Passive Bistatic Radar Using Digital Terrestrial Television Broadcasting Signal for Subsurface Target Detection

Feng, Weike; Friedt, Jean‐Michel; Wang, Suyun; Liu, Hai; Suzuki, Motoyuki

doi:10.1109/piers-fall48861.2019.9021707

Cited by 3 publications

(2 citation statements)

References 12 publications

(18 reference statements)

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“…Some examples of these transmitters, which are called Illuminators of Opportunity (IOs), are digital audio broadcast (DAB) [1], Wi-Fi [2], and global system for mobile communications (GSM) [3]. Among various communication and broadcast transmitters, the FM radio [4] and digital video broadcasting-terrestrial (DVB-T) [5] are more popular IOs since they have widely spread stations with high power levels. Passive radars have gained a lot of attention recently.…”

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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Section: Introductionmentioning

confidence: 99%

Multitarget Detection in Passive MIMO Radar Using Block Sparse Recovery

2021

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“…Feng [12] established a unified model for suppressing interference. By analyzing the interference power spectrum density, the two characteristics of coherence time and coherence bandwidth were introduced, and finally, fast interference and slow interference, frequency blocking interference, and frequency selective interference were effectively separated.…”

Section: Introductionmentioning

confidence: 99%

Characteristic Parameter Optimization of Wireless Flaw Sensing System Based on Active Interference Suppression Algorithm

Wu¹

2021

Discrete Dynamics in Nature and Society

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Based on the active interference suppression algorithm, this study combines the radar working mode and the interference type and realizes the effective detection of the flaw detection signal by successively processing the radar receiving signal and the filtering processing. Firstly, this article builds a simulation platform similar to the actual situation to verify the existing conventional active interference suppression algorithms. Secondly, for the detection of chirp active deception jamming signals entering from the main lobe, a radar active deception jamming detection method based on the characteristic parameter matching of the harmonic components of active deception jamming signals is proposed. After that, the spectral characteristics of the harmonic components of the deception interference signal are analyzed, and the center frequency and the tuning frequency of the real target echo are obtained. Finally, by establishing a frequency modulation parameter library for possible interference harmonic signal components, the acquisition phase of the radar gate by the jammer matched analysis with the preestablished frequency modulation parameter library is implemented to achieve active deception interference detection. This method can effectively detect active deception jamming signals in a complex tunnel environment. The interference suppression algorithms verified by simulation include noise FM interference suppression algorithm based on cancellation and distance false target interference suppression algorithm based on LFM radar summary processing. Through actual measurement data processing and analysis, the effectiveness of the method is verified and the idea of interference suppression is expanded. The construction of the simulation platform is obtained by appropriately modifying the actual parameters, a certain type of suppression jammer, and a certain type of deception jammer used in a certain countermeasure field test at a radar station.

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