frequencies at which reflections were measured and from the algorithm for selecting these frequencies was investigated. Findings. Averaging the values of reflections from local objects for several frequencies leads to a decrease in dispersion and, as a result, to a more accurate correspondence of the reflected signal level to the geometric size of the local object. The variance decreases most rapidly for a small number of frequencies selected for averaging when selecting frequencies located in an interval of at least 1% relative to each other. Originality. To solve the problem of orientation based on radar reflections from local objects, it is necessary to identify the landmarks selected on a digital terrain model. Due to the fact that local objects (hills) are a collection of many reflectors falling into the allowed volume of the radar, with different levels of reflections and random phases, there may not be radar reflection from a local object at a certain frequency, or it may be very small. In order to unambiguously identify all landmarks, measurements must be carried out at several frequencies. The work has established how many frequencies measurements should be performed at and on what principle these frequencies should be selected. Practical value. The advent of digital terrain models made it possible to solve the problem of terrain orientation by comparing radar reflections from local objects with reflection models based on digital terrain maps. Radar reflection models use mathematical expectations of reflection values, unlike real reflections, which have random deviations in signal levels depending on the operating frequency. Reducing the variance of these deviations increases the accuracy of identifying characteristic local objects (landmarks) used to orient the radar in the absence of data from satellite navigation systems.

Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization

This paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed integer programming (MOMIP), which eliminates the need for additional constraints. To enhance the algorithm performance, integer variables and continuous ones are treated separately employing multiple velocity formulas. The velocity formulas for integer variables are modified using the sigmoid function and genetic operation, leading to the proposal of two MSRS deployment algorithms, namely MOPSO-Sigmoid and MOPSO-Gene. To evaluate the performance of the proposed algorithms, they are compared with two existing MOPSO-based algorithms. The first algorithm is the MSRS deployment algorithm for the non-connected deployment region that addresses the additional constraint problem model. The second algorithm is based on an existing conventional MOPSO algorithm and addresses the equivalent MOMIP problem model. A numerical study demonstrates that MOPSO-Sigmoid and MOPSO-Gene exhibit promising efficiency and effectiveness.

Vibration attenuation of a PCB enclosure in a radar system employing internal particle dampers

Vibration attenuation of a PCB enclosure in a radar system employing internal particle dampers

Flexible substrate based wideband polarization converter with extremely high polarization conversion ratio

In this article an anticipated reflection-mode cross-polarization converters (CPCs) are designed on a polydimethylsiloxane (PDMS) based flexible substrate operating in the GHz region. The proposed CPC metasurface comprises three layers where a flexible substrate is endwise between the resonating layer and ground layers. The converter can convert linear to circular and circular to cross-polarized waves at the frequency range of 5–8 GHz, as evidenced by the identical amplitude of Rxx and Ryx and a phase difference of 90°. More than 99 % of the polarization conversion ratio (PCR) was performed between the 6.75 and 9.07 GHz frequency range, while more than 80 % of the PCR was performed between the 6.0 and 10.73 GHz frequency range. The highest PCR values show nearly flawless energy conversion from an x-polarized incident wave to a y-polarized reflected wave. The PCR ratio of 80 % is achieved by this ultra-wideband linear-to-cross- and circular-to-cross-polarization converter. The anticipated converter's spectrum for all incidence angles between 0◦ and 60°. The PCR remained higher than 99 % over a wide incidence angle range to 60° without much performance degradation. The bending stability of the PDMS-based flexible substrate is also investigated up to 60°. The structure is fabricated on a flexible substrate, and the measured performance is compared with simulated results. Radar, antenna, and communications systems, as well as other cross-polarization converters, can utilize the transmitted wave's very high polarization purity result.

M2CS: A Microwave Measurement and Control System for Large-scale Superconducting Quantum Processors

Abstract As superconducting quantum computing continues to advance at an unprecedented pace, there is a compelling demand for the innovation of specialized electronic instruments that act as crucial conduits between quantum processors and host computers. Here, we introduce a Microwave Measurement and Control System (M2CS) dedicated for large-scale superconducting quantum processors. M2CS features a compact modular design that balances overall performance, scalability and flexibility. Electronic tests of M2CS show key metrics comparable to commercial instruments. Benchmark tests on transmon superconducting qubits further show qubit coherence and gate fidelities comparable to state-of-the-art results, confirming M2CS’s capability to meet the stringent requirements of quantum experiments run on intermediate-scale quantum processors. The compact and scalable nature of our design holds the potential to support over 1000 qubits after upgrade in stability and integration. The M2CS architecture may also be adopted to wider range of scenarios, including other quantum computing platforms such as trapped ions and silicon quantum dots, as well as more traditional applications like microwave kinetic inductance detectors, phased array radar systems.

Receiving Paths Improvement of Digital Phased Array Antennas Using Adaptive Dynamic Range

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models.

Research on Monitoring the Speed of Glacier Terminus Movement Based on the Time-Series Interferometry of a Ground-Based Radar System

The Tibetan Plateau (TP) is the largest glacier reserve outside the Antarctic and Arctic regions. Climate warming has affected the reserve of freshwater resources and led to frequent glacier disasters. However, due to its extreme environment of hypoxia and low pressure, it is extremely difficult to obtain data. Compared with other traditional monitoring methods such as makers and satellite remote sensing technology, Ground-Based (GB) radar systems have the advantages of convenient carrying and installation, sub-second level sampling, and sub-millimeter measurement accuracy. They can be used as an effective way to study the short-term rapid movement changes in glaciers. Based on a self-built GB radar system, monitoring experiments were conducted on two glacier termini on the TP. The movement speed of the Rongbuk glacier terminus on Mount Qomolangma was obtained through time-series interferometric measurement as 4.10 cm/day. When the altitude was about 5200 m, the glacier movement speed was 7.74 cm/day, indicating the spatial differences with altitude changes. And in another region, the movement speed of the Yangbulake glacier terminus on Mount Muztag Ata was 198.96 cm/day, indicating significant changes in glacier movement. The cross-validation of Sentinel-1 data during the same period proved the effectiveness of GB radar system interferometry in measuring glacier movement speed and also provided field validation data for remote sensing inversion.

Tsunami Early Warning Using High-Frequency Ocean Radar System in the Kii Channel, Japan

Abstract The tsunami of the 2011 Tohoku earthquake was monitored by the high-frequency (HF) ocean radar system in the Kii Channel, Japan. In this study, we conducted a retrospective analysis of tsunami early warning based on HF radar observations. First, we synthesized the surface current velocity using observation data of two radar land stations: Minato and Saikazaki. After vector synthesis, the time series of current velocity showed high consistency with the results of forward simulation. Then, we used the time series starting from 08:00 (UTC), 11 March 2011, as the input for data assimilation, reconstructing the tsunami wavefield in the Kii Channel. Comparison with actual observations at Kobe showed that our approach can accurately forecast coastal tsunami waveforms at least 50 min prior to tsunami arrival. The normalized prediction error decreased over time, indicating a progressively improving forecast accuracy. This is the first study on tsunami data assimilation using HF radar for the early warning of an actual seismogenic tsunami. We also discussed the limitations of the current HF radar system in the Kii Channel and made suggestions regarding the deployment of future radar land stations.

Comparative Study of GPR Acquisition Methods for Shallow Buried Object Detection

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features.

Reconfigurable Multifunctional Metasurfaces for Full-Space Electromagnetic Wave Front Control

In order to implement multiple electromagnetic (EM) wave front control, a reconfigurable multifunctional metasurface (RMM) has been investigated in this paper. It can meet the requirements for 6G communication systems. Considering the full-space working modes simultaneously, both reflection and transmission modes, the flexible transmission-reflection-integrated RMM with p-i-n diodes and anisotropic structures is proposed. By introducing a 45°-inclined H-shaped AS and grating-like micro-structure, the polarization conversion of linear to circular polarization (LP-to-CP) is achieved with good angular stability, in the transmission mode from top to bottom. Meanwhile, reflection beam patterns can be tuned by switching four p-i-n diodes to achieve a 1-bit reflection phase, which are embedded in the bottom of unit cells. To demonstrate the multiple reconfigurable abilities of RMMs to regulate EM waves, the RMMs working in polarization conversion mode, transmitted mode, reflected mode, and transmission-reflection-integrated mode are designed and simulated. Furthermore, by encoding two proper reflection sequences with 13×13 elements, reflection beam patterns with two beams and four beams can be achieved, respectively. The simulation results are consistent with the theoretical method. The suggested metasurface is helpful for radar and wireless communications because of its compact size, simple construction, angular stability, and multi-functionality.

A Neuromorphic Radar Sensor for Low-Power IoT Systems

As radar sensors become an integral component of Internet of Things (IoT) systems, the challenge of high power consumption poses a significant barrier, especially for battery-operated devices. This article introduces NeuroRadar, a groundbreaking solution that leverages a radar front-end capable of generating spike sequences, which can be efficiently processed by energy-saving Spiking Neural Networks (SNNs). We explore the innovative design and implementation of NeuroRadar, showcasing its effectiveness in applications like gesture recognition and human tracking. By achieving dramatically lower power consumption compared to traditional radar systems, NeuroRadar represents a new paradigm in energy-efficient IoT sensing.

Design and Implementation of Wind Speed-Based Radar Antenna Safety System Prototype

Air defense radar is a system that detects the presence of one or more air objects at a certain distance, altitude, and direction. One type of air defense radar used by the Indonesian National Armed Forces (TNI) is the Thomson TRS 2215D. An essential part of the radar is the rotating part of the radar antenna support called the antenna pivot. The rotation of the radar antenna must constantly be monitored and controlled for rotational stability at a speed of 6 Rotations per Minute (RPM) with a maximum wind speed of 120 km/h to prevent damage to the driving gear. Stormy weather with high wind speeds can cause the rotation speed of the radar antenna to be uncontrollable, which can cause damage. The solution offered in this study is to build a safety system that will lock the radar antenna automatically when the wind speed is detected to exceed tolerances and maintain the security of the radar antenna and its support system. The safety system was designed using an ESP32 Wi-Fi device equipped with an anemometer wind speed sensor, a Liquid Crystal Display (LCD) monitor, a Direct Current (DC) motor, and a Blynk Internet of Things (IoT) application. The test was conducted in a simulation using a multi-meter and oscilloscope measuring instrument. Testing the radar antenna's safety system prototype on a laboratory scale shows that the safety system can work as designed. The system can lock the radar antenna when the airflow is set at a speed of 54 km/h or 15 m/s, communication with the Blynk server works well, and the ESP32 device can transmit data at a maximum distance of 14 meters.

Design and development of low‐weight and high‐strength electromagnetic shielding nanocomposite in a microwave frequency range

AbstractIn this novel work, we fabricated the multiwalled carbon nanotubes (MWCNTs)/polypyrrole (PPy) nanocomposite by employing the novel ultrasonic dual mixing (UDM) method. Subsequently, the atomistics and structural characterizations of the fabricated MWCNT/PPy nanocomposite (PM) were performed using SEM, Raman, and XRD. Furthermore, we experimentally evaluated the mechanical properties of the fabricated PM by reinforcing the PPy matrix with MWCNTs at different wt.%. The experimental study of PM shows that the mechanical properties depend upon the amount and dispersion quality of MWCNTs in the PPy matrix. In order to validate the experimental outcomes, the conservative fully coupled finite element (FE) models were developed using the ANSYS Workbench and novel FEAST software. The electromagnetic interference (EMI) shielding effectiveness (SE) of developed PM was also calculated at different thicknesses of PM by taking the effect of SE due to absorption (SEA) and SE reflection (SER) in the microwave frequency range of the C‐band (5.37–8.2 GHz) into consideration. The total SE (SET), the cumulative sum of SEA and SER, is the strong function of the thickness of the PM. Additionally, our study's outcomes reveal that as the thickness of the PM increased, the SET also increased by dominating the SEA rather than the SER. The SET value of 75 dB was observed for a sample with a thickness of 3 mm, indicating a high level of shielding. Thus, material becomes a highly attractive option for achieving high‐performance EMI SE in the C‐band for satellite communications, Wi‐Fi devices, weather radar systems, surveillance, and cordless telephones. A complex permittivity and permeability were also studied using the Nicolson–Ross–Weir (NRW) method to understand the mechanism behind the EMI SE.Highlights Nanocomposite fabricated using the novel ultrasonic dual mixing method. FE models were developed to validate experimental results. The EC and EMI SE are improved with the increase in the sample thickness. Absorption, rather than reflection, dominated the shielding mechanism. SE was observed dB with good mechanical properties.

Anti-Rain Clutter Interference Method for Millimeter-Wave Radar Based on Convolutional Neural Network

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method’s effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions.

A novel millimetre‐wave 2D wide‐angle scanning phased array antenna based on decoupling surface

AbstractThis paper presents a novel millimetre‐wave (mmWave) 2D wide‐angle scanning (WAS) phased array antenna (PAA) based on decoupling surface (DS). The proposed element is a coaxial feeding stacked patch antenna structure that consists of an L‐shape coaxial probe, substrate integrated waveguide cavity, slot layer, radiating patch, DS and defective ground structure. The simulated impedance bandwidth for the element is 14.85% (26.18–30.34 GHz). The simulated result of the 2 × 2 array shows that the designed antenna based on DS achieves an isolation of ≥19 dB within the bandwidth. To validate the scanning capabilities of the proposed antenna, an 8 × 8 PAA prototype is fabricated and measured. The measured scanning range of the prototype achieves a 2D WAS range of ±56° at 27 GHz. The proposed antenna shows great potential in application scenarios, such as automotive radar and satellite communication due to its compact design and ability to achieve WAS in two dimensions.

Joint Prediction of Sea Clutter Amplitude Distribution Based on a One-Dimensional Convolutional Neural Network with Multi-Task Learning

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type of amplitude prediction lacks the necessary flexibility in practical applications. Therefore, based on the measured X-band radar sea clutter data from Yantai, China in 2022, this paper proposes a multi-task one-dimensional convolutional neural network (MT1DCNN) and designs a dedicated input feature set for the joint prediction of the type and parameters of sea clutter amplitude distribution. The results indicate that the MT1DCNN model achieves an F1 score of 97.4% for classifying sea clutter amplitude distribution types under HH polarization and a root-mean-square error (RMSE) of 0.746 for amplitude distribution parameter prediction. Under VV polarization, the F1 score is 96.74% and the RMSE is 1.071. By learning the associations between sea clutter amplitude distribution types and parameters, the model’s predictions become more accurate and reliable, providing significant technical support for maritime target detection.

Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm

Flint Hall Farm in Godstone, Surrey, UK, is situated adjacent to the London Orbital Motorway, or M25, and contains several landslide systems which pose a significant geohazard risk to this critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000; current in situ instrumentation includes inclinometers and piezoelectric sensors. Interferometric Synthetic Aperture Radar (InSAR) is an active remote sensing technique that can quantify millimetric rates of Earth surface and structural deformation, typically utilising satellite data, and is ideal for monitoring landslide movements. We have developed the hardware and software for an Unmanned Aerial Vehicle (UAV), or drone radar system, for improved operational flexibility and spatial–temporal resolutions in the InSAR data. The hardware payload includes an industrial-grade DJI drone, a high-performance Ettus Software Defined Radar (SDR), and custom Copper Clad Laminate (CCL) radar horn antennas. The software utilises Frequency Modulated Continuous Wave (FMCW) radar at 5.4 GHz for raw data collection and a Range Migration Algorithm (RMA) for focusing the data into a Single Look Complex (SLC) Synthetic Aperture Radar (SAR) image. We present the first SAR image acquired using the drone radar system at Flint Hall Farm, which provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope, such as corner reflectors and the in situ instrumentation, are visible as bright pixels, with their size and positioning as expected; the surrounding grass and vegetation appear as natural speckles. Drone SAR imaging is an emerging field of research, given the necessary and recent technological advancements in drones and SDR processing power; as such, this is a novel achievement, with few authors demonstrating similar systems. Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure.

Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance.

Using lightweight denoising network to suppress multiple barrage jamming in range‐Doppler domain

AbstractBarrage jamming technology poses a significant threat to radar detection functions, yet there is limited research on anti‐multiple barrage jamming using single‐channel radar systems. Addressing this gap, this paper proposes a framework for anti‐multiple barrage jamming based on range‐Doppler domain denoising. This framework first performs pulse compression and Doppler processing on the echo signal to enhance the signal‐to‐jamming ratio. The range‐Doppler spectrum data is then input into a deep denoising network to suppress multiple types of jamming. Additionally, the paper proposes a lightweight deep denoising network comprising a feature extraction module, a jamming suppression module, and a feature fusion module. The feature extraction module preliminarily extracts jamming features using multiple layers of depth‐wise and point‐wise convolution. The jamming suppression module removes noise at various resolutions through a lightweight encoding and decoding structure, effectively suppressing the barrage jamming signal. The feature fusion module uses convolution kernels with different sizes to merge the multiple features output by the jamming suppression module. Simulation results demonstrate that the proposed method effectively suppresses ten types of barrage jamming. Furthermore, a measured dataset is constructed to verify the method’s effectiveness.