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Ground-based synthetic aperture radars (GBSAR) are popular instruments widely used for the monitoring of infrastructures. One of the main problems of ground-based interferometric radars is the elevation ambiguity. Multiple-input multiple-output (MIMO) arrays could solve this problem. This work proposes a study on possible MIMO configurations to achieve elevation resolution in ground-based radar measurements. Specifically, two array configurations are compared: a random sparse array suitable for the compressive sensing technique, and a non-uniform array. The two solutions are compared by means of simulations and experimental tests. An ad hoc system has been developed to jointly test the two configurations, and results obtained in a controlled and real urban scenario are shown. It is found that both systems are able to solve elevation ambiguity. The non-uniform array seems to achieve good performance in a general scenario, while the CS processing can outperform the other only after optimization, depending on the specific scenario and application.

A Ka-band dual-polarization magnetoelectric (ME) dipole antenna array based on three-dimensional (3D)-printed technology with low sidelobe level (SLL) is proposed in this paper. The metal posts and cross-slots are explored to build a novel subarray with 2 × 2-unit ME-dipoles as the basic element. Creatively, a square waveguide to cross-slots transition with a pyramid horn cavity structure is investigated to feed the ME-dipole subarray. Furthermore, two types of power-tapering corporate-feed networks with laminated structures are used to design an 8 × 8-unit low-SLL array. The fabricated array has a relative bandwidth (VSWR < 2) of 14.3% and 17.1%, with a realized gain higher than 25.8 dBi and 26.1 dBi for the H-pol. and V-pol., respectively. The maximum radiation efficiency for both arrays is 73.2%. The measured first sidelobe levels are less than –17.5 dB for both polarizations. With competitive performance and low fabrication cost, the proposed dual-polarization ME-dipole antenna array would be valuable for polarization-agile radar and communication systems.

Respiration rate monitoring using ultra-wideband (UWB) radar is preferred because it provides contactless measurement without restricting the person’s privacy. This study considers a novel non-contact-based solution using a single-input multiple-output (SIMO) UWB impulse radar. In the proposed system, the collected radar data are converted to several narrow-band signals using the generalized Goertzel algorithm (GGA), which are used as the input of the designed phased arrays for position estimation. In this context, we introduce the incoherent signal subspace methods (ISSM) for the direction of arrivals (DOAs) and distance evaluation. Meanwhile, a beam focusing approach is used to determine each individual and estimate their breathing rate automatically based on a linearly constrained minimum variance (LCMV) beamformer. The experimental results prove that the proposed algorithm can achieve high estimation accuracy in a variety of test environments, with an error of 2%, 5%, and 2% for DOA, distance, and respiration rate, respectively.

Ground penetrating radar (GPR) systems are sensors that are able to acquire underground images by scanning the surface of the soil/pavement under investigation. Usually, a GPR system records its own position along the scan line, using a mechanical odometer, i.e., a rolling wheel in contact with the ground. This simple and cheap solution can be ineffective on uneven terrains. In this paper, a positioning system based on an interferometric radar is presented. This kind of radar is able to detect small displacements of the targets in its field of view. Such a capability was used to track the GPR position along a line. The system was validated with simulations and tested in a realistic experimental scenario.

In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is [0.0245 m0.0224 m0.0039 rad]T and the average localization errors using the conventional EKF are [1.0405 m2.2700 m0.1782 rad]T, [0.4963 m0.3482 m0.0254 rad]T and [0.2774 m0.3897 m0.0353 rad]T, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

As an autonomous system, an anti-radiation loitering munition (LM) experiences uncertainty in both a priori and sensed information during loitering because it is difficult to accurately know target radar information in advance, and the sensing performance of the seeker is affected by disturbance and errors. If, as it does in the state of the art, uncertainties are ignored and the LM travels its planned route, its battle effectiveness will be severely restricted. To tackle this problem, this paper studies the method of autonomous planning and control of loitering routes using limited a priori information of target radar and real-time sensing results. We establish a motion and sensing model based on the characteristics of anti-radiation LMs and use particle filtering to iteratively infer the target radar information. Based on model predictive control, we select a loitering path to minimize the uncertainty of the target information, so as to achieve trajectory planning control that is conducive to the acquisition of target radar information. Simulation results show that the proposed method can effectively complete the autonomous trajectory planning and control of anti-radiation LMs under uncertain conditions.

Multi-target tracking (MTT) generally requires either a network of Doppler radar receivers distributed at different locations or a phased array radar. The targets moving with small/no radial velocity or angular velocity only cannot be detected and localized completely by deploying Doppler radar without antenna arrays or multiple receivers. To resolve this issue, we present a new MTT algorithm based on 2-D velocity measurements, namely, radial and angular velocities, using dual-frequency interferometric radar. The contributions of the proposed research are twofold: First, we introduce the mathematical model and implementation of the proposed algorithm by explicitly establishing the relationship between 2-D velocity measurements and kinematic state of the target in terms of Cartesian coordinates. Based on 2-D velocity measurement function, the proposed MTT algorithm comprises the following steps: (i) data association using global nearest neighbor (GNN) method (ii) target state estimation using interacting multiple model (IMM) estimator combined with square-root cubature Kalman filter (SCKF) (iii) track management using rule-based M/N logic. Second, performance of the proposed algorithm is evaluated in terms of tracking accuracy, computational complexity and IMM mean model probabilities. Simulation results for different scenarios with multiple targets moving in different tracks have been presented to verify the effectiveness of the proposed algorithm.

Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generally used as vehicle sensors, each of which has its own characteristics. As examples, cameras are used for a high-level understanding of a scene, radar is applied to weather-resistant distance perception, and LiDAR is used for accurate distance recognition. The ability of a camera to understand a scene has overwhelmingly increased with the recent development of deep learning. In addition, technologies that emulate other sensors using a single sensor are being developed. Therefore, in this study, a LiDAR data-based scene understanding method was developed through deep learning. The approaches to accessing LiDAR data through deep learning are mainly divided into point, projection, and voxel methods. The purpose of this study is to apply a projection method to secure a real-time performance. The convolutional neural network method used by a conventional camera can be easily applied to the projection method. In addition, an adaptive break point detector method used for conventional 2D LiDAR information is utilized to solve the misclassification caused by the conversion from 2D into 3D. The results of this study are evaluated through a comparison with other technologies.

Passive Coherent Location methods and techniques have an established position in the modern state-of-the-art radar. Inexpensive, easy to deploy and undetectable for other sensors, passive radars are growing in popularity. Due to that, a need arises to develop proper methods of any possible kind of countermeasure. In this work, a method of detection and localization of hidden PCL systems is proposed. Authors exploit certain physical features of an RF receiver in order to detect such a passive systems. Results of selected hardware measurements are presented as a proof of concept. Summarized findings are followed by an extensive discussion of conditions related with the method implementation in a real world scenarios.

This paper describes an antenna design approach for achieving super directivity in an AESA (Active Electronic Scanned Array) radar using an unconventional 3D phased array (PA) antenna concept based on directional Yagi–Uda elements. The proposed scheme is shown to have a wider scanning feature, with higher directivity in comparison to the same geometry dipole array without increasing the element number. The antenna’s microwave design includes an antipodal Yagi–Uda antenna element that is implemented efficiently on a microstrip PCB using a balun (balance–unbalance)-fed network. This type of antenna is valuable in restricted aperture scans for achieving a narrow antenna beam that increases the angular resolution and measurement precision of tracked targets and also enlarges the detection range or, alternatively, achieves the same performance with a lower number of elements—meeting the goal of low-cost production. The notable result of the high antenna directivity was obtained by both the element and the array architecture, which allowed for improvements in the Array Factor (AF) directivity by increasing the element’s spacing and broadening the scan sector, achieved via the suppression of the element’s Grating Lobe (GL). Another important benefit of this antenna design is the superior coupling reduction caused by its enlarged element distances, which are very significant in electronic scans. An outstanding opportunity to exploit this low coupling can be found in separated MIMO radar architecture. Other benefits of this design’s architecture are the support of a combined module and antenna on a unified board thanks to the End-Fire radiation pattern, its low frequency sensitivity, and its low-cost manufacturing.

Indoor object detection and tracking using millimeter-wave (mmWave) radar sensors have received much attention recently due to the emergence of applications of energy assignment, privacy, health, and safety. Increasing the valid field of view of the system and accuracy through multi-sensors is critical to achieving an efficient tracking system. This paper uses two mmWave radar sensors for accurate object detection and tracking: two noise reduction stages to reduce noise and distinguish cluster groups. The presented data fusion method effectively estimates the transformation of the data alignment and synchronizes the result that can allow us to visualize the objects’ information acquired by one radar on another one. An efficient density-based clustering algorithm to provide high clustering accuracy is presented. The Unscented Kalman Filter tracking algorithm with data association tracks multiple objects simultaneously in terms of accuracy and timing. Furthermore, an indoor object tracking system is developed based on our proposed method. Finally, the proposed method is validated by comparing it with our previous system and a commercial system. The experimental results demonstrate that the proposed method’s advantage is of positive significance for handling the effect of occlusions at higher numbers of weak data and for detecting and tracking each object more accurately.

One of the most crucial emergencies that require instant action to be taken during traveling across water is the so-called man overboard (MOB). Thus, constant monitoring equipment needs to be installed for the fast notice and detection of the victim to be rescued, if an incident happens. Despite the fact that different installations such as radar sensors, thermal cameras etc., can be handy, a combination of these could be beneficial yet it would increase the complexity. Nevertheless, the full potential may be not reached yet. The key component to what needs to be done in order to achieve the utmost accuracy is artificial intelligence (AI). That is, with the aid of AI, one can deploy an automated surveillance system capable of making its own humanlike decisions regarding such incidents like MOB. To achieve this, fully organized real-time cooperation among the concerned system components is essential. The latter holds since in such dynamically changing operational environments like these, information must be distributed fast, errorless and reliably to the decision center. This study aims to analyze and demonstrate the outcome of an integrated sensor-based system that utilizes AI, implemented for ship incidents. Different machine learning algorithms were used where each one of them made use of information that originated from a cluster of radar sensors located remotely. In particular, the deployed system’s objective is to detect human motion so it can be used to protect against potentially fateful events during ship voyages.

In this paper, a high gain 77-GHz receiver with a low noise figure (NF) was designed and implemented in a 40-nm CMOS process. With the purpose of making better use of active devices, an extra inductor, Ld, is adopted in the new neutralization technique. The three-stage differential low noise amplifier (LNA) using the proposed technique improves the voltage gain and reduces the NF. The receiver design utilizes an active double-balanced Gilbert mixer with a transformer coupling network between the transconductance stage and the switch stage. The flicker noise contribution from the switch MOS transistors is largely reduced due to the low DC current of the switch pairs. The LO signal is provided by an on-chip fundamental voltage-controlled oscillator (VCO) with a tuning range from 70.5 to 78.1 GHz. A conversion gain of 32 dB and a NF of 11.86 dB are achieved at 77 GHz by the designed receiver. The LNA as well as the mixer consume a total DC power of 33.2 mW and occupy a core size of 1 × 0.38 mm2.

With the development of information technology for modern military confrontations, radar emitter fingerprint identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Owing to the confidentiality of military systems, most of the existing studies use simulation data for radar emitter fingerprint identification experiments and analysis. However, most of the existing modeling methods focus on the mechanism analysis of the nonlinear fingerprint characteristics of a single independent component. Its main disadvantage is that it can only represent the nonlinear fingerprint characteristics of some components in the radar emitter system but cannot fully reflect the nonlinear fingerprint characteristics of the whole radar emitter system. In this paper, a nonlinear fingerprint-level radar simulation modeling method is proposed. In contrast to the previous single component modeling method, the systematic nonlinear characteristic modeling method of this model can provide individual radar signal data under different modulation modes and working parameters, and provide experimental conditions for data support and theoretical analysis of radar emitter fingerprint identification.

In this paper, antipodal Vivaldi antenna is designed for 5th generation (5G) mobile communication and Ku-band applications. The proposed designed has three layers. The upper layer consists of eight-element array of split-shaped leaf structures, which is fed by a 1-to-8 power divider network. Middle layer is a substrate made of Rogers 5880. The bottom layer consists of truncated ground and shorter mirror-image split leaf structures. The overall size of the designed antenna is confined significantly to 33.31 × 54.96 × 0.787 (volume in mm3), which is equivalent to 2λo× 3.3λo× 0.05λo (λo is free-space wavelength at 18 GHz). Proposed eight elements antenna is multi-band in nature covering Ku-bands (14.44–20.98 GHz), two millimeter wave (mmW) bands i.e., 24.34–29 GHz and 33–40 GHz, which are candidate frequency bands for 5G communications. The Ku-Band is suitable for radar applications. Proposed eight elements antenna is very efficient and has stable gain for 5G mobile communication and Ku-band applications. The simulation results are experimentally validated by testing the fabricated prototypes of the proposed design.

In this paper, a 24 GHz direct conversion receiver (DCR) for frequency-modulated continuous-wave (FMCW) ranging radar based on low flicker noise mixer in 90 nm silicon-on-insulator (SOI) CMOS technology is presented. A low-noise and low-power low-noise-amplifier (LNA) adopting simultaneous noise and input matching (SNIM) method is designed. Neutralized technology and boost inductor are introduced to improve performance. The measurement results of standalone LNA show that the peak gain is 17.2 dB at 23.8 GHz and the −3 dB bandwidth is around 2.2 GHz from 22.8 GHz to 25 GHz. The LNA achieves an average 3 dB NF within the 24 GHz band. A current-bleeding mixer is used to lower noise and the factors influencing flicker noise have been discussed. Proper element values and local oscillator (LO) power have been chosen to make the mixer low-noise. Measurement results illustrate that the receiver exhibits 20.3 dB peak gain, 7 dB SSB noise figure (NF) and −22 dBm IP1dB. Flicker noise of the mixer and the receiver are measured respectively and the noise knee-point of receiver is observed 60 kHz. The receiver consumes only 16 mW with chip area of 0.65 mm2 including pads. The results demonstrate that the proposed receiver can be a promising candidate for FMCW ranging radar.

In recent years, interferometric radar has been extensively used as a sensor for static and dynamic monitoring of bridges. Generally speaking, a radar can only detect displacement components along the view direction. As the movement of a real bridge or of a large structure can be rather complex, this limitation can be a significant drawback in engineering practice. In order to overcome this limitation, in this article, a multi-monostatic radar for retrieving the displacement vector is proposed. It is basically a multiple input, multiple output (MIMO) interferometric radar, equipped with a transponder that consists of a pair of antennae, with relative amplifiers, connected to the radar with a radiofrequency (RF) cable. This arrangement allows for the simultaneous detection of two independent displacement components. The radar was successfully tested both in a controlled environment and in the field on a real bridge crossing the Arno river in Florence, Italy.

A 24-GHz direct-conversion transmitter is proposed for in-cabin radar applications. The proposed RF transmitter consists of an I/Q up-conversion mixer, an I/Q local (LO) oscillator generator, and a power amplifier. To improve the linearity of the I/Q up-conversion mixer, an inverter transconductor with third-order intermodulation (IM3) distortion cancellation is proposed. To improve the I/Q balancing performance of the I/Q LO generator, a poly-phase filter, including parasitic line inductance, is proposed. By employing a highly linear I/Q up-conversion mixer and a balanced I/Q LO generator, the 24-GHz direct-conversion transmitter achieves high linearity and I/Q balancing characteristics. It is fabricated in a 65-nm CMOS process and consumes 150 mW. It shows an OP1dB of 8.6 dBm, an LO leakage of &minus, 48 dBc, and an image rejection ratio of &minus, 49 dBc for the entire operating band from 24 GHz to 24.5 GHz.

The paper proposes a simple machine learning solution for hand-gesture classification, based on processed MM-wave radar signal. It investigates the classification up to 12 different intuitive and ergonomic gestures, which are intended to serve as a contactless user interface. The system is based on AWR1642 boost Frequency-Modulated Continuous-Wave (FMCW) radar, which allows capturing standardized data to support the scalability of the proposed solution. More than 4000 samples were collected from 4 different people, with all signatures extracted from the radar hardware available in open-access database accompanying the publication. Collected data were processed and used to train Long short-term memory (LSTM) and artificial recurrent neural network (RNN) architecture. The work studies the impact of different input parameters, the number of hidden layers, and the number of neurons in those layers. The proposed LSTM network allows for classification of different gestures, with the total accuracy ranging from 94.4% to 100% depending on use-case scenario, with a relatively small architecture of only 2 hidden layers with 32 neurons in each. The solution is also tested with additional data recorded from subjects not involved in the original training set, resulting in an accuracy drop of no more than 2.24%. This demonstrates that the proposed solution is robust and scalable, allowing quick and reliable creation of larger databases of gestures to expand the use of machine learning with radar technologies.

Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated gesture recognition system that is based on frequency modulated continuous wave MIMO radar combined with deep learning network for gesture recognition. First, a pre-processing algorithm, which consists of the windowed fast Fourier transform and the intermediate-frequency signal band-pass-filter (IF-BPF), is applied to obtain improved Range Doppler Map. A range FFT based MUSIC (RFBM) two-dimensional (2D) joint super-resolution estimation algorithm is proposed to obtain a Range Azimuth Map to obtain gesture transversal feature. Range Doppler Map and Range Azimuth Map then respectively form a Range Doppler Map Time Sequence (RDMTS) and a Range Azimuth Map Time Sequence (RAMTS) in gesture recording duration. Finally, a Dual stream three-dimensional (3D) Convolution Neural Network combined with Long Short Term Memory (DS-3DCNN-LSTM) network is designed to extract and fuse features from both RDMTS and RAMTS, and then classify gestures with radial and transversal change. The experimental results show that the proposed system could distinguish 10 types of gestures containing transversal and radial motions with an average accuracy of 97.66%.

The last two decades have witnessed a rich variety of indoor positioning and localization research. Starting with Microsoft Research pioneering the fingerprint approach based RADAR, MIT’s Cricket, and then moving towards beacon-based localization are few among many others. In parallel, researchers looked into other appealing and promising technologies like radio frequency identification, ultra-wideband, infrared, and visible light-based systems. However, the proliferation of smartphones over the past few years revolutionized and reshaped indoor localization towards new horizons. The deployment of MEMS sensors in modern smartphones have initiated new opportunities and challenges for the industry and academia alike. Additionally, the demands and potential of location-based services compelled the researchers to look into more robust, accurate, smartphone deployable, and context-aware location sensing. This study presents a comprehensive review of the approaches that make use of data from one or more sensors to estimate the user’s indoor location. By analyzing the approaches leveraged on smartphone sensors, it discusses the associated challenges of such approaches and points out the areas that need considerable research to overcome their limitations.

This paper presents a microstrip array antenna designed for a 2 &times, 2 Compressive Sensing Multiple-Input Multiple-Output (CS-MIMO) millimeter-wave radar operating at 37.5 GHz. The CS-MIMO linear array antenna is designed to obtain an optimal aperture by seeking a suitable random pattern for the antenna positions. Applying CS allows a considerable reduction in the number of antennas respect to a dense array based on the Nyquist criterion. In this study, we report all possible configurations of 2 &times, 2 CS-MIMO by placing antennas in random positions, plus their compression ratio. Finally, by selecting the proper design, we examine the experimental validation of the CS-MIMO antenna prototype by comparing measurements and simulations with a Standard MIMO (Std-MIMO) antenna prototype as a benchmark. The experimental results show that the angular resolution can be increased through a random array CS-MIMO by a factor of at least 2.9 respect to Std-MIMO while preserving the radar field of view.

Direction of Arrival (DoA) estimation is essential to adaptive beamforming widely used in many radar and wireless communication systems. Although many estimation algorithms have been investigated, most of them focus on the performance enhancement aspect but overlook the computing complexity or the hardware implementation issues. In this paper, a low-complexity yet effective DoA estimation algorithm and the corresponding hardware accelerator chip design are presented. The proposed algorithm features a combination of signal sub-space projection and parallel matching pursuit techniques, i.e., applying signal projection first before performing matching pursuit from a codebook. This measure helps minimize the interference from noise sub-space and makes the matching process free of extra orthogonalization computations. The computing complexity can thus be reduced significantly. In addition, estimations of all signal sources can be performed in parallel without going through a successive update process. To facilitate an efficient hardware implementation, the computing scheme of the estimation algorithm is also optimized. The most critical part of the algorithm, i.e., calculating the projection matrix, is largely simplified and neatly accomplished by using QR decomposition. In addition, the proposed scheme supports parallel matches of all signal sources from a beamforming codebook to improve the processing throughput. The algorithm complexity analysis shows that the proposed scheme outperforms other well-known estimation algorithms significantly under various system configurations. The performance simulation results further reveal that, subject to a beamforming codebook with a 5&deg, angular resolution, the Root Mean Square (RMS) error of angle estimations is only 0.76&deg, when Signal to Noise Ratio (SNR) = 20 dB. The estimation accuracy outpaces other matching pursuit based approaches and is close to that of the classic Estimation of Signal Parameters Via Rotational Invariance Techniques (ESPRIT) scheme but requires only one fifth of its computing complexity. In developing the hardware accelerator design, pipelined Coordinate Rotation Digital Computer (CORDIC) processors consisting of simple adders and shifters are employed to implement the basic trigonometric operations needed in QR decomposition. A systolic array architecture is developed as the computing kernel for QR decomposition. Other computing modules are also realized using various linear systolic arrays and chained together seamlessly to maximize the computing throughput. A Taiwan Semiconductor Manufacturing Company (TSMC) 40 nm CMOS process was chosen as the implementation technology. The gate count of the chip design is 454.4k, featuring a core size of 0.76 mm2, and can operate up to 333 MHz. This suggests that one DoA estimation, with up to three signal sources, can be performed every 2.38 s.

In this paper, we propose a novel 77 GHz automotive radar sensor, and demonstrate its cyberattack resilience using real measurements. The proposed system is built upon a standard Frequency Modulated Continuous Wave (FMCW) radar RF-front end, and the novelty is in the DSP algorithm used at the firmware level. All attack scenarios are based on real radar signals generated by Texas Instruments AWR series 77 GHz radars, and all measurements are done using the same radar family. For sensor networks, including interconnected autonomous vehicles sharing radar measurements, cyberattacks at the network/communication layer is a known critical problem, and has been addressed by several different researchers. What is addressed in this paper is cyberattacks at the physical layer, that is, adversarial agents generating 77 GHz electromagnetic waves which may cause a false target detection, false distance/velocity estimation, or not detecting an existing target. The main algorithm proposed in this paper is not a predictive filtering based cyberattack detection scheme where an &ldquo, unusual&rdquo, difference between measured and predicted values triggers an alarm. The core idea is based on a kind of physical challenge-response authentication, and its integration into the radar DSP firmware.

The detection of adjacent vehicles in highway scenes has the problem of inaccurate clustering results. In order to solve this problem, this paper proposes a new clustering algorithm, namely Spindle-based Density Peak Fuzzy Clustering (SDPFC) algorithm. Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster. The final clustering result is obtained by a fuzzy clustering algorithm based on the spindle update. The experimental data are the radar echo signal collected in the real highway scenes. Compared with the DBSCAN, FCM, and K-Means algorithms, the algorithm has higher clustering accuracy in certain scenes. The average clustering accuracy of SDPFC can reach more than 95%. It is also proved that the proposed algorithm has strong robustness in certain highway scenes.

An overview of the cylindrical wave approach in the modeling of through-wall radar problems with targets hidden behind a dielectric wall is reported. The cylindrical wave approach is a technique for the solution of the two-dimensional scattering by buried circular cross-section cylinders in a semi-analytical way, through expansion of the scattered fields into cylindrical waves. In a through-wall radar application, the scattering environment is made by a dielectric layer between two semi-infinite half-spaces filled by air. For this layout, two possible implementations of the cylindrical wave approach have been developed in the case of plane-wave excitation. The first was an iterative scheme with multiple-reflection scattered fields, and the second was a fast and non-iterative solution, through suitable basis functions (i.e., reflected and transmitted cylindrical waves). Such waves take into account all the interactions of the source field with the interfaces bounding the dielectric layers and the targets. The non-iterative approach was also extended for excitation from the radiated field by a line source. A final system was derived for the computation of the scattered field by PEC or dielectric targets. Numerical results show the potentialities of the cylindrical wave approach in the modeling of through-wall radar, in particular in the evaluation of the scattered fields by human targets in a building’s interior, modeled with a two-dimensional approach.

This work addresses the range in which the accuracy of object identification is enhanced regardless of radar parameters. We compute the radar cross-section (RCS) of conducting objects in free space and random media. We use beam wave incidence and postulate its coherency with a finite width around an object located in the far field. Accordingly, we examine the impact of radar parameters on the RCS, where these parameters include the incident angle, target size and complexity, medium fluctuation intensity and the beam size of the incident waves. H-wave polarization is assumed for the waves’ incidence.

In recent years, interferometric radars have been extensively used as sensors for static and dynamic monitoring of bridges. Generally speaking, a radar can only detect the displacement component along its view direction. As the movement of a real bridge or a large structure can be rather complex, this limitation can be a significant drawback in engineering practice. In order toovercome this limitation, in this article, a multi-monostatic interferometric radar with radio link is proposed. This radar is able to detect a second component of displacement using a transponder. The transponder is connected to the radar through a radio link. The radio link allows the installation of the transponder far away from the radar, and even in the opposite direction. The equipment is based on a MIMO radar, two transceivers for the radio link, and a transponder. The transceivers and the transponder are essentially two antennas and an amplifier system. The equipment is experimentally tested in controlled scenarios and in the case study of Indiano Bridge, Florence, Italy.

Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.

The SuperDARN HF radars can be used for meteor observation and inversion of mid-upper atmosphere neutral wind using observed meteor echo Doppler velocities. Aiming at the problem that the extraction of meteor echo based on echo power, Doppler velocity and spectral width is rough and contains ionospheric echo, this paper optimizes the extraction algorithm of meteor echo. Based on the AgileDARN HF radar’s digital characteristics, the observation method of meteor echo was improved, and we designed a meteor observation mode without changing the hardware system: using a meteor observation with a 7.5 km range resolution and a 2 s integration time, we extracted the Doppler characteristics of different echo types at meteor echo ranges, according to these features, the extraction algorithm of meteor echo was optimized. By analyzing the measured data, the characteristics of diurnal variation, power distribution, Doppler velocity distribution and spectral width distribution of meteor echo extracted by the optimization algorithm were obtained. The meteor echo characteristics obtained by the improved algorithm are more consistent with the theoretical analysis, thus, the improved algorithm is better than the SuperDARN high frequency radar meteor echo extraction algorithm and has good performance. The meteor echo extraction algorithm presented in this paper can extract the meteor echo more accurately, so that the atmospheric neutral wind can be retrieved more accurately. At the same time, the proposed algorithm is not only applicable to AgileDARN HF radar meteor observation mode data, but also to AgileDARN and SuperDARN normal mode data, which is beneficial to expand the data application of SuperDARN radars.

Accurate direction of arrival (DOA) estimation of wideband, low-power nonstationary signals is important in many radio frequency (RF) applications. This article analyses the performance of two incoherent aggregation techniques for the DOA estimation of high chirp-rate linear frequency modulated (LFM) signals used in modern radar and electronic warfare (EW) applications. The aim is to determine suitable aggregation techniques for blind DOA estimation for real-time implementation with a frequency channelised signal. The first technique calculates a single pseudospectrum by directly combining the spatial covariance matrices from each of the frequency bins. The second technique first calculates the spatial pseudospectra from the spatial covariance matrix (SCM) from each frequency bin and then combines the spatial pseudospectra into one single estimate. Firstly, for single and multiple signal emitters, we compare the DOA estimation performance of incoherent SCM-based aggregation with that of the incoherent spatial pseudospectra-based aggregation using the root mean-squared error (RMSE). Secondly, we determine the types of signals and conditions for which these incoherent aggregation techniques are more suited. We demonstrate that the low-complexity SCM-based aggregation technique can achieve relatively good estimation performance compared to the pseudospectra-based aggregation technique for multiple narrowband signal detection. However, pseudospectra aggregation is better suited for single wideband emitter detection. Both the incoherent aggregation techniques presented in this article offer a computational advantage over the coherent processing techniques and hence are better suited for real-time implementation Refereed/Peer-reviewed

Design criteria of low-cost, dual-concentric metasurface possessing wideband phase gradient (PG) are introduced. The radar cross-section reduction (RCSR) is explained by anomalous reflection that characterizes the superficial planar. The geometry consists of two single band RCSR modulated surfaces (MSs) that are triggered in each other. Each MS is built-up of square patch (SP) unit cells configured as a modulation structure to realize PG that causes anomalous reflection and monostatic RCSR behavior. Applying sinusoidal modulation to the sequence of the SP unit cells leads to the formation of PG along the surface and hence the intensity of the reflected wave is reduced for the broadside direction (θr=0∘). The proposed structure fabricated on a 0.8 mm thin FR-4 substrate extends over 249 × 249 mm2. It achieves a wide RCSR bandwidth from 20.9 GHz to 45.7 GHz (i.e., relative bandwidth of 75%) as designed in Dassault Systèmes (CST) Microwave Studio as a full-wave simulator and confirmed by the measurement results.

Maritime safety issues have aroused great attention, and it has become a difficult problem to use the sky-wave over-the-horizon radar system to locate foreign targets or perform emergency rescue quickly and timely. In this paper, a distributed multi-point sky-wave over-the-horizon radar system is used to locate marine targets. A positioning algorithm based on the Doppler frequency is proposed, namely, the two-step weighted least squares (2WLS) method. This algorithm first converts the WGS-48 geodetic coordinates of the transceiver station to spatial rectangular coordinates, then, introduces intermediate variables to convert the nonlinear optimization problem into a linear problem. In the 2WLS method, four mobile transmitters and four mobile receivers are set up, and the Doppler frequency is calculated by transmitting and receiving signals at regular intervals, it is proven that the 2WLS algorithm has always maintained a better positioning accuracy than the WLS algorithm as the error continues to increase with a certain ionospheric height measurement error and the Doppler frequency measurement error. This paper provides an effective method for the sky-wave over-the-horizon radar to locate maritime targets.

We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98% accuracy on 10-fold cross-validation for both data sets.

Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).

In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. The conventional classifiers based on radar data using deep learning are single input structures using range–Doppler maps or micro-Doppler. Deep learning with a single input structure has limitations in improving classification performance. In this paper, we propose a multi-input classifier based on convolutional neural network (CNN) to reduce the amount of computation and improve the classification performance using the frequency modulated continuous wave (FMCW) radar. The proposed multi-input deep learning structure is a CNN-based structure using a distance Doppler map and a point cloud map as multiple inputs. The classification accuracy with the range–Doppler map or the point cloud map is 85% and 92%, respectively. It has been improved to 96% with both maps.

The active electronically scanned array (AESA) radar consists of many transmit/receive (T/R) modules and is used to track missiles approaching destroyers and fighters. The performance of the AESA radar depends on the T/R module temperature. The T/R module temperature should be maintained under 80 °C to guarantee the performance of the AESA radar. In order to match the design requirements of the cooling system of the AESA radar, it is necessary to evaluate the cooling performance according to various operation/installation environments. In this study, computational fluid analysis was performed by changing the number of T/R modules and the coolant mass flow rate to evaluate the cooling performance of the AESA radar coolant channel. The number of T/R modules was changed from 2 to 16, and the number of coolant inlet Re was changed from 277 to 11,116. As a result, it was confirmed that the temperature increased as the number of T/R modules increased. In addition, when the coolant status was laminar flow, it was confirmed that the cooling performance was significantly lowered. Therefore, the coolant status should be transient or turbulence to decrease the temperature of the T/R module. Additionally, the correlation between the arrangement of the T/R module and the cooling flow must be considered to cool the AESA radar.

In the dual-polarized radar system, the horizontally and vertically polarized signals can be exploited to improve the direction of arrival (DOA) estimation performance. In this paper, the DOA estimation problem is considered in the dual-polarized radar. By exploiting the target sparsity in the spatial domain, the sparse-based method is proposed after formulating the DOA estimation problem as a sparse reconstruction problem. In the traditionally sparse methods using the compressed sensing (CS) theory, the spatial domain is discretized into grids to establish a dictionary matrix and solve the sparse reconstruction problem, but the off-grid error is introduced in the discretized grids. Therefore, we formulate a novel definition of atomic norm for the dual-polarized signals and give an atomic norm-based method to denoise the received signals. Then, an efficient semidefinite program (SDP) is derived, and the DOA is estimated by searching the peak values of the denoised signals. Simulation results show that the proposed method can significantly improve the DOA estimation performance in the dual-polarized radar. Additionally, compared with the state-of-art methods, the proposed method has better estimation performance with relatively low computational complexity.

One of the major concerns in radar cross-section (RCS) measurements is the isolation of the target echo from unwanted spurious signals. Generally, the method of software range gate is applied to extract useful data. However, this method may not work to expectations, especially for targets with a large length-width ratio. This is because the effective target zone is dependent on the aspect angle. The implementation of conventional fixed range gates will introduce an uneven clutter signal that leads to a decline in signal-to-noise ratio. The influence of this uneven clutter signal becomes increasingly severe in the terahertz band, where the wavelength is short and the illumination power is weak. In this work, the concept of adaptive range gates was adopted to extract a target echo of higher accuracy. The dimension of the range gate was determined by the angle-dependent radial projection of the target. In order to evaluate the performance of the proposed method, both experimental measurements and numerical simulations were conducted. Noticeable improvements in the signal-to-noise ratio at certain angles were observed.

Many new challenges are faced by the PBR (passive bi-static radar) employing non-cooperative radar illuminators. After the CFAR (constant false alarm) processor, the appearance of the amount of false alarm clutter points impacts the following tracing performance. To enhance the PBR tracing performance, we consider to reduce these clutter points before target tracing as soon as possible. In this paper, we propose a PBR clutter suppression algorithm based on dilation morphology of non-uniform grid. Firstly, we construct the non-uniform polar grid based on the acquisition geometry of PBR. Then, with the help of the grid platform, we separate the false alarm clutter points based on the dilation morphology. To efficiently operate the algorithm, we build up its parallel iteration scheme. To verify the performance of the proposed algorithm, we utilize both simulated data and field data to do the experiment. Experimental results show that the algorithm can effectively suppress most of the clutter points. Besides, we respectively combine the proposed suppression algorithm with two typical tracking algorithms to test the performance. Experimental results reveal that the compound tracing algorithm outperforms the traditional one. It can enhance the PBR tracing performance, reduce the occurrence probability of false tracks and meanwhile save time.

Knowledge of the subsurface structure not only provides useful information on lunar geology, but it also can quantify the potential lunar resources for human beings. The dual-frequency lunar penetrating radar (LPR) aboard the Yutu rover offers a Special opportunity to understand the subsurface structure to a depth of several hundreds of meters using a low-frequency channel (channel 1), as well as layer near-surface stratigraphic structure of the regolith using high-frequency observations (channel 2). The channel 1 data of the LPR has a very low signal-to-noise ratio. However, the extraction of weak signals from the data represents a problem worth exploring. In this article, we propose a weak signal extraction method in view of local correlation to analyze the LPR CH-1 data, to facilitate a study of the lunar regolith structure. First, we build a pre-processing workflow to increase the signal-to-noise ratio (SNR). Second, we apply the K-L transform to separate the horizontal signal and then use the seislet transform (ST) to reserve the continuous signal. Then, the local correlation map is calculated using the two denoising results and a time&ndash, space dependent weighting operator is constructed to suppress the noise residuals. The weak signal after noise suppression may provide a new reference for subsequent data interpretation. Finally, in combination with the regional geology and previous research, we provide some speculative interpretations of the LPR CH-1 data.

Radar signal recognition is an indispensable part of an electronic countermeasure system. In order to solve the problem that the current techniques have, which is a low recognition rate and a slow recognition speed for radar signals, a rapid accurate recognition system is proposed, especially for when multiple signals arrive at the receiver. The proposed system can recognize eight types of radar signals while separating signals: binary phase shift keying (BPSK), linear frequency modulation (LFM), Costas, Frank code, and P1&ndash, P4 codes. Regression variational mode decomposition (RVMD) is explored to separate the received signals, which saves time for parameter optimization of variational mode decomposition (VMD). Furthermore, signal separation and a noise removal technique based on VMD and the first component recognition technique based on a deep belief network (DBN) are proposed. In addition, in order to overcome the loss of the secondary component caused by signal separation, a fusion network is explored to increase the recognition rate of the secondary component in a short time. The simulation results show that the recognition system achieves an overall recognition rate of 99.5% and 94% at a signal-to-noise ratio (SNR) of 0 dB when receiving single signals and double signals, while spending 0.8 s and 2.23 s, respectively. The proposed system can also be used to recognize medical and mechanical signals.

Synthetic Aperture Radar (SAR) raw data missing occurs when radar is interrupted by various influences. In order to cope with this problem, a new method is proposed to focus the azimuth missing SAR raw data via segmented recovery in this paper. A reference function in time domain is designed to make the missing raw data sparser in two dimensional frequency domain. Afterwards, greedy algorithms are available to recover the missing data in two dimensional frequency domain. In addition, in order to avoid range frequency aliasing problem caused by reference function multiplication in time domain, the missing raw data is split into several parts in range direction and is recovered with a segmented recovery strategy. Then, the recovered raw data is available to be focused with traditional SAR imaging algorithms. The range migration algorithm is chosen to deal with the recovered raw data in this paper. Point target and area target simulations are carried out to validate the effectiveness of the proposed method on azimuth missing SAR raw data. Moreover, the proposed method is implemented on real SAR data in order to further provide convincing demonstration.

Target echo cancellation is an ingenious method that protects the target of interest (TOI) from being detected by radar. Interrupted-sampling repeater jamming (ISRJ) is a novel deception jamming method for linear frequency modulation (LFM) radar countermeasures, which has been applied in target echo cancellation recently. Compared with the conventional cancellation method, not only can the target echo be successfully cancelled at radar receiver, but a train of false targets is also produced and forms deception jamming by applying the ISRJ technique. In this paper, an improved radar target echo cancellation method based on ISRJ is proposed that utilizes an extra frequency shifting modulation on the intercepted LFM radar signal. The jammer power is more efficiently utilized by the proposed method. Moreover, more flexible multi-false-target deception jamming can be obtained by adjusting the interrupted sampling frequency. The real target remains effectively protected by the false preceding target in the presence of amplitude mismatch of cancellation signal and target echo. Numerical simulations and measured data experiments are conducted to demonstrate the effectiveness of the proposed method.

The frequency diverse array (FDA) has drawn substantial attention because it provides a new degree of freedom. However, the multitarget localization is fundamentally limited by the range-angle-coupled and range-periodic beampattern of the basic FDA. It has been suggested to design a special FDA configuration to localize targets, but seldom of the existing works consider the design both in transmitting and receiving. In this paper, a transmit&ndash, receive system of FDA radar is proposed for the multitarget localization. In order to decouple the beampattern in the range and angle domains, the configurations of subarray-based FDA (SB-FDA) and full-band FDA (FB-FDA) are chosen as transmitter and receiver, respectively. In such a system framework, the receive beamwidth in range domain is only a quarter of the transmission. Then, two typical multitarget scenarios, sparse targets and unresolved targets, are both considered in the multitarget localization. For sparse targets, a proper frequency increment is selected to control the range-periodic transmit and receive mainlobes to focus on a single target, besides being staggered with others. In this way, multitarget localization is achieved in different pulses with monopulse processing. For unresolved targets, a method of intra-pulse beam scanning is proposed to localize each target with little interference from others. We also analyze the system performance in Cram&eacute, r-Rao lower bound (CRLB) of localization and output signal to interference-plus-noise ratio (SINR). Several simulation results demonstrate the effectiveness of the proposed transmit&ndash, receive system in multitarget localization.

The unknown mutual coupling effect between antennas significantly degrades the target localization performance in the bistatic multiple-input multiple-output (MIMO) radar. In this paper, the joint estimation problem for the direction of departure (DOD) and direction of arrival (DOA) is addressed. By exploiting the target sparsity in the spatial domain and formulating a dictionary matrix with discretizing the DOD/DOA into grids, compressed sensing (CS)-based system model is given. However, in the practical MIMO radar systems, the target cannot be precisely on the grids, and the unknown mutual coupling effect degrades the estimation performance. Therefore, a novel CS-based DOD/DOA estimation model with both the off-grid and mutual coupling effect is proposed, and a novel sparse reconstruction method is proposed to estimate DOD/DOA with updating both the off-grid and mutual coupling parameters iteratively. Moreover, to describe the estimation performance, the corresponding Cramér–Rao lower bounds (CRLBs) with all the unknown parameters are theoretically derived. Simulation results show that the proposed method can improve the DOD/DOA estimation in the scenario with unknown mutual coupling effect, and outperform state-of-the-art methods.

Object detection is an important perception task in autonomous driving and advanced driver assistance systems. The visible camera is widely used for perception, but its performance is limited by illumination and environmental variations. For robust vision-based perception, we propose a deep learning framework for effective sensor fusion of the visible camera with complementary sensors. A feature-level sensor fusion technique, using skip connection, is proposed for the sensor fusion of the visible camera with the millimeter-wave radar and the thermal camera. The two networks are called the RV-Net and the TV-Net, respectively. These networks have two input branches and one output branch. The input branches contain separate branches for the individual sensor feature extraction, which are then fused in the output perception branch using skip connections. The RVNet and the TVNet simultaneously perform sensor-specific feature extraction, feature-level fusion and object detection within an end-to-end framework. The proposed networks are validated with baseline algorithms on public datasets. The results obtained show that the feature-level sensor fusion is better than baseline early and late fusion frameworks.

The Chang&rsquo, e-5 lunar exploration mission of China is equipped with a Lunar Regolith Penetrating Radar (LRPR) for measuring the thickness and structures of the lunar regolith in the landing area. Since the LRPR is stationary, an ultra-wideband multiple-input multiple-output (MIMO) array is designed as a replacement for conventional mobile subsurface probing systems. The MIMO array, with 12 antenna elements and a switch matrix, operates in the frequency band from 1.0 to 4.75 GHz. In this work, the design and layout of the antenna elements were optimized with respect to the lander. To this end, the antenna elements were designed as miniaturized Vivaldi antennas with quarter elliptical slots (i.e., quarter elliptical slotted antenna, or QESA). QESAs are significantly small while being able to mitigate the impact of the lander on antenna electrical performances. QESAs also have a wide operating bandwidth, flat gain, and excellent time domain characteristics. In addition, a high-temperature resistant ultra-light radome with high transmissivity is designed to protect the external antenna array. After calibration, the MIMO array is used to detect targets embedded in volcanic ash. The detection depth reaches 2.5 m, and the detection effect is good.

An evaluation of monostatic radar cross section (RCS) response in the near-field range was performed for several targets with different and complex topologies. The main objective was to provide and validate an efficient tool based on electromagnetic (EM) simulations to characterize a traffic scenario. Thus, a novel method based on the combination of geometrical theory of diffraction (GTD) and physical optics (PO) was used to estimate RCS, and the results were compared with the method of moments (MoM) methodology. The simulations were experimentally validated using a commercial vehicular frequency-modulated continuous wave (FMCW) radar at 24 GHz. With this simple measurement system, RCS measurements can be made using an easier and cheaper process to obtain RCS response in the near-field range, which is the most usual situation for traffic applications. A reasonable agreement between the measurements and the EM simulations was observed, validating the proposed methodology in order to efficiently characterize the RCS of targets typically found in real traffic scenarios.

In this paper, we present ground moving target indication (GMTI) signal processing algorithm encompassing clutter suppression, target detection and parameter estimation. One of the most significant yet least publicized is the need of the GMTI mode for a forward-looking airborne radar. The integration of GMTI mode in a forward-looking airborne radar allows reconnaissance and surveillance operations in all weather conditions. In this context, space time adaptive processing (STAP) offers a unique prospect of enabling the GMTI mode in forward looking airborne radar. STAP is a two-dimensional filter designed to suppress platform motion-induced clutter Doppler spread. Interference is characterized by a covariance matrix. In the case of a forward-looking airborne radar, the clutter Doppler is dependent on range. Clutter Doppler dependency on the range renders the training cells heterogeneous. The heterogeneity effects are particularly prominent in the near range bins. Non-homogeneous training cells have a deleterious effect on STAP performance. In this study, we propose an adaptive Doppler compensation to mitigate the degraded STAP performance in the near range bins. The adaptivity feature circumvents the need for the availability of radar parameters in real-time. The real time implementation of STAP is impeded by requirements of a large number of training samples and covariance matrix inversion. Therefore, there is a dire need to devise a framework to detect and estimate target parameters within the STAP. In this regard, we propose an efficient STAP algorithm to detect and estimate target parameters. STAP weights are applied to the input data to obtain a 3D array. The range projection of the 3D array is utilized to detect and estimate the range of the target, while the angle&ndash, Doppler projection is used to estimate spatial and temporal parameters of the target. Most of the literature on STAP is geared towards a known covariance matrix. The assumption of a known covariance matrix may degrade STAP performance because of the inherent mismatches between the actual and assumed target steering vectors. In this study, we estimate the covariance matrix based on the synthetic data generated from a model of an airborne phased array radar. The developed STAP algorithms closely mimic a real-time implementation scheme in an airborne radar platform. The results of the proposed algorithm are validated through target parameter estimation and STAP metrics on synthetic data.