Evolution of Netted Radar Systems

Geng, Zhe

doi:10.1109/access.2020.3007774

Cited by 37 publications

(17 citation statements)

References 116 publications

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“…base stations of wireless communications systems) for target detection, imaging, and tracking, which could increase the radar coverage area while avoiding the high infrastructure cost and the spectrum-crowdedness caused by the construction of new dedicated radar transmitters. However, since the waveforms from the IOs are usually unknown to radar receivers, the performance of passive radar is usually much worse than the conventional active radar [42]. In [43]- [44], DL was used to realize simultaneous waveform estimation and image reconstruction for passive SAR composed of a ground-based IO at known position and an airborne receiver.…”

Section: B DL For Passive Radarmentioning

confidence: 99%

Deep-Learning for Radar: A Survey

Geng¹,

Zhang

et al. 2021

IEEE Access

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A comprehensive and well-structured review on the application of deep learning (DL) based algorithms, such as convolutional neural networks (CNN) and long-short term memory (LSTM), in radar signal processing is given. The following DL application areas are covered: i) radar waveform and antenna array design; ii) passive or low probability of interception (LPI) radar waveform recognition; iii) automatic target recognition (ATR) based on high range resolution profiles (HRRPs), Doppler signatures, and synthetic aperture radar (SAR) images; and iv) radar jamming/clutter recognition and suppression. Although DL is unanimously praised as the ultimate solution to many bottleneck problems in most of existing works on similar topics, both the positive and the negative sides of stories about DL are checked in this work. Specifically, two limiting factors of the real-life performance of deep neural networks (DNNs), limited training samples and adversarial examples, are thoroughly examined. By investigating the relationship between the DL-based algorithms proposed in various papers and linking them together to form a full picture, this work serves as a valuable source for researchers who are seeking potential research opportunities in this promising research field.

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Section: B DL For Passive Radarmentioning

confidence: 99%

Deep-Learning for Radar: A Survey

Geng¹,

Zhang

et al. 2021

IEEE Access

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“…Most existing studies on the centralized approach assume a PMR without using reference channels which is mainly the case of passive airborne radar, where the radar platform moves and prevents establishing a stationary direct-path between the transmitters and the receiver. In such a circumstance, the cross-correlation between the received signals of various receivers is used to detect and localize targets [11]. For example in [12], a multistatic passive radar was considered with a single transmitter and multiple receivers without reference channels.…”

Section: Introductionmentioning

confidence: 99%

Multitarget Detection in Passive MIMO Radar Using Block Sparse Recovery

2021

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In this paper, we develop a new algorithm for centralized target detection in passive MIMO radar (PMR) using sparse recovery technique. PMRs use a network of receivers and illuminators of opportunity to detect and localize targets. We consider a widely separated PMR network assuming the availability of reference channels. We first transform the collected information of all receivers to a common space and combine them to attain a unified model. The problem of target detection in the extracted model is equal to a block sparse recovery problem. Since employing the generic sparse recovery tools are impractical due to the ultra-large dimension of the sensing matrix, we exploit the structure of the involving matrices and propose a very efficient distributed algorithm which extracts all scatterers, including targets and clutter simultaneously with a unified procedure. The proposed algorithm is highly efficient, and it does not require a high bandwidth link to transfer raw data from nodes to the fusion center. Moreover, the algorithm inherently benefits from parallel processing and distributes the extensive computations among receivers. Our simulation results demonstrate that the proposed algorithm outperforms the popular PMR detection algorithm, especially in the presence of interfering targets and any strong clutter residue.INDEX TERMS Passive coherent location, passive MIMO radar, block sparse recovery, radar multitarget detection.

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“…To remedy this issue, many researchers have proposed the use of orthogonal waveforms in modern radar systems. The representative system adopting orthogonal waveforms is the Spectrum Shared Radar System (SSRS), where multiple radars in close proximity share the same frequency band as depicted in Figure 1 [ 1 , 2 , 3 ]. To estimate a target’s characteristics accurately in the SSRS, each radar must operate independently using a waveform that does not interfere with other radar signals.…”

Section: Introductionmentioning

confidence: 99%

Design of Optimized Coded LFM Waveform for Spectrum Shared Radar System

Kim

Lim

2021

Sensors

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To meet the increasing demands for remote sensing, a number of radar systems using Linear Frequency Modulation (LFM) waveforms have been deployed, causing the problem of depleting frequency resources. To address this problem, several researchers have proposed the Spectrum Shared Radar System (SSRS) in which multiple radars share the same frequency band to transmit and receive their own signals. To mitigate the interferences caused by the signal transmission by other radars, SSRS employs orthogonal waveforms that inherit the orthogonality of the waveforms from orthogonal codes. However, the inherited orthogonality of the codes is significantly reduced when incorporating LFM waveforms with the codes. To solve this problem, in this paper, we propose a novel but simple scheme for generating a set of optimized coded LFM waveforms via new optimization framework. In the optimization framework, we minimize the weighted sum of autocorrelation sidelobe peaks (ASP) and cross-correlation peaks (CP) of the coded LFM waveforms to maximize the orthogonality of the waveforms. Through computer simulations, we show that the waveforms generated by the proposed scheme outperform the waveforms created by previous proposals in terms of ASP and CP.

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Evolution of Netted Radar Systems

Cited by 37 publications

References 116 publications

Deep-Learning for Radar: A Survey

Deep-Learning for Radar: A Survey

Multitarget Detection in Passive MIMO Radar Using Block Sparse Recovery

Design of Optimized Coded LFM Waveform for Spectrum Shared Radar System

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