Signal Conditioning for DAB-Illuminated Passive Radar

Mazurek, Gustaw

doi:10.1109/spsympo51155.2020.9593458

Cited by 2 publications

(8 citation statements)

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“…In this paper, we continue the work presented in [ 31 ]. We begin with a short review of the essential properties of the DAB signal.…”

Section: Introductionmentioning

confidence: 60%

“…Such ambiguity peaks of the illuminating signal are generally undesirable in radar applications [ 17 , 26 , 29 ]. As shown in [ 31 ], these side peaks come from the deterministic parts of the DAB signal, i.e., the PR and FIC symbols in the frame. Therefore, the removal or substitution of these symbols improves the signal’s autocorrelation function, as will be demonstrated in the following subsection.…”

Section: Methodsmentioning

confidence: 99%

“…A method of DAB signal preprocessing was presented [ 31 ] that works in the time domain with the complex baseband DAB signal x ( n ). That method employs signal envelope measurements to detect the Null symbol, which occurs at the start of each frame.…”

Section: Methodsmentioning

confidence: 99%

“…When the signal envelope rises above the threshold in this state, the beginning of the PR symbol is detected, and the time instant of this situation (

) is stored as a reference for the subsequent operations. This method has been shown [ 31 ] to perform satisfactorily with the high-SNR signal, for example, when x ( n ) is the reference signal from the PCL receiver. However, if synchronization to noisy signals is required, more robust frame synchronization methods (e.g., based on the correlation techniques) may still be implemented [ 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…The preprocessing method for DAB signal in PCL application was described in [ 31 ]. However, the presented results were limited to only eliminating the periodic ambiguity peaks in the signal’s autocorrelation function, with no investigation into the influence on the PCL detection results.…”

Section: Introductionmentioning

confidence: 99%

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DAB Signal Preprocessing for Passive Coherent Location

Mazurek

2022

Sensors

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Digital Audio Broadcast (DAB) transmitters can be successfully used as illumination sources in Passive Coherent Location (PCL). However, extending the integration time in such a configuration leads to the occurrence of periodical artifacts in the bistatic range/Doppler plots, resulting from the time structure of the DAB signal. In this paper, we propose some methods of signal preprocessing (based on symbol removal, substitution by noise, and duplication) that operate on the DAB transmission frame level and improve the received signal’s correlation properties. We also demonstrate that two of these methods allow us to avoid the mentioned artifacts and thus to improve the quality of the range/Doppler plots with detection results. We evaluate the performance of the proposed methods using real DAB signals acquired in an experimental PCL platform. We also provide the analysis of the Signal to Noise Ratio (SNR) during the detection of a moving target which shows that the proposed solution, based on symbol duplication, can offer around 3 dB of gain in SNR. Finally, we carry out the computational complexity analysis showing that the proposed method can be implemented with a minimal cost after some optimizations.

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“…In this paper, we continue the work presented in [ 31 ]. We begin with a short review of the essential properties of the DAB signal.…”

Section: Introductionmentioning

confidence: 60%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…When the signal envelope rises above the threshold in this state, the beginning of the PR symbol is detected, and the time instant of this situation (

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DAB Signal Preprocessing for Passive Coherent Location

Mazurek

2022

Sensors

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Average effective subcarrier-domain sparse representation approach for target information estimation in CP-OFDM-based passive bistatic radar

Zhao,

Gong,

Zhou

et al. 2024

EURASIP J. Adv. Signal Process.

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Although some existing sparse representation (SR) methods are robust for target detection in passive bistatic radar (PBR), they still face the challenges of high computational complexity and poor detection performance for extremely low-signal-to-clutter ratio (SCR) target. So, an average effective subcarrier (AES)-domain sparse representation approach is investigated in this paper. Firstly, the AES-based SR model is proposed to solve the problem of high computational complexity, which is established by utilizing the sparseness of the orthogonal frequency-division multiplexing (OFDM) with cyclic prefix (CP) signals in each effective subcarrier domain. Then, considering the difficulty of detecting extremely low-SCR targets, clutter cancellation is implemented by the SR-based optimization model. Two AES-S algorithms, namely AES-S-based clutter cancellation in the time domain (AES-S-T) and AES-S-based clutter cancellation in the subcarrier domain (AES-S-C), are proposed, and the computational complexity is further reduced. Finally, extensive simulation and experimental results illustrate that the proposed algorithms have good detection performance and low computational complexity in PBR detection scene.

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Signal Conditioning for DAB-Illuminated Passive Radar

Cited by 2 publications

References 10 publications

DAB Signal Preprocessing for Passive Coherent Location

DAB Signal Preprocessing for Passive Coherent Location

Average effective subcarrier-domain sparse representation approach for target information estimation in CP-OFDM-based passive bistatic radar

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