sensors-logo

Journal Browser

Journal Browser

Microwave Sensors and Radar Techniques

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 37536

Special Issue Editors

Faculty of Mechatronics, Armaments and Aerospace, Military University of Technology, 00-908 Warsaw, Poland
Interests: digital signal processing; radar design and radar signal processing
Special Issues, Collections and Topics in MDPI journals
Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland
Interests: microwave techniques; noise radar technology; microwave radiometry; microwave active and passive devices design

Special Issue Information

Dear Colleagues,

Radar technology has been known for more than 100 years and it has been under permanent development for both civilian and military applications. The development of microwave technology has been encouraged by radar technology and they have both evolved together. In recent years, development trends of radar technologies can be seen, in particular, in the field of system architecture, algorithms, waveforms, signal processing, materials and new device design, new applications and practical solutions. Therefore, it is our pleasure to invite you all to contribute to this Special Issue of Sensors, the topics of interest for which include, but are not limited to, the following:

  • Radar sensors design and platform developments
  • Antenna design, modeling, and measurements
  • Microwave integrated circuits
  • Microwave measurements
  • Microwave-acoustic wave devices
  • Waveform design techniques
  • Radar signal processing
  • Algorithms for real-time radar signal processing
  • Active and passive devices
  • Automotive radar
  • Weather radar
  • Multi-sensor data fusion
  • Radar target tracking

Prof. Dr. Adam M. Kawalec
Dr. Waldemar Susek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • radar signal processing techniques
  • radar imaging
  • time-domain microwave applications
  • modern radar applications
  • multifunction radar
  • correlation processing
  • noise radar technology
  • ultra-wideband radar

Published Papers (14 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

19 pages, 1224 KiB  
Article
Lean Neural Networks for Autonomous Radar Waveform Design
by Anthony Baietto, Jayson Boubin, Patrick Farr, Trevor J. Bihl, Aaron M. Jones and Christopher Stewart
Sensors 2022, 22(4), 1317; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041317 - 09 Feb 2022
Cited by 3 | Viewed by 1805
Abstract
In recent years, neural networks have exploded in popularity, revolutionizing the domains of computer vision, natural language processing, and autonomous systems. This is due to neural networks ability to approximate complex non-linear functions. Despite their effectiveness, they generally require large labeled data sets [...] Read more.
In recent years, neural networks have exploded in popularity, revolutionizing the domains of computer vision, natural language processing, and autonomous systems. This is due to neural networks ability to approximate complex non-linear functions. Despite their effectiveness, they generally require large labeled data sets and considerable processing power for both training and prediction. Some of these bottlenecks have been mitigated by recent increased availability of high-quality data sets, improvements in neural network development software, and greater hardware support. Due to algorithmic bloat, neural network inference times and imprecision make them undesirable for some problems where fast classical algorithm solutions already exist, other classes of algorithms, such as convex optimization, with non-trivial execution times could be reduced using neural solutions. These algorithms could be replaced with light-weight neural networks, benefiting from their high degree of parallelization and high accuracy when properly trained. Previous work has explored how low size, weight, and power (low SWaP) neural networks and neuromorphic computing can be used to improve autonomous radar waveform design techniques that currently rely on convex optimization. Autonomous radar waveform design helps meet the need for interference mitigation caused by an ever-growing number of consumer and commercial technologies which pollute the radio frequency (RF) spectrum. Spectral notching, a radar waveform design technique, augments transmitted radar waveforms to avoid frequencies with excessive interference while maintaining the integrity of the waveform. In this paper, we extend that work, demonstrating that lean neural networks and specialized hardware can improve inference time for waveform design without sacrificing accuracy. Our lean neural solution incorporates problem-specific information into the layer structures and loss functions to decrease network size and improve accuracy. We provide model outcomes implemented on radio frequency system on a chip (RFSoC) hardware that support our simulation results. Our neural network solution decreases inference time on traditional CPU hardware by 1057× and on GPU hardware accelerators by 883× while maintaining 99% cosine similarity. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

18 pages, 6134 KiB  
Article
Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition
by Haitao Wang, Xiaoyong Lyu and Kefei Liao
Sensors 2022, 22(1), 117; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010117 - 24 Dec 2021
Cited by 5 | Viewed by 2464
Abstract
Passive radars based on long-term evolution (LTE) signals suffer from sever interferences. The interferences are not only from the base station used as the illuminator of opportunity (BS-IoO), but also from the other co-channel base stations (CCBS) working at the same frequency with [...] Read more.
Passive radars based on long-term evolution (LTE) signals suffer from sever interferences. The interferences are not only from the base station used as the illuminator of opportunity (BS-IoO), but also from the other co-channel base stations (CCBS) working at the same frequency with the BS-IoO. Because the reference signals of the co-channel interferences are difficult to obtain, cancellation performance degrades seriously when traditional interference suppression methods are applied in LTE-based passive radar. This paper proposes a cascaded cancellation method based on the spatial spectrum cognition of interference. It consists of several cancellation loops. In each loop, the spatial spectrum of strong interferences is first recognized by using the cyclostationary characteristic of LTE signal and the compressed sensing technique. A clean reference signal of each interference is then reconstructed according to the spatial spectrum previously obtained. With the reference signal, the interferences are cancelled. At the end of each loop, the energy of the interference residual is estimated. If the interference residual is still strong, then the cancellation loop continues; otherwise it terminates. The proposed method can get good cancellation performance with a small-sized antenna array. Theoretical and simulation results demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

22 pages, 7135 KiB  
Article
Low Correlation Interference OFDM-NLFM Waveform Design for MIMO Radar Based on Alternating Optimization
by Tianqu Liu, Jinping Sun, Qing Li, Zhimei Hao and Guohua Wang
Sensors 2021, 21(22), 7704; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227704 - 19 Nov 2021
Cited by 3 | Viewed by 1558
Abstract
The OFDM chirp signal is suitable for MIMO radar applications due to its large time-bandwidth product, constant time-domain, and almost constant frequency-domain modulus. Particularly, by introducing the time-frequency structure of the non-linear frequency modulation (NLFM) signal into the design of an OFDM chirp [...] Read more.
The OFDM chirp signal is suitable for MIMO radar applications due to its large time-bandwidth product, constant time-domain, and almost constant frequency-domain modulus. Particularly, by introducing the time-frequency structure of the non-linear frequency modulation (NLFM) signal into the design of an OFDM chirp waveform, a new OFDM-NLFM waveform with low peak auto-correlation sidelobe ratio (PASR) and peak cross-correlation ratio (PCCR) is obtained. IN-OFDM is the OFDM-NLFM waveform set currently with the lowest PASR and PCCR. Here we construct the optimization model of the OFDM-NLFM waveform set with the objective function being the maximum of the PASR and PCCR. Further, this paper proposes an OFDM-NLFM waveform set design algorithm inspired by alternating optimization. We implement the proposed algorithm by the alternate execution of two sub-algorithms. First, we keep both the sub-chirp sequence code matrix and sub-chirp rate plus and minus (PM) code matrix unchanged and use the particle swarm optimization (PSO) algorithm to obtain the optimal parameters of the NLFM signal’s time-frequency structure (NLFM parameters). Next, we keep current optimal NLFM parameters unchanged, and optimize the sub-chirp sequence code matrix and sub-chirp rate PM code matrix using the block coordinate descent (BCD) algorithm. The above two sub-algorithms are alternately executed until the objective function converges to the optimal solution. The results show that the PASR and PCCR of the obtained OFDM-NLFM waveform set are about 5 dB lower than that of the IN-OFDM. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

16 pages, 3136 KiB  
Article
SFCW Radar with an Integrated Static Target Echo Cancellation System
by Danijel Šipoš and Dušan Gleich
Sensors 2021, 21(17), 5829; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175829 - 30 Aug 2021
Cited by 3 | Viewed by 3437
Abstract
Continuous Wave (CW) radars systems, especially air-coupled Ground-Penetrating Radar (GPR) or Through-Wall Imaging Radar (TWIR) systems, echo signals reflected from a stationary target with high energy, which may cause receiver saturation. Another effect caused by reflection of stationary targets is noticeable as background [...] Read more.
Continuous Wave (CW) radars systems, especially air-coupled Ground-Penetrating Radar (GPR) or Through-Wall Imaging Radar (TWIR) systems, echo signals reflected from a stationary target with high energy, which may cause receiver saturation. Another effect caused by reflection of stationary targets is noticeable as background within a radargram. Nowadays, radar systems use automatic gain control to prevent receiver saturation. This paper proposes a method to remove stationary targets automatically from the received signal. The method was designed for a radar system with a moving platform, with an assumption that the distance between the surface and target is constant. The design is proposed of an SFCW radar with an integrated system for real-time multiple static target Echo Cancellation (EC). The proposed EC system removes the static target using active Integrated Circuit (IC) components, which generate the corresponding EC signal for each frequency step of the SFCW radar and sum it with the received echo signal. This has the main advantage of removing even multiple echoes at any distance, and excludes the need for a high-dynamic-range receiver. Additionally, the proposed system has minimal impact on the radar size and power consumption. Besides static target removal, the antenna coupling can be removed if the signal appears to be constant. The operating frequency was selected between 500 MHz and 2.5 GHz, due to the limitation of the used electronic components. The experimental results show that the simulated target’s echo using a cable with a known length could be suppressed to up to 38 dB. Experimental results using a moving radar platform and the real environment scenario with static and dynamic targets, show that the proposed EC system could achieve up to 20 dB attenuation of the static target. The system does not affect any other target of interest, which can even move at any distance during the measurement. Therefore, this could be a promising method for further compact implementation into SFCW radars, or any other radar type that generates CW single frequencies. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

20 pages, 2759 KiB  
Article
Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study
by Hubert Milczarek, Czesław Leśnik, Igor Djurović and Adam Kawalec
Sensors 2021, 21(8), 2840; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082840 - 17 Apr 2021
Cited by 19 | Viewed by 3036
Abstract
Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving [...] Read more.
Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

17 pages, 5262 KiB  
Article
Through-Wall UWB Radar Based on Sparse Deconvolution with Arctangent Regularization for Locating Human Subjects
by Artit Rittiplang and Pattarapong Phasukkit
Sensors 2021, 21(7), 2488; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072488 - 03 Apr 2021
Cited by 5 | Viewed by 2703
Abstract
A common problem in through-wall radar is reflected signals much attenuated by wall and environmental noise. The reflected signal is a convolution product of a wavelet and an unknown object time series. This paper aims to extract the object time series from a [...] Read more.
A common problem in through-wall radar is reflected signals much attenuated by wall and environmental noise. The reflected signal is a convolution product of a wavelet and an unknown object time series. This paper aims to extract the object time series from a noisy receiving signal of through-wall ultrawideband (UWB) radar by sparse deconvolution based on arctangent regularization. Arctangent regularization is one of the suitably nonconvex regularizations that can provide a reliable solution and more accuracy, compared with convex regularizations. An iterative technique for this deconvolution problem is derived by the majorization–minimization (MM) approach so that the problem can be solved efficiently. In the various experiments, sparse deconvolution with the arctangent regularization can identify human positions from the noisy received signals of through- wall UWB radar. Although the proposed method is an odd concept, the interest of this paper is in applying sparse deconvolution, based on arctangent regularization with an S-band UWB radar, to provide a more accurate detection of a human position behind a concrete wall. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

18 pages, 3293 KiB  
Article
Frequency Domain Panoramic Imaging Algorithm for Ground-Based ArcSAR
by Yun Lin, Yutong Liu, Yanping Wang, Shengbo Ye, Yuan Zhang, Yang Li, Wei Li, Hongquan Qu and Wen Hong
Sensors 2020, 20(24), 7027; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247027 - 08 Dec 2020
Cited by 8 | Viewed by 1784
Abstract
The ground-based arc-scanning synthetic aperture radar (ArcSAR) is capable of 360° scanning of the surroundings with the antenna fixed on a rotating arm. ArcSAR has much wider field of view when compared with conventional ground-based synthetic aperture radar (GBSAR) scanning on a linear [...] Read more.
The ground-based arc-scanning synthetic aperture radar (ArcSAR) is capable of 360° scanning of the surroundings with the antenna fixed on a rotating arm. ArcSAR has much wider field of view when compared with conventional ground-based synthetic aperture radar (GBSAR) scanning on a linear rail. It has already been used in deformation monitoring applications. This paper mainly focuses on the accurate and fast imaging algorithms for ArcSAR. The curvature track makes the image focusing challenging and, in the classical frequency domain, fast imaging algorithms that are designed for linear rail SAR cannot be readily applied. This paper proposed an efficient frequency domain imaging algorithm for ArcSAR. The proposed algorithm takes advantage of the angular shift-invariant property of the ArcSAR signal, and it deduces the accurate matched filter in the angular-frequency domain, so panoramic images in polar coordinates with wide swath can be obtained at one time without segmenting strategy. When compared with existing ArcSAR frequency domain algorithms, the proposed algorithm is more accurate and efficient, because it has neither far range nor narrow beam antenna restrictions. The proposed method is validated by both simulation and real data. The results show that our algorithm brings the quality of image close to the time domain back-projection (BP) algorithm at a processing efficiency about two orders of magnitude better, and it has better image quality than the existing frequency domain Lee’s algorithm at a comparable processing speed. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

17 pages, 6883 KiB  
Article
1-Tx/5-Rx Through-Wall UWB Switched-Antenna-Array Radar for Detecting Stationary Humans
by Artit Rittiplang and Pattarapong Phasukkit
Sensors 2020, 20(23), 6828; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236828 - 29 Nov 2020
Cited by 15 | Viewed by 4186
Abstract
This research proposes a through-wall S-band ultra-wideband (UWB) switched-antenna-array radar scheme for detection of stationary human subjects from respiration. The proposed antenna-array radar consists of one transmitting (Tx) and five receiving antennas (Rx). The Tx and Rx antennas are of Vivaldi type with [...] Read more.
This research proposes a through-wall S-band ultra-wideband (UWB) switched-antenna-array radar scheme for detection of stationary human subjects from respiration. The proposed antenna-array radar consists of one transmitting (Tx) and five receiving antennas (Rx). The Tx and Rx antennas are of Vivaldi type with high antenna gain (10 dBi) and narrow-angle directivity. The S-band frequency (2–4 GHz) is capable of penetrating non-metal solid objects and detecting human respiration behind a solid wall. Under the proposed radar scheme, the reflected signals are algorithmically preprocessed and filtered to remove unwanted signals, and 3D signal array is converted into 2D array using statistical variance. The images are reconstructed using back-projection algorithm prior to Sinc-filtered refinement. To validate the detection performance of the through-wall UWB radar scheme, simulations are carried out and experiments performed with single and multiple real stationary human subjects and a mannequin behind the concrete wall. Although the proposed method is an odd concept, the interest of this paper is applying the 1-Tx/5-Rx UWB switched-antenna array radar with the proposed method that is capable of distinguishing between the human subjects and the mannequin behind the concrete wall. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

14 pages, 3446 KiB  
Article
Some of Problems of Direction Finding of Ground-Based Radars Using Monopulse Location System Installed on Unmanned Aerial Vehicle
by Adam Rutkowski and Adam Kawalec
Sensors 2020, 20(18), 5186; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185186 - 11 Sep 2020
Cited by 12 | Viewed by 3579
Abstract
Locating active radars in real environmental conditions is a very important and complex task. The efficiency of the direction finding (DF) of ground-based radars and other microwave emitters using unmanned aerial vehicles (UAV) is dependent on the parameters of applied devices for angle [...] Read more.
Locating active radars in real environmental conditions is a very important and complex task. The efficiency of the direction finding (DF) of ground-based radars and other microwave emitters using unmanned aerial vehicles (UAV) is dependent on the parameters of applied devices for angle location of microwave emitters, and on the construction and modes of operation of the observed transmitting antenna systems. An additional factor having the influence on DF of the radar, when are used systems installed on the UAV, is the rotation of the antenna of a radar. The accuracy of estimation of direction of any microwave transmitter is determined by the terrain properties that surround the transmitter and the objects reflecting microwave signals. The exemplary shapes of the radar antenna patterns and the associated relationships with the probability of remotely detecting the radar and determining its bearings are described. The simulated patterns of the signals received at an emitter-locating device mounted on a UAV and the expected results of a monopulse DF based on these signals are presented. The novelty of this work is the analysis of the DF efficiency of radars in conditions where intense multi-path phenomena appear, and for various amplitudes and phases of the direct signal and multi-path signals that reach the UAV when assuming that so-called simple signals and linear frequency modulation (LFM) signals are transmitted by the radar. The primary focus is on multi-path phenomenon, which can make it difficult, but not entirely impossible, to detect activity and location of radar with a low-flying small UAV and using only monopulse techniques, that is, when only a single pulse emitted by a radar must be sufficient to DF of this radar. Direction of arrival (DOA) algorithms of signals in dense signal environment were not presented in the work, but relevant suggestions were made for the design of such algorithms. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

Other

Jump to: Research

11 pages, 21398 KiB  
Technical Note
Range Dividing MIMO Waveform for Improving Tracking Performance
by Eun-Hee Kim, Han-Saeng Kim and Ki-Won Lee
Sensors 2021, 21(21), 7290; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217290 - 02 Nov 2021
Cited by 1 | Viewed by 1237
Abstract
A multiple-input multiple-output (MIMO) method that shares the same frequency band can efficiently increase radar performance. An essential element of a MIMO radar is the orthogonality of the waveform. Typically, orthogonality is obtained by spreading different signals into divided domains such as in [...] Read more.
A multiple-input multiple-output (MIMO) method that shares the same frequency band can efficiently increase radar performance. An essential element of a MIMO radar is the orthogonality of the waveform. Typically, orthogonality is obtained by spreading different signals into divided domains such as in time-domain multiplexing, frequency-domain multiplexing, and code domain multiplexing. This paper proposes a method of spreading the interference signals outside the range bins of interest for pulse doppler radars. This is achieved by changing the pulse repetition frequency under certain constraints, and an additional gain can be obtained by doppler processing. This method is very effective for improving the angular accuracy of the MIMO radar for a small number of air targets, although it may have limitations in use for many targets or in high clutter environments. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

17 pages, 12943 KiB  
Letter
A Superfast Super-Resolution Method for Radar Forward-Looking Imaging
by Weibo Huo, Qiping Zhang, Yin Zhang, Yongchao Zhang, Yulin Huang and Jianyu Yang
Sensors 2021, 21(3), 817; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030817 - 26 Jan 2021
Cited by 6 | Viewed by 2168
Abstract
The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, [...] Read more.
The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an L1 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable L1 regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg-Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from O(N3) to O(N2). It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

13 pages, 1891 KiB  
Letter
Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework
by Ruimeng Zhang, Yan Zhang, Jinping Sun and Qing Li
Sensors 2020, 20(18), 5158; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185158 - 10 Sep 2020
Cited by 7 | Viewed by 2207
Abstract
In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation [...] Read more.
In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm’s performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

17 pages, 4043 KiB  
Letter
A New Multiple Hypothesis Tracker Using Validation Gate with Motion Direction Constraint
by Jinping Sun, Ziwei Wang and Qing Li
Sensors 2020, 20(17), 4816; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174816 - 26 Aug 2020
Cited by 4 | Viewed by 2573
Abstract
In multi-target tracking scenarios with dense and heterogeneous clutter, there is a substantial increase in the false measurements that originated from the clutter within the validation gate, and consequently, the number of measurement-to-track association hypothesis grows rapidly in traditional multiple hypothesis tracker (MHT), [...] Read more.
In multi-target tracking scenarios with dense and heterogeneous clutter, there is a substantial increase in the false measurements that originated from the clutter within the validation gate, and consequently, the number of measurement-to-track association hypothesis grows rapidly in traditional multiple hypothesis tracker (MHT), leading to a sharp decrease in data association accuracy and tracking performance. A new multiple hypothesis tracker using validation gate with motion direction constraint (MHT-MDC) is proposed to solve these problems. In the MHT-MDC, a motion direction constraint (MDC) gate is designed by considering the prior target maneuvering information, which effectively reduces the volume of validation gate and, thus, diminishes the number of false measurements in the gate when the innovation covariance is large. Subsequently, the clutter density in the MDC gate is adaptively estimated by the conditional mean estimator of clutter density (CMECD), based on which the score functions in the MDC gate can be calculated. The MHT-MDC is compared with the MHT algorithm in simulations, and the experimental results demonstrate its superior tracking performance for weakly maneuvering targets in high clutter density scenarios. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

13 pages, 4459 KiB  
Letter
An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing
by Anna Ślesicka and Adam Kawalec
Sensors 2020, 20(12), 3468; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123468 - 19 Jun 2020
Cited by 9 | Viewed by 2676
Abstract
The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input [...] Read more.
The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar geometry model and the orthogonal matching pursuit (OMP) algorithm. For the estimation of spatio-temporal spectrum of clutter and target, a model of joint sparse recovery was established. As a result, clutter suppression and target detection in a heterogeneous environment will be achieved. In addition, the proposed method uses a single snapshot of the radar data cube, which eliminates the need for access to all training cells. Full article
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
Show Figures

Figure 1

Back to TopTop