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Recently, radar frequency bands have attracted attention as candidates for cognitive radio owing to their wide bandwidth but low utilization. When spectrum sensing is performed in radar bands, sliding-window-based detection can be used to exploit the sparsity of the radar pulse signal in the time domain and obtain a sufficient number of samples. The detection performance and sensing time depend on the configuration of the windows. The detection performance was optimized when the window size was equal to the pulsewidth. However, the pulsewidth is generally unknown. Another way to increase the detection performance is to obtain more samples of the window. However, this causes large computation overhead owing to the sparsity of radar signals. Therefore, we propose an adaptive-sliding-window-based detection scheme to address these problems. First, the window is adaptively applied. Second, a pulsewidth estimation algorithm is proposed to approximate the window size to the pulsewidth for each detection. Additionally, we demonstrate the performance improvement of the proposed scheme and evaluate the estimation error of the proposed algorithm via simulations.

Reasonable route planning and resource allocation strategy in the airborne radar systems (ARS), can sufficiently utilize the limited resources and promote the multitarget tracking (MTT) performance. However, using separately the route planning and resource optimization method cannot take full advantage of the airborne platform. Considering this issue, we propose a joint online route planning and resource optimization strategy in the ARS to improve the system capability for MTT. First, a kinematic model of the ARS, including the mathematical expression between the radar states and the system control parameters, is introduced, which integrates the route planning to the radar scheduling scheme. Next, the posterior Cramer–Rao lower bound about route planning and resource optimization variables for the tracking targets is derived. Then, a scaled-based utility function is established to quantify the MTT performance. Hereafter, a nonconvex problem is formulated by minimizing the utility function with route and resource constraints, and then a efficient three-stage partition-based solution is proposed. Finally, simulation experiments demonstrate the effectiveness of the proposed algorithm. Furthermore, over the traditional benchmark algorithm, the tracking performance of the proposed approach improves 30.44%.

As an increasingly recognized optical interferometric technique for nondestructive testing and evaluation, shearography has attracted extensive interest in various industrial applications. However, it suffers from the ignorance of the whole process of dynamic surface deformation and the difficulty in determining the depth-resolved information of inhomogeneities. In this regard, a novel speckle pattern interferometric modality named photothermal radar shearography is proposed. Unlike the differential mode of conventional shearography, the present work focuses on the dynamic surface displacement field channel technique with more comprehensive understanding of the subsurface structural information. With the utilization of frequency modulated photothermal excitation, the depth-distributed information of subsurface structures and inhomogeneities is encoded into the induced dynamic surface deformation. Using Hilbert transform and least-square method solved by discrete cosine transform, the time-domain interference signal of each pixel from the recorded speckle patterns sequence is demodulated and unwrapped to obtain the transient full-field shearographic phase distribution which indicates the dynamic surface deformation. In the meanwhile, incrementally delayed cross-correlation matched filtering allows for the localization of axial energy distribution to generate depth-selective structural display for tomography. This proposed transient-based interferometric methodology thus enables depth-tomographic profiles and three-dimensional visualization of subsurface anomalies for the first time, which significantly improves the superiority and attractiveness of shearography in providing insight into the status, performance and reliability of industry.

This paper presents a 160 GHz, D-band, low-noise amplifier (LNA) and a D-band power amplifier (PA) implemented in the Global Foundries 22 nm CMOS FDSOI. The two designs are used for the contactless monitoring of vital signs in the D-band. The LNA is based on multiple stages of a cascode amplifier topology with a common source topology adopted as the input and output stages. The input stage of the LNA is designed for simultaneous input and output matching, while the inter-stage-matching networks are designed for maximizing the voltage swing. The LNA achieved a maximum gain of 17 dB at 163 GHz. The input return loss was quite poor in the 157–166 GHz frequency band. The −3 dB gain bandwidth corresponded to 157–166 GHz. The measured noise figure was between 7.6 dB and 8 dB within the −3 dB gain bandwidth. The power amplifier achieved an output 1 dB compression point of 6.8 dBm at 159.75 GHz. The measured power consumptions of the LNA and the PA were 28.8 mW and 10.8 mW, respectively.

In-band full-duplex (IBFD) technology has the potential to not only double the communication throughput but also enable additional capabilities. The fusion of radar and communication subsystems is an excellent example of how IBFD facilitates integration of radar functionality into a communication system. Developing and analysis of a joint IBFD radar-communication (RadCom) system is at the core of this article. This system derives the range-Doppler image from the state of an adaptive filter, which already exists in a typical IBFD transceiver. Our approach is waveform-independent and reuses the radio frequency (RF) front-end while it requires little additional digital logic. The proposed system is prototyped to assess the modem-radar coexistence in a real-world IBFD communication link budget. Employing the prototyped system, we quantify the additional required logic resources and investigate whether such a RadCom approach dictates a tradeoff between the two intended functionalities. The experimental results show that the proposed solution enables a communication system to detect targets within 20 m while maintaining an IBFD link with another communication node, info:eu-repo/semantics/published

Autonomous vehicles have been widely equipped with radars, camera, and LiDARs due to their complementary capabilities of environment perception. However, it becomes a critical task to accurately track the trajectory of the preceding vehicle when the host vehicle suffers a LiDAR failure under challenging lighting conditions. This paper proposes an integrated learning-based solution to address this critical issue. It is composed of a Q-learning-based Gaussian mixture model (QL-GMM) for clustering dense radar data, a weight-scheduled method for radar data association, and a switchable recurrent neural network (RNN) with dual-level long short-term memory (DL-LSTM) cells for trajectory tracking under LiDAR failure and various illumination levels. Specifically, the QL-GMM is first introduced to improve the conventional GMM-EM algorithm with the cluster number determined by a Q-learning approach. Then, the weight-scheduled method is presented to associate the data from multiple radars. Furthermore, the switched DL-LSTM RNN is developed to adaptively fuse the trajectories from the radar and camera streams based on three lighting modes. The training data and testing data were acquired on a fully-instrumented autonomous vehicle. Experimental verification results demonstrate that the proposed method can achieve a promising improvement for LiDAR-fault-tolerant trajectory tracking under different lighting conditions.

In this paper, a method of enhancing gain in a microstrip patch array antenna using two metallic plates for 24 GHz radar applications is presented. A 4 × 1 linear microstrip square patch array antenna covering the 24.0 to 24.25 GHz frequency range and using a shunt-connected series feed network with a tapered power distribution was first designed with a measured maximum gain of 9.8 dBi and dimensions of 30 mm × 12 mm. Two metallic plates were appended along the array axis of the antenna to double the gain in the 4 × 1 array antenna. Effects on performance from varying the tilting angle and length of the metallic plates, such as the input reflection coefficient, the radiation patterns, and gain, were investigated through simulation. Gain enhancement in the 4 × 1 patch array antenna with metallic plates was highest when the tilting angle was around 70∘, and gain kept increasing as the length of the metallic plates increased. A prototype of the 4 × 1 patch array antenna was fabricated with plates at a tilting angle of 70∘, a length of 50 mm, and a measured maximum gain of 16.8 dBi. Therefore, a gain enhancement of about 7 dB was achieved by adding the metallic plates along the array axis of the 4 × 1 patch array antenna.

This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz., Comment: Published in Transactions on Robotics. Edited 2022-11-07: Updated affiliation and citation

Dual-function radar and communication (DRC) system has been recently recognized as a promising approach to solve the spectrum scarcity problem. However, when the target exists within a crowded area where pathloss dominating, the performance of radar may be severely degraded. To tackle this issue, this article proposes for the first time the deployment of an intelligent reflecting surface (IRS) to help the DRC system to enhance the radar detection performance. The IRS can configure the environment around the radar by adaptively adjusting the phases of its reflecting units to strengthen the signal quality toward specific directions, mostly the target direction, and completely null-out transmissions in other directions, mostly the directions toward the communication system. Specifically, in this article, we investigate the joint optimization of the IRS passive phase-shift matrix (PSM) and precoding matrix of the radar-aided basestation for the DRC system. The optimization is carried-out through maximizing the signal-to-noise ratio (SNR) at the radar receiver under both sensing and communication constraints, which turns out to be a nonconvex problem. In order to circumvent this challenging problem, an alternation optimization approach is employed to decouple the optimization variables and split this intractable problem into two subproblems. However, it is still challenging to obtain the optimal PSM due to the high power of the objective function and the unit-modulus constraints. To solve this problem, a majorization–minimization algorithm is conceived to transform the nonconvex problem to an easy to solve quadratic constraint quadratic programming problem. Simulation results demonstrate that the IRS can help improving the performance of the DRC system in terms of the received SNR, and the proposed algorithm shows fast convergence.

Radar emitter classification (REC) plays an increasingly significant role in the electronic reconnaissance system. Due to there are many unreliable factors in traditional feature-based methods, this article focuses on improving the accuracy and stability of REC based on multipaths of original radar signals. A novel aggregated multipath extreme gradient boosting (AMP-XGBoost) is therefore put forward. Multi-path, including multi-scale, multi-domain transformations and their concatenations, is developed to exploit more information of the original signal, which contributes to providing more distinguishable features. XGBoost is used to automatically extract the features contained in these paths and complete recognition. Finally, multiple paths are sifted and aggregated according to certain weights to obtain a higher accuracy. Experiments are carried out based on signals from 6 different intra-pulse modulation mode radars with the same radio frequency (RF) range. It is demonstrated that the proposed method has higher REC accuracy than the methods only based on a single scale or domain. In the meantime, experimental results under different low SNRs show that the proposed method has higher stability. Finally, experiments based on the measured aviation radar data proves the superiority of the proposed method.

Nowadays, accurate and real-time vehicle tracking is critical to ensure the safety of intelligent vehicles. However, tracking in the complex traffic environments still remains a challenging issue. In this article, we present a road-map aided Gaussian mixture probability hypothesis density (RA-GMPHD) filter for multivehicle tracking with automotive radar. Since the road-map is commonly available in traffic scenarios, we focus on leveraging road-map information to enhance the tracking performance. We first model the vehicle dynamics in a 2-D road coordinates, then approximatively map it onto ground coordinates considering map errors. Additionally, we integrate the variable structure interacting multiple model into the RA-GMPHD filter considering both the dynamic uncertainty of targets and the road geographic constraints. Furthermore, we perform extensive simulations and conduct physical testings to demonstrate the superiority of our approaches compared with state-of-the-art method. Experimental results show our methods enhance both the tracking quality and tracking continuity.

This paper reports an experimental study aimed at confirming disruptions in the operation of ADS-B receivers. The experimental investigation into disruptions in the operation of ADS-B receivers involved the FlightAware Piaware receiver. Examples of the disrupted performance of ADS-B receivers are given. It was found that the experimentally detected disruptions in the operation of ADS-B receivers could lead to a decrease in the accuracy of determining the coordinates of air objects with the joint use of the radar and multilateration technology. A method for determining the coordinates of an air object by radar with additional use of multilateration technology has been devised. The method involves the following stages: entering initial data, the calculation of distances between the points of reception and the air object, the computation of the inconsistency vector, the calculation of the matrix of partial derivatives taking into consideration the estimates of the coordinates of an air object at the previous iteration, the computation of the correction, the calculation of the refined coordinates of the air object. Unlike those known ones, the improved method for determining the coordinates of an air object by a radar additionally uses multilateration technology. The accuracy of determining the air objects' coordinates by a radar with the additional use of multilateration technology was estimated. It was established that the additional application of multilateration technology would reduce the error in determining the coordinates of an air object by 1.58 to 2.39 times on average, compared to using only an autonomous radar

Automatic detection of a falling person based on noncontact sensing is a challenging problem with applications in smart homes for elderly care. In this article, we propose a radar-based fall detection technique based on time-frequency analysis and convolutional neural networks. The time-frequency analysis is performed by applying the short-time Fourier transform to each radar return signal. The resulting spectrograms are converted into binary images, which are fed into the convolutional neural network. The network is trained using labeled examples of fall and nonfall activities. Our method employs high-level feature learning, which distinguishes it from previously studied methods that use heuristic feature extraction. The performance of the proposed method is evaluated by conducting several experiments on a set of radar return signals. We show that our method distinguishes falls from nonfalls with 98.37% precision and 97.82% specificity, while maintaining a low false-alarm rate, which is superior to existing methods. We also show that our proposed method is robust in that it successfully distinguishes falls from nonfalls when trained on subjects in one room, but tested on different subjects in a different room. In the proposed convolutional neural network, the hierarchical features extracted from the radar return signals are the key to understand the fundamental composition of human activities and determine whether or not a fall has occurred during human daily activities. Our method may be extended to other radar-based applications such as apnea detection and gesture detection.

This paper proposes a compact three-mode base station capable of performing radar sensing, communication, and wireless power transfer (WPT) in collaboration with indoor sensor networks. With regard to the wireless sensor node, the base station transmits two-tone signals in the downlink to support its operation and provides two-way communication. The sensor node sends uplink information through backscattering using the third order intermodulation (IM3) product of the rectification. In the radar mode, a single-tone continuous wave (CW) is used to monitor if there is a moving target in the static environment. If a speed is detected, the transmit signal to the node is stopped, while the single-tone CW excitation will continue until the speed of the target is zero, and then the base station transmits a stepped frequency continuous wave (SFCW) signal to measure the distance of the target. The repeat between the two radar waveforms continues until the target is undetectable within the detection range. The software defined radio PlutoSDR is adopted as the base station. The system can wirelessly supply power and bi-directionally communicate with a CO2 sensor node 2 m away. It gives a range resolution of 2.5 cm and a minimum detectable speed of 0.25 m/s in the radar mode.

In passive multiple-input multiple-output (MIMO) radar, the transmit signals of the noncooperative illuminators of opportunity are usually not completely known. They are usually standard communications signals following specific protocols which stipulate the preamble format. In this article, we show that the preamble information can be exploited to improve the performance of passive MIMO radar. We have derived two generalized likelihood ratio tests (GLRTs) for passive MIMO radar detection, one for the case where the noise variance is known, and the other for the case where the noise variance is unknown. Our analysis shows that the derived two GLRTs have constant false alarm rate. Simulation results show that under the same condition the derived GLRTs outperform the GLRTs without using the preamble information. Moreover, the derived GLRTs are also compared with the GLRTs for active MIMO radar that totally knows transmit signals. These comparisons show that the performance of the derived GLRTs vary between the GLRTs for active MIMO radar and those for the passive MIMO radar with the transmit signals completely unknown.

Highly automated systems are taking over an increasing number of human tasks. This includes not only the execution of certain actions but also perception and interpretation of the environment. Here, environment-perceiving sensor systems such as camera, lidar, or radar represent a key technology while posing a major challenge in terms of complex physical interactions that make validation very complicated. Typically, required system tests within dynamic environments are enabled too late, while real-world testing is neither efficient nor sufficient. Striving for a seamless transition from a first digital prototype to the final real system, we propose the use of digital twins and hybrid testbeds. These combine real and simulated components per availability or requirement to continuously enable simulation-based validation in all stages of development. To this end, we present an overall concept of scalable sensor models and simulation methods that meet the varying requirements along the development process by introducing a triple-axis scalability. Here, reusable building blocks allow for scaling across different sensor types and applications with varying level of detail, while natural interfaces and measurement-accurate ground truth annotators ensure a seamless integration into the underlying development process using methods of X-in-the-loop.

In this article, to get a high-resolution radar image with distributed frequency modulated continuous waveform multiple-input–multiple-output (FMCW MIMO) radar, Bayesian matching pursuit (BMP)-based imaging methods are proposed, in which the received signals at the distributed FMCW MIMO radars are reformulated in terms of the (azimuth, range) patches in the image region of interest and the maximum a posterior (MAP) estimator that can estimate the azimuth angles and ranges of multiple targets is then derived. For a single FMCW MIMO radar, we first propose the BMP-based imaging, in which the support vector (indicating the presence of targets in the radar image patches) and the associated coefficients (the reflection coefficients of the associated targets) are iteratively updated. Because MAP estimation needs the combinatorial search over all possible candidates to find the nonzero elements in the support vector, an approximated MAP estimation method is newly proposed. In addition, by extending the BMP-based imaging for a single FMCW MIMO radar, we develop a new distributed BMP-based imaging method when multiple FMCW MIMO radars are spatially distributed. Through the simulation, we confirm that the proposed BMP-based radar imaging methods outperform the conventional backprojection method or the orthogonal matching pursuit-based method with slightly increased computational complexity.

Radar system design for the detection of targets with unknown actual range in spectrally crowded environments is a challenge due to increasing demands for spectrum and the possibly experienced echo eclipsing. Assuming unknown target echo delay, this article deals with robust joint design of the radar code and receive filter bank guaranteeing spectral compatibility with the surrounding electromagnetic radiators. The average signal-to-interference-plus-noise ratio (SINR) at the output of the filter bank is considered as the performance measure to optimize under both similarity and spectral compatibility constraints on the radar code. Through an appropriate reformulation of the resulting nonconvex optimization problem, we devise an alternate optimization algorithm, which sequentially improves the average SINR and converges to a point fulfilling the Karush–Kuhn–Tucker (KKT) conditions of the design problem. Each iteration of the algorithm involves both a convex and a hidden convex optimization problem, which can be solved via the rank-1 decomposition procedure. Finally, the performance is assessed by studying the tradeoff among the SINR, spectral shape, and autocorrelation. Numerical results reveal that the new method has better flexibility and robustness compared to several counterparts, which are presented in open literature.

In this article, a bidirectional thermal wave radar thermography (BTWRT) is used for the inspection of hybrid carbon/glass fiber reinforced polymer (C/GFRP) laminates with subsurface defects, and the reliability of BTWRT is deeply investigated based on the probability of detection (POD) analysis. Three classes of C/GFRP laminates are prepared for experimental study, and the hybrid volume ratio of glass fiber and carbon fiber is 1:4, 1:1, and 4:1, respectively. Each class of C/GFRP laminate includes a total of 72 artificial flat-bottom holes with different diameter-to-depth ratios. An 808-nm laser is used as external excitation and modulated by a bidirectional chirp signal. Thermal wave radar feature images are obtained by time-slice imaging algorithms [dynamic component extraction, Hilbert transform (HT), and analytical chirp correlation (ACC)] and conventional algorithms (cross-correlation, chirp lock-in, HT mean, and fast Fourier transform). The imaging quality and reliability of different algorithms are compared and evaluated through the signal-to-noise ratio (SNR) and POD assessment using a hit/miss method, respectively. The SNR comparison and POD assessment results indicate that the ACC time-slice algorithm is more suitable for C/GFRP delamination defect inspection using BTWRT in view of the optimal detectability and reliability. This work provides a valuable reference for BTWRT inspection of hybrid C/GFRP subsurface defects, and the ACC time-slice imaging algorithm has the potential to develop into a highly reliable vision inspection method for the industrial applications out of laboratory.

The necessity of studying the influence of the transformation of the frequency mismatch function of a coherent bundle of radio pulses on the quality of solving the radar frequency resolution problem is substantiated. This solution determines the effectiveness of radar observation of high-speed and maneuvering individual and group aerodynamic objects. The method is based on explicit expressions for calculating the normalized frequency mismatch function of a coherent bundle of radio pulses, taking into account its transformation due to the radial motion of high-speed and maneuvering individual and group aerodynamic objects. The estimation of the potential frequency resolution of bundles with different numbers of radio pulses with typical parameters for a coherent pulse radar is carried out. Possible values of frequency resolution under the additive effect of uncorrelated internal noise of the radar receiver and the multiplicative effect of correlated phase fluctuations of the radar signal are estimated. With an insignificant multiplicative effect of correlated phase fluctuations, a twofold increase in the number of radio pulses in a bundle provides an improvement in the frequency resolution (reduction of the width of the normalized frequency mismatch function) by 100 %. With the predominant multiplicative effect of these fluctuations, a twofold increase in the number of radio pulses results in an improvement in the frequency resolution by about 40 %. The developed method is of great theoretical and practical importance for the further development of the radar theory of high-speed and maneuvering individual and group aerodynamic objects.

The method of determining coordinates of an airborne object using ADS-B receivers was improved. The method involves the following sequence of actions: input of initial data, measurement of coordinates of the airborne object by the radar, checking the availability of data about the airborne object obtained from the ADS-B receivers. In the absence of such data, coordinates of the airborne object are determined only from the data of the radar. The airborne object mark was identified according to information from the radar and the ADS-B receivers. Unlike the known methods, the advanced method of determining coordinates of an airborne object by a radar additionally uses information from the ADS-B receivers. The ADS-B receiver signals were experimentally studied. It was found that the ADS-B receiver has received more than 6,000 messages about airborne objects in a single day. It was established that information about the location of the airborne object contained in ADS-B messages was encoded in CPR format. An algorithm for decrypting the ADS-B messages with a global connection of an airborne object to geographical coordinates was presented. An algorithm for detecting signals of onboard transponders of ADS-B airborne objects was presented. Non-standard ADS-B messages from airborne objects were studied. It was suggested that some short non-standard ADS-B messages were received from small and military airborne objects. Accuracy of determining coordinates of airborne objects by the radar with additional use of the ADS-B receiver was estimated. Dependence of the root mean square error of determining the airborne object coordinates on a distance to the airborne object was presented for various cases. It was established that the accuracy of determining the airborne object coordinates can be raised from 36% to 67% depending on the distance to the airborne object

This paper is concerned with the distributed ${H} _{\infty }$ -consensus filtering problem on attitude tracking over a radar filter network subject to switching topology and random packet dropouts occurring in the data transmission from both the Sun sensor and the filters. Since ground-based radars cannot directly measure the satellite attitude, a Sun sensor is deployed at the satellite side and its measurements are transmitted to radar filters through different network communication channels while suffering from random packet dropouts with different probabilities. In the radar filter network, each radar filter receives data not only from the Sun sensor but also from its local neighboring radar filters in accordance with a switching network topology. A delicate distributed ${H} _ {\infty}$ -consensus filtering algorithm, which incorporates the effects of switching network topology and random packet dropouts, is adopted to estimate attitude and attitude-rate. The algorithm guarantees ${H} _ {\infty }$ -consensus attenuation performance for the estimation deviations among radar filters, and the robustness against the switching network topology and packet dropouts for the radar filter network. The illustrative examples are given to verify the effectiveness of the proposed distributed ${H} _ {\infty}$ -consensus filtering algorithm.

Various kinds of data are generated from industrial Internet of Things, and these data can be applied for connecting production equipment, identifying and locating items, etc. These data should be forwarded to the decision center for further analyses, especially in wartime. Thus, the channel status information (CSI) for industrial big data transmission has to be acquired. In this article, we develop a system architecture for industrial big data (BD) transmission based on radar-communication integration with arbitrary geometrical array. The traditional channel estimation method, which usually utilizes the regular antenna array to estimate the CSI, cannot be applied to the arbitrary geometrical array. Here, we use the manifold separation technique to transform the complex array configuration into regular array and the downlink channel covariance matrix is estimated by exploiting the frequency calibration technique when the uplink channel covariance matrix is received. The computational complexity for the proposed method and other state-of-the-art methods are analyzed. The simulation results prove that the proposed method can achieve excellent estimation performance for its application in radar-communication integration.

General structure of a compensator of a direct penetrating signal in the diversed reception channel was developed. It is advisable to use the antenna and the receiver of the auxiliary diverted reception channel as an auxiliary antenna and an auxiliary channel. To be able to suppress the penetrating signal in the band of the receiving device of the surveillance radar, distance between the antennas should be up to 6m. In general, the compensator of the penetrating signals should contain an adder in which the signals received by the main channel are added with the signals received by the auxiliary channel and sent through the amplifier with a corresponding complex transmission coefficient. The direct penetration signal compensator features the obligatory condition of adjusting the value of the complex transmission coefficient of the auxiliary channel signal amplifier. The direct penetration signal compensator is digital and uses the direct method of forming weighting coefficients without the use of feedback. To reduce the time of formation of weighting coefficients when using direct methods of calculation of the correlation matrix, the technology of parallel computational processes was used. The quality of operation of the direct penetrating signal suppression system in the diverted reception channel was evaluated. It was established that without the use of suppression of direct penetrating signals, their powerful response at the output of the matched filter mask weak echo signals. When using a direct penetrating signal in the main channel of the compensator, its response at the output of the matched filter is significantly reduced. This makes it possible to observe weak echoes against the background of a strong penetrating signal. The use of the developed direct penetrating signal compensator provides suppression of the direct penetrating signal from 57dB to 70dB

The necessity of estimating the decrease in the accuracy of measuring the informative parameters of aradar signal in real conditions of its propagation and reflection has been substantiated. The results of the estimation determine the requirements for optimizing this measurement to achieve the required efficiency. A numerical analysis of the decrease in the accuracy of measuring the Doppler frequency of a coherent packet is presented, depending on the statistical characteristics of fluctuations of the initial phases of itsradio pulses. Expressions are given for calculating the fluctuation component of the measurement error ofradio pulse packet frequency for various coefficients of interpulse correlation of phase fluctuations. An assessment is made of the possibility of increasing the accuracy of Doppler frequency measurement, which can be ensured by statistical optimization of the algorithm fortime-frequency processing of a givenradar signal by taking into account its phase fluctuations. The conditions for the multiplicative influence of phase fluctuations ofradio pulses of the received packet are substantiated, which determine the efficiency of optimization of Doppler frequency measurement. Based on the results of the study, an optimization method for measuring the Doppler frequency of the packet taking into account fluctuations in the initial phases of itsradio pulses is proposed. The accuracy of Doppler frequency measurement under the influence of both the internal noise of theradar receiver and the correlated phase fluctuations of itsradio pulses is estimated. The efficiency of optimization of measuring the Doppler frequency of the packet is estimated taking into account fluctuations of the initial phases of itsradio pulses by means of computer simulation. It is proved that, under the influence of phase fluctuations, the accuracy of Doppler frequency measurement can be increased due to the performed optimization from 1.86 to 6.29times. This opens the way to improving the existing algorithms for measuring the highertime range derivatives to improve the quality of tracking complex maneuvering aerodynamic objects. This explains the importance and usefulness of the work for theradar theory.

Reconfigurable intelligent surface (RIS) has been considered in wireless communication systems to improve spectral efficiency. This letter addresses the spectrum sharing problem between multiple-input-multiple-output (MIMO) radar and multi-user MISO communication systems, where RIS is exploited to deal with the interference imposed by the base station (BS). We aim to maximize the radar detection probability by jointly optimizing the BS transmit beamformer and RIS phase shift matrix, subject to the users’ signal-to-interference-plus-noise-ratio and power consumption constraints. Due to the coupled variables, the formulated problem is challenging to solve; hence, we convert it into a better tractable form and propose an alternating algorithm to handle the resulting problem. The simulation results validate the effectiveness of RIS in controlling interference and improving radar detection probability.

Dual-function radar-communications (DFRC) system has been recognized as a promising solution to alleviate the radio frequency spectrum congestion and the shortage of spectrum resources. In this article, we address the problem of designing the joint subcarrier selection and power allocation scheme to minimize the power consumption of a DFRC system, under a general scenario in which the DFRC system is capable of performing a primary radar purpose and a secondary communications purpose in the meantime. In particular, the key mechanism is to minimize the total radiated power of the multicarrier DFRC system by jointly selecting the best possible subcarriers for radar and communications purposes in sequence and allocating the optimal power resource on the corresponding subcarriers, under the constraints of a predefined mutual information for target characterization and a desired communications data rate for information transmission. The resulting problem is formulated as a two-variable nonconvex optimization problem, one for subcarrier selection and the other for power allocation. Then, after convex relaxation reformulation and problem partition, an efficient three-step solution technique is developed for the joint optimization scheme, which combines the cyclic minimization algorithm and Karush–Kuhn–Tuckers optimality conditions. Finally, numerical results are provided to validate the theoretical findings and to verify the effectiveness of the proposed joint optimization scheme.

The article presents a multiple-input–multiple-output (MIMO) radar system for which transmit-receive beamforming in conjunction with a compact frequency domain representation of the radar signals yields a sparse representation of the radar scene. Compressed sensing (CS) methods are then applied to design the radar waveforms for detecting the presence within the radar scene of multiple extended targets with known impulse responses, as well as for designing the receiver filters to reconstruct the radar scene. The detection performance of the MIMO radar system based on noisy scene reconstruction is illustrated with numerical examples obtained from simulations. These show that the receiver operating characteristic curves obtained using the MIMO radar waveforms and receiver filters designed based on CS methods outperform those obtained when statically defined waveforms and receiver filters are used for target detection in the reconstructed scene.

In this article, we aim to study radar power allocation problem in multiple maneuvering targets tracking from a new perspective by deep learning, to develop a data-driven approach, which enables radar to learn a good (hopefully the best) power allocation policy from data. In order to enhance the utilization effectiveness of radar power resource, we present a cognitive design based on control framework incorporating an actor-critic variation of deep reinforcement learning called deep deterministic policy gradient. Specifically, at each tracking interval, the transmit power is allocated with control framework based on prior information, which is represented by the Fisher information matrix (FIM) of prior information of target's state computed using target's dynamic models. However, as targets are maneuvering, the dynamic models are always unavailable. Thanks to the sequential manner of target tracking, we consider to integrate a memory based on recurrent neutral network into the control framework for the estimation of FIM of prior information by characterizing the output as parameters for a high-dimensional Gaussian distribution of target's state instead of a deterministic state. Theoretical analysis has established that the derived covariance matrix is essentially the inverse of the FIM of prior information. Simulation results have shown the effectiveness of the proposed power allocation method.

This article presents a new alternative for data resource, by applying the proposed methods of Principal Components Analysis (PCA) or of Discrete Wavelet Transformation (DWT) on the VV and VH polarization images of the Sentinel-1 radar satellite, aiming at a better classification of data. The study area concerns the Houareb site located in the city of Kairouan in central Tunisia. In addition to Sentinel-1 data, field truth data and the Euclidian Minimum Distance (EMD) criterion were used for classification and validation. Energy descriptors have been proposed in this study for classifications. Cross validation was used to evaluate the results of the classification. The best classification result was achieved using the DWT method applied on VH and VV images with an Overall Precision (OA) of 0.671 and 0.548, respectively, against an OA value of 0.371 and of 0.449 when the PCA method and the Minimum Distance (MDist) classifier were applied on the dual (VV; VH) polarization, respectively. The DWT transformation gives the highest Kappa Precision Coefficient (KPC) of 0.8.

This paper proposes a new indoor people detection and tracking system using a millimeter-wave (mmWave) radar sensor. Firstly, a systematic approach for people detection and tracking is presented—a static clutter removal algorithm used for removing mmWave radar data’s static points. Two efficient clustering algorithms are used to cluster and identify people in a scene. The recursive Kalman filter tracking algorithm with data association is used to track multiple people simultaneously. Secondly, a fast indoor people detection and tracking system is designed based on our proposed algorithms. The method is lightweight enough for scalability and portability, and we can execute it in real time on a Raspberry Pi 4. Finally, the proposed method is validated by comparing it with the Texas Instruments (TI) system. The proposed system’s experimental accuracy ranged from 98% (calculated by misclassification errors) for one person to 65% for five people. The average position errors at positions 1, 2, and 3 are 0.2992 meters, 0.3271 meters, and 0.3171 meters, respectively. In comparison, the Texas Instruments system had an experimental accuracy ranging from 96% for one person to 45% for five people. The average position errors at positions 1, 2, and 3 are 0.3283 meters, 0.3116 meters, and 0.3343 meters, respectively. The proposed method’s advantage is demonstrated in terms of tracking accuracy, computation time, and scalability.

In this paper, we obtain the basic configuration of the left-hand circular polarization (LHCP) array two patches stack triangular truncated microstrip antenna. This construction use the basic corporate feed microstrip-line with modified lossless T-junction power divider on radiating patch for circularly polarized-synthetic aperture radar (CP-SAR) sensor embedded on airspace with compact, small, and simple configuration. The design of Circular Polarization (CP) is realized by truncating the whole three tips and adjusting the parameters of antenna at the resonant frequency, f=5.2 GHz. The results of characteristic performance and S-parameter for the LHCP array two patches stack antenna at the resonant frequency show successively about 7.24 dBic of gain, 1.99 dB of axial ratio (Ar), and -11.43 dB of S-parameter. Moreover, the impedance bandwidth and the 3 dB-Ar bandwidth of this antenna are around 560 MHz (10.77%) and 50 MHz (0.96%), respectively.

In this paper, a real-time Monte Carlo localization (RT_MCL) method for autonomous cars is proposed. Unlike the other localization approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution. The RT_MCL method is based on the fusion of lidar and radar measurement data for object detection, a pole-like landmarks probabilistic map and a tailored particle filter for pose estimation. The lidar and radar are fused using the unscented Kalman filter (UKF) to provide pole-like static-object pose estimations that are well suited to serve as landmarks for vehicle localization in urban environments. These pose estimations are then clustered using the grid-based density-based spatial clustering of applications with noise algorithm to represent each pole landmark in the form of a source-point model to reduce computational cost and memory requirements. A reference map that includes pole landmarks is generated offline and extracted from a 3-D lidar to be used by a carefully designed particle filter for accurate ego-car localization. The particle filter is initialized by the fused GPS + IMU measurements and used an ego-car motion model to predict the states of the particles. The data association between the estimated landmarks by the UKF and that in the reference map is performed using the iterative closest point algorithm. The RT_MCL is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Extensive simulation studies have been carried out to evaluate the performance of the RT_MCL in both longitudinal localization and lateral localization.

The paper briefly overviews the development of unmanned surface ship technology over the last 20 years. Main problems of their navigation equipment and algorithms, and their compliance with the International Maritime Organization requirements are discussed.

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.

The use of microwave and mm-wave imaging (MMI) is gaining significant growth due to the developments of fast and robust imaging algorithms that resonate well with the recent cost-effective hardware developments. Specifically, the combination of frequency modulated continuous wave (FMCW) radar modules and synthetic aperture radar (SAR) has been recently considered as an effective, fast, and robust near-field three-dimensional (3D) imaging technique. In this paper, we prove that two of the most recent processing developments based on the back-propagation (BP) concept and delay and sum (DAS) concept, within the context of FMCW radar, are equivalent. We also compare the performance of these techniques with the one based on the range migration (RM) algorithm using a quantitative measure called structural similarity index (SSIM).

In this letter, we consider the problem of increasing the spectral efficiency of a chirp-based communication system. Existing radar systems employ non-overlapping chirps with a large time-bandwidth product, and a modulation of these chirps for communications systems results in low spectral efficiency. Chirp overlapping can enhance the data rate, but it suffers from performance degradation due to intersymbol interference (ISI). In this letter, we derive a useful bit error rate (BER) expression to analyze the performance of a system with a finite number of overlapped chirps. Additionally, we investigate the conditions in which the ISI can be reduced while approaching the Nyquist signaling rate. We then propose simple linear equalization techniques to compensate for the ISI, allowing us to achieve faster-than-Nyquist signaling. We demonstrate the effectiveness of our approach by extensive analytical and numerical results.

With the widespread application of millimeter-wave (mmWave) radar technology in modern vehicles, vehicle-to-vehicle radar interference becomes a significant concern in exploiting the benefits of mmWave radar. This letter studies the radar mutual interference among vehicles on a practical bi-directional multi-lane road. Particularly, the fluctuation of the target radar cross-section (RCS) is modeled by Swerling I model and Chi-square model. Closed-form expression for successful ranging probability that works as the radar ranging performance metric is obtained by applying the stochastic geometry. Compared with constant RCS model in previous literature, the proposed performance metric based on random RCS models can provide more precise and intuitive results. The simulation results show that the increasing ranging distance and intensity of vehicles lead to a sharp decrease of the successful ranging probability.

Given the rapid development in radar detective systems and wireless communication technology, high-efficiency microwave absorbers have attracted more and more attention. Electromagnetic absorption ...

The problem of adaptive orthogonal frequency division multiplexing (OFDM) waveform design in the case of stochastic extended targets in the presence of strong and nonstationary interferences is an essential part of a cognitive radar system. In this article, a new approach is developed based on maximizing the mutual information between the random target impulse response and the received echoes with respect to the transmitted waveform. We use the advantage of OFDM signals and present an adaptive structure to match the complex weights of OFDM signal with the radar scene for making complete use of the transmission power. The interference reflections are utilized rather than eliminating them to design the weights of the transmitted OFDM signal. The optimization algorithm for designing transmitted signals is based on adaptive learning whose essential information is obtained by a feedback between receiver and transmitter. This feedback includes essential target and interference characteristics which extracted from the reflected echoes. Based on changes in interference features, the weights of the transmitted OFDM signal on each subcarrier are designed and transmitted in the next coherent processing interval. Experimental results reveal that the proposed approach achieves a higher rate of performance compared to nonadaptive waveform design approaches.

The study of sea clutter reflectivity plays an important role in radar performance evaluations in the military industry. The industrial bodies are trying to apply the sea clutter intelligent processing technology to the radar system with the form of Internet of Things and Industry 4.0. Many sea clutter reflectivity models that have been proposed are difficult to fully adapt to the surrounding seas and different radar systems in China. This article proposes a model named multi-source input neural network (MSINN) sea clutter model using sea clutter collected by ultra high frequency (UHF) radar. In order to prepare sea clutter reflectivity data for training MSINN, the radar continuously collects sea clutter containing various disturbances. In the face of the challenges of preprocessing and storage of measured sea clutter big data, this article proposes a sea clutter preprocessing scheme based on yolov3-tiny model. Experimental results show that the average detection precision of test sea clutter Range-Pulse (RP) images is 75.3% and the effective region extraction time of sea clutter RP image is 0.003642 s, which can meet the requirement of real-time detection and data requirement of predicting sea clutter reflectivity based on MSINN. Compared with the traditional empirical model, the average prediction error of sea clutter reflectivity based on MSINN is 1.82 dB, which improves the prediction accuracy and is more suitable for the Yellow Sea in China.

In this article, a low-energy chirp-pulsed radar thermography (CP-RT) is used to detect the subsurface delamination of carbon fiber reinforced polymer (CFRP) composite as nondestructive testing and evaluation (NDT&E) techniques. The CFRP specimen with artificial flat-bottom holes (FBHs) is prepared for NDTandE by chirp-pulsed radar thermography. Two lasers are employed to be external excitation heat sources. The laser intensities are modulated according to a chirp-pulsed radar signal that combines linear frequency modulation and pulse excitation, and the temperature rise is controlled within 2 °C in the experiment. The thermal-wave response signal is processed by a series of different postprocessing characteristic extraction algorithms. These algorithms include time–frequency algorithms [crosscorrelation algorithm (CC), fast Fourier transform (FFT), and dual-orthogonal demodulation algorithm (DOD)] and statistical analysis approaches [principal component analysis (PCA) and PCA-based reconstructed independent component analysis (PCA-RICA)]. The signal-to-noise ratio (SNR) of defects is employed to evaluate the defect detectability for different size defects by different postprocessing algorithms. A three-dimensional (3-D) tomography method based on the FFT phase characteristic is proposed. A truncated-correlation photothermal tomography based on DOD is also introduced to enable the 3-D tomography of CFRP specimen. The FFT phase presents a relatively high SNR and has good correlation with the depth of the defect. The FFT-based CP-RT has the potential to provide a rapid NDT&E and 3-D tomography approach for CFRP with subsurface defects under the low-energy excitation condition.

Ionospheric heights provided by ionosondes are vital for over-the-horizon radar (OTHR) target tracking. However, biases contained in the provided ionospheric heights definitely cause degradation of the tracking performance. This paper is concerned with the joint estimation problem of the target state and the ionospheric height bias for OTHR target tracking subject to multipath and cluttered measurements. A Markovian jump ionospheric height bias model is presented by simultaneously considering the intermittent and abrupt ionosphere changes. Meanwhile, a set of stochastic variables are adopted to depict association uncertainties among measurements, clutters, and propagation modes so that association is embedded in the resultant measurement model with random coefficients. Through such modeling transformation from association uncertainties to parameter randomness, the coupling processing of data association and state estimate is equivalent to pure state estimate subject to stochastic parameters. Furthermore, an optimal linear joint estimator containing causality constraints is developed in the minimum mean-squared error sense, and further extended to the case of nonlinear measurement model via iterative optimization. A target tracking example with four resolvable propagation modes illustrates the effectiveness of the proposed estimation scheme.

Recently, radar frequency bands have been considered as promising candidates for cognitive radio (CR) owing to their low utilization. From a temporal access perspective, a substantial number of spectral opportunities could be exploited if CR users could achieve rotational synchronization with radar. However, to the best of our knowledge, no research has been focused on this synchronization issue. In this article, we propose a rotational synchronization scheme between radar and CR networks to opportunistically access the radar band. First, we propose a framed structure with sensing and transmission phases reflecting the rotational characteristics of radar. Second, we provide a cooperative synchronization algorithm. To determine the appropriate spectrum sensing timing in the proposed frame, we use a statistical approach via maximum a posteriori estimation considering the sensing abilities of CR users. We analyze the synchronization error of our proposed scheme via simulations.

In this paper, a neural tuning technique is proposed and applied to a fractional-order proportional-integral-derivative (FOPID) controller. The proposed controller is applied to a radar-guided missile which is used for tracking high-speed moving targets in defense systems. The proposed neural tuning along with $\text{H}_{2}\text{/} \text{H}_{\infty }$ optimization method is intended to improve the tracking performance of the proportional navigation (PN) system and the stability of the missile trajectory during flight time. Due to the coupled and nonlinear dynamics of the considered missile, we propose a new control structure that integrates the FOPID controller into the PN system of the missile which results in better stability properties for the missile. In order to tune the FOPID, we propose a neural tuning technique that starts with a genetic algorithm-based optimizer and continues with a neural network-based scheme. This speeds up finding a near-optimal solution and refining it effectively. Then, $\text{H}_{2}\text{/} \text{H}_{\infty }$ optimization process is applied within the neural tuning technique to achieve better stability and tracking performance for the missile during the whole flight time. The proposed controller is compared with the standard PID controller tuned by the conventional Ziegler–Nichols (ZN) tuning method as well as particle swarm optimization (PSO) method, and the simulation results proved the superiority of the proposed tuning method over ZN- and PSO-based tuning approaches.

This paper proposes an integrated communication and localization system scheme using orthogonal frequency division multiplexing (OFDM) chirp waveform. A new and efficient generation method is proposed to produce the OFDM-chirp waveform. The OFDM-chirp waveform is quantitatively compared with conventional OFDM waveform with respect to complementary cumulative distribution function of the peak-to-average power ratio. The OFDM-chirp waveform is analyzed by the ambiguity function together with its matched filtering performance. Furthermore, we implemented the OFDM-chirp waveform for integrated communication and localization with the universal software radio peripheral platform. Experimental results show that it outperforms conventional OFDM waveform for integrated communication and localization applications.

We propose a novel subsurface pipeline mapping and 3D reconstruction method by fusing ground-penetrating radar (GPR) scans and camera images. To facilitate the simultaneous detection of multiple pipelines, we model the GPR sensing process and prove hyperbola response for general scanning with nonperpendicular angles. Furthermore, we fuse visual simultaneous localization and mapping outputs, encoder readings with GPR scans to classify hyperbolas into different pipeline groups. We extensively apply the J-linkage method and maximum likelihood estimation with error analysis to improve algorithm robustness and accuracy. As a result, we optimally estimate the radii and locations of all pipelines. We have implemented our method and tested it in physical experiments with representative pipeline configurations. Two different kinds of 3-m-long pipes are used, with radii being 4.62 and 3.02 cm, respectively. The results show that our method successfully reconstructs all subsurface pipes. Moreover, the average estimation errors for two orientation angles of pipelines are 1.73° and 0.73°, respectively. The average localization error is 4.47 cm. Note to Practitioners —Automatic and accurate underground pipeline mapping technology is very important in civil construction projects. Lack of 3D utility pipeline maps may lead to accidental damage in civil construction and maintenance. Although ground-penetrating radar (GPR-based pipeline mapping methods have been studied for several years, these methods require the perpendicular scanning with respect to the pipe, which is impossible to guarantee in practice since the orientations of pipelines are unknown. Furthermore, these traditional methods can only estimate one pipeline at a time in a survey area and require prior knowledge of pipe diameter. We propose a robotic subsurface pipeline mapping method with a GPR and a camera to handle difficult factors such as multiple pipes, unknown pipeline orientation, and unknown pipeline diameters. Hence, we can perform GPR scanning along any generic linear trajectories. Our method has been tested in physical experiments with representative pipeline configurations. The results are sufficiently accurate, and it proves that our method can be an effective technology to reconstruct the underground pipelines.