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Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 31489

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Prof. Dr. Dmitriy Garmatyuk

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Department of Electrical and Computer Engineering Miami University, Oxford, OH 45056, USA
Interests: cognitive radar for autonomous vehicles; UWB OFDM software-defined multi-functional radar system design and analysis; UWB OFDM SAR signal processing; adaptive radar detection in indoor scenarios; fusion of radar and communications with UWB OFDM concept; UWB radar-assisted navigation in GPS-denied environments
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Dr. Chandra Sekhar Pappu

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Electrical, Computer and Biomedical Engineering Department, Union College, Schenectady, NY 12308, USA
Interests: chaotic systems; radar signal processing; joint radar-communication systems

Special Issue Information

Dear Colleagues,

Due to an ever increasing demand for access to the electromagnetic spectrum, a significant amount of research has been focused on fusion of radar and communication systems (RadComm). Most recently, this topic has fueled a renewed interest in multi-carrier radar systems, since they present an ideal model for creating a joint RadComm design. Similarly, passive radar configurations, waveform design via e.g. orthogonal frequency division multiplexing (OFDM), noise and chaos; and even radar networks are being investigated towards the goal of RadComm. This Special Issue focuses on waveform design and architecture for RadComm systems. We welcome contributions dealing with the improved performance of the RadComm system using multi-carrier radars, exploiting spectral diversity, passive radar systems, MIMO/multi-static implementations, noise and chaotic radars, cognitive radar sensor design, and advanced signal processing applications.

Dr. Dmitriy Garmatyuk
Dr. Chandra Sekhar Pappu
Guest Editors

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Keywords

Radar-communication (RadComm) systems
Multi-carrier radar
Radar detection
Frequency diversity
Adaptive radar
OFDM
Noise and Chaotic Radars
Passive Radar Systems
Radar Networks

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Published Papers (11 papers)

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9 pages, 1388 KiB

Open AccessCommunication

Many-Objective RadarCom Signal Design via NSGA-II Genetic Algorithm Implementation and Simulation Analysis

by Richard Washington, Dmitriy Garmatyuk, Saba Mudaliar and Ram M. Narayanan

Remote Sens. 2022, 14(15), 3787; https://doi.org/10.3390/rs14153787 - 6 Aug 2022

Cited by 2 | Viewed by 1479

Abstract

In this communication, we investigate the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in many-objective optimization scenarios pertaining to joint radar and communication functionality. We introduce five objectives relevant to sensing and secure communications and develop a cost function where these objectives can be individually prioritized by a user. We consider three scenarios: Radar Priority, Communication Priority, and All (Objectives) Equal; we then demonstrate the optimization results using an orthogonal frequency-division multiplexing (OFDM) radarcom signal. The objectives with selected weights are shown to improve system performance and thereby validate the viability of our approach. The Radar Priority scenario showed the best improvement in probability of detection, PSLR, and PAPR. Compared to the baseline performance values, the improvements were: from 94.05% to 96%, from 11.7 to 13.6 dB, and from 9.46 to 7.09 dB, respectively. The communication scenario saw the best improvement in BER and clutter similarity (measured by NRMSE) from 3.52% to 0.39% and 0.87 to 0.59, respectively. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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18 pages, 6307 KiB

Open AccessArticle

Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform

by Mariam H. Abd, Ghaida A. Al-Suhail, Fadhil R. Tahir, Ahmed M. Ali Ali, Hamza A. Abbood, Kia Dashtipour, Sajjad Shaukat Jamal and Jawad Ahmad

Remote Sens. 2022, 14(9), 1984; https://doi.org/10.3390/rs14091984 - 20 Apr 2022

Cited by 9 | Viewed by 3874

Abstract

There is no doubt that chaotic systems are still attractive issues in various radar applications and communication systems. In this paper, we present a new 0.3 GHz mono-static microwave chaotic radar. It includes a chaotic system based on a time-delay to generate and process frequency modulated (FM) waveforms. Such a radar is designed to extract high-resolution information from the targets. To generate a continuous FM signal, the chaotic signal is first modulated using the voltage control oscillator (VCO). Next, the correct value for the loop gain (G) is carefully set when utilizing the Phase-Locked Loop (PLL) at the receiver, so that the instantaneous frequency that reflects a chaotic state variable can be reliably recovered. In this system, the PLL synchronization and radar correlation are enough to recover the echo signal and detect the target. The finding indicates that the system can be implemented with no need to use the complete self-synchronization or complex projective synchronization schemes as compared to the existing chaotic radar systems. The simulation results show that the short-time cross-correlation of the transmitted and reconstructed waveforms is good and satisfactory to detect the target under various signal-to-noise ratio (SNR) levels and with less complexity in the design. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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16 pages, 3341 KiB

Open AccessCommunication

Coordination of Complementary Sets for Low Doppler-Induced Sidelobes

by Jiahua Zhu, Chongyi Fan, Yongping Song, Xiaotao Huang, Bingbing Zhang and Yanxin Ma

Remote Sens. 2022, 14(7), 1549; https://doi.org/10.3390/rs14071549 - 23 Mar 2022

Cited by 9 | Viewed by 1658

Abstract

Golay complementary waveforms are, by definition, able to generate narrow pulses with low sidelobes via coherent signal processing. However, while an ideal impulse can be obtained for target returns at zero-Doppler, significant sidelobes are observed at nonzero Doppler shifts. In this paper, a Generalized Binominal Design (GBD) procedure is proposed for the waveforms consisting of complementary sets in an attempt to reduce the Doppler-induced sidelobes. Our theoretical analysis as well as simulation results show that the proposed approach performs as good as existing Binominal Design method used for sidelobe suppression with Golay complementary waveforms and can also achieve around

28 %

enhancement on the Doppler resolution, with an acceptable loss in peak to peak-sidelobe ratio (PPSR). Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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25 pages, 541 KiB

Open AccessArticle

Forward Scatter Radar Meets Satellite: Passive Sensing of Aerial Target Using Satellite Communication Waveforms

by Mingqian Liu, Zhenju Zhang, Yunfei Chen, Shifei Zheng and Jianhua Ge

Remote Sens. 2022, 14(6), 1375; https://doi.org/10.3390/rs14061375 - 11 Mar 2022

Cited by 4 | Viewed by 2717

Abstract

The problem of single-channel reception of global positioning system (GPS) communication waveforms makes passive sensing of aerial target difficult because of forward scatter. This paper proposes a novel aerial target passive sensing method based on linear canonical transformation (LCT) using the forward scattered satellite communication waveforms. The proposed method firstly preprocesses the received signal based on the characteristics of the traditional satellite tracking loop and the forward scattered satellite communication waveforms to effectively suppress the interference of the direct wave through DC removal. Then, the Gaussian noise and multipath interference in the channel are suppressed by applying a rectangular window to its linear canonical domain. Finally, aerial target sensing is performed based on the peak value of signals in the linear canonical transform domain. The characteristic signal is constructed by analyzing the satellite communication waveforms. Combining the linear canonical transform with the matched filter (MF) to estimate the target parameter. Simulation results show that the proposed method can effectively perform the aerial target sensing by using satellite communication waveforms in the forward scatter scenario. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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18 pages, 20666 KiB

Open AccessArticle

A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning

by Peng Man, Chibiao Ding, Wenjuan Ren and Guangluan Xu

Remote Sens. 2021, 13(23), 4919; https://doi.org/10.3390/rs13234919 - 3 Dec 2021

Cited by 9 | Viewed by 2718

Abstract

With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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21 pages, 1105 KiB

Open AccessArticle

Joint Radar-Communications Exploiting Optimized OFDM Waveforms

by Ammar Ahmed, Yimin D. Zhang and Aboulnasr Hassanien

Remote Sens. 2021, 13(21), 4376; https://doi.org/10.3390/rs13214376 - 30 Oct 2021

Cited by 22 | Viewed by 3208

Abstract

We propose novel Joint Radar-communication spectrum sharing strategies exploiting orthogonal frequency-division multiplexing (OFDM) waveforms that concurrently achieve the objectives of both radar and communication systems. An OFDM transmitter is considered that transmits dual-purpose OFDM subcarriers such that all the subcarriers are exploited for the primary radar function and further exclusively allocated to the secondary communication function serving multiple users. The waveform optimization is performed by employing mutual information (MI) as the optimization criterion for both radar and communication operations. For the purpose of radar performance optimization, we consider the MI between the frequency-dependent target response and the transmit OFDM waveforms. On the other hand, communication system performance is evaluated in terms of the MI between the frequency-dependent communication channels of communication users with the transmit OFDM subcarriers. These optimization objectives not only enable the transmit power allocation of the OFDM subcarriers, but also govern the subcarrier distribution among the communication users. Two resource optimization scenarios are considered, resulting in radar-centric and cooperative resource allocation strategies that exploit convex and mixed-integer linear programming optimization problems for power allocation and subcarrier distribution, respectively. We further present a chunk subcarrier allocation approach that applies to both optimization strategies to reduce the computational complexity with a trivial performance loss. Simulation results are presented to illustrate the effectiveness of the proposed strategies. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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20 pages, 1645 KiB

Open AccessArticle

Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems

by Chandra S. Pappu, Aubrey N. Beal and Benjamin C. Flores

Remote Sens. 2021, 13(20), 4113; https://doi.org/10.3390/rs13204113 - 14 Oct 2021

Cited by 19 | Viewed by 3309

Abstract

In this article, we propose the utilization of chaos-based frequency modulated (CBFM) waveforms for joint monostatic and bistatic radar-communication systems. Short-duration pulses generated via chaotic oscillators are used for wideband radar imaging, while information is embedded in the pulses using chaos shift keying (CSK). A self-synchronization technique for chaotic systems decodes the information at the communication receiver and reconstructs the transmitted waveform at the bistatic radar receiver. Using a nonlinear detection scheme, we show that the CBFM waveforms closely follow the theoretical bit-error rate (BER) associated with bipolar phase-shift keying (BPSK). We utilize the same nonlinear detection scheme to optimize the target detection at the bistatic radar receiver. The ambiguity function for both the monostatic and bistatic cases resembles a thumbtack ambiguity function with a pseudo-random sidelobe distribution. Furthermore, we characterize the high-resolution imaging capability of the CBFM waveforms in the presence of noise and considering a complex target. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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17 pages, 831 KiB

Open AccessArticle

Tensor-Based Reduced-Dimension MUSIC Method for Parameter Estimation in Monostatic FDA-MIMO Radar

by Tengxian Xu, Xianpeng Wang, Mengxing Huang, Xiang Lan and Lu Sun

Remote Sens. 2021, 13(18), 3772; https://doi.org/10.3390/rs13183772 - 20 Sep 2021

Cited by 16 | Viewed by 2501

Abstract

Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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26 pages, 1382 KiB

Open AccessArticle

Joint Design of the Transmit Beampattern and Angular Waveform for Colocated MIMO Radar under a Constant Modulus Constraint

by Hao Zheng, Bo Jiu, Kang Li and Hongwei Liu

Remote Sens. 2021, 13(17), 3392; https://doi.org/10.3390/rs13173392 - 26 Aug 2021

Cited by 10 | Viewed by 2155

Abstract

In this paper, we investigate the joint design of a transmit beampattern and angular waveform (AW) for colocated multiple-input multiple-output (MIMO) radars. The importance of the AW in the proposed signal processing strategy is first clarified, and then, two optimization models are established, which are aimed at either the power spectral density (PSD) design or the spectral compatibility and similarity design of the AW. There are two main differences between the proposed models and existing models. First, instead of matching a desired template or maximizing the transmit power on specific regions, the transmit beampattern in this paper is optimized to approach several key points, which guarantees the high transmit gain and the flexible adjustment of each beam gain. Second, instead of optimizing the performance of the transmit waveform, only the characteristics of the AW are examined, and they can be constrained quantitatively according to their relationship with the transmit gain. The two models can be unified into the same framework, and an efficient algorithm is proposed to solve the problem under a constant modulus constraint. The convergence of the proposed algorithm is demonstrated, and some improvements to reduce the computational complexity are proposed. Numerical simulations showed that compared to the existing methods, the proposed approach can be used to obtain a higher transmit gain, flexibly adjust each beam gain, and more accurately control the PSD, spectral compatibility, and similarity of the AW. Moreover, numerical simulations showed that, compared to the use of existing methods, the proposed algorithm has higher computational efficiency. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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24 pages, 804 KiB

Open AccessArticle

Robust Antijamming Strategy Design for Frequency-Agile Radar against Main Lobe Jamming

by Kang Li, Bo Jiu, Hongwei Liu and Wenqiang Pu

Remote Sens. 2021, 13(15), 3043; https://doi.org/10.3390/rs13153043 - 3 Aug 2021

Cited by 22 | Viewed by 3355

Abstract

To combat main lobe jamming, preventive measures can be applied to radar in advance based on the concept of active antagonism, and efficient antijamming strategies can be designed through reinforcement learning. However, uncertainties in the radar and the jammer, which will result in a mismatch between the test and training environments, are not considered. Therefore, a robust antijamming strategy design method is proposed in this paper, in which frequency-agile radar and a main lobe jammer are considered. This problem is first formulated under the framework of Wasserstein robust reinforcement learning. Then, the method of imitation learning-based jamming strategy parameterization is presented to express the given jamming strategy mathematically. To reduce the number of parameters that require optimization, a perturbation method inspired by NoisyNet is also proposed. Finally, robust antijamming strategies are designed by incorporating jamming strategy parameterization and jamming strategy perturbation into Wasserstein robust reinforcement learning. The simulation results show that the robust antijamming strategy leads to improved radar performance compared with the nonrobust antijamming strategy when uncertainties exist in the radar and the jammer. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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14 pages, 2920 KiB

Open AccessTechnical Note

Robust Suppression of Deceptive Jamming with VHF-FDA-MIMO Radar under Multipath Effects

by Yibin Liu, Chunyang Wang, Jian Gong, Ming Tan and Geng Chen

Remote Sens. 2022, 14(4), 942; https://doi.org/10.3390/rs14040942 - 15 Feb 2022

Cited by 13 | Viewed by 1787

Abstract

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has received a lot of attention due to the advantages of waveform diversity. Suppression of mainlobe deceptive jamming can be effectively achieved with the degree of freedom (DOF) in the range domain. However, the existing research mainly focuses on non-coherent signals. The echo signal of VHF-FDA-MIMO radar for low elevation has its own unique characteristics. False targets cannot be suppressed with conventional beamforming methods. Thus, a signal model for VHF-FDA-MIMO radar subjected to deceptive jamming is established. The reconstruction of the covariance matrix and the estimation of the steering vector are implemented with the generalized MUSIC algorithm. In addition, a matching Capon reconstruction method is proposed to finish the robust suppression of false targets for the problem of self-cancellation in the absence of a priori information. Finally, the beampattern and performance curves of different methods are compared. The simulation results show that the methods can be effectively applied to the suppression of deceptive jamming in VHF-FDA-MIMO radar under the multipath effect. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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