Range-Doppler Domain-Based DOA Estimation Method for FM-Band Passive Bistatic Radar

Park, Geun-Ho; Kim, Hyoung-Nam

doi:10.1109/access.2020.2981957

Cited by 7 publications

(6 citation statements)

References 49 publications

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“…Therefore, the ESPRIT algorithm needs to perform DOA estimation only for one target. Moreover, because the power of the signal is concentrated, and the power of the noise is distributed in the R-D map through the FFT operation, there is no degradation in performance even if the ESPRIT operation is performed for one peak value of the R-D map, that is when the number of snapshots is set to one [27]. Figure 4 illustrates the root mean square error (RMSE) of ESPRIT algorithm with the signal-to-noise ratio (SNR) for the targets detected in the R-D map.…”

Section: Doa Estimation Technique For Single Targetmentioning

confidence: 99%

“…In addition, because E H s1 E s1 −1 is a real number, the arg(z k ) operation in Equation ( 13) remains unaffected. Thus, when the number of targets is one, Equation ( 12) can be simplified as shown in Equation (27).…”

Section: Least Square Module (Lsm)mentioning

confidence: 99%

“…The signal subspace entered according to the number of antennas is stored in an RF. After aligning the data according to the subarray configuration of Equation ( 15) using a multiplexer, we enter it into the PSI calculator to perform the operations in Equation (27). The PSI calculator consists of a matrix multiplication module and a complex adder, so it is designed to support a scalable number of antennas.…”

Section: Least Square Module (Lsm)mentioning

confidence: 99%

See 2 more Smart Citations

Scalable ESPRIT Processor for Direction-of-Arrival Estimation of Frequency Modulated Continuous Wave Radar

et al. 2021

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The estimation of signal parameters via rotational invariance techniques (ESPRIT) is an algorithm that uses the shift-invariant properties of the array antenna to estimate the direction-of-arrival (DOA) of signals received in the array antenna. Since the ESPRIT algorithm requires high-complexity operations such as covariance matrix and eigenvalue decomposition, a hardware processor must be implemented such that the DOA is estimated in real time. Additionally, the ESPRIT processor should support a scalable number of antenna configuration for DOA estimation in various applications because the performance of ESPRIT depends on the number of antennas. Therefore, we propose an ESPRIT processor that supports two to eight scalable antenna configuration. In addition, since the proposed ESPRIT processor is based on multiple invariances (MI) algorithm, it can achieve a much better performance than the existing ESPRIT processor. The execution time is reduced by simplifying the Jacobi method, which has the most significant computational complexity for calculating eigenvalue decomposition (EVD) in ESPRIT. Moreover, the ESPRIT processor was designed using hardware description language (HDL), and an FPGA-based verification was performed. The proposed ESPRIT processor was implemented with 10,088 slice registers, 18,207 LUTs, and 80 DSPs, and the slice register, LUT, and DSP were reduced by up to 71.45%, 54.5%, and 68.38%, respectively, compared to the existing structure.

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Section: Doa Estimation Technique For Single Targetmentioning

confidence: 99%

Section: Least Square Module (Lsm)mentioning

confidence: 99%

Section: Least Square Module (Lsm)mentioning

confidence: 99%

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Scalable ESPRIT Processor for Direction-of-Arrival Estimation of Frequency Modulated Continuous Wave Radar

et al. 2021

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“…As PBR receives signals from IOs and has a separated transmitter-receiver structure, it possesses advantages such as interference resistance, anti-stealth capabilities, resistance to low-altitude penetration, cost-effectiveness and efficient spectrum utilization. Common IOs include frequency modulation (FM) [8], digital audio broadcasting (DAB) [9], digital video broadcasting terrestrial (DVB-T) [10], digital terrestrial multimedia broadcasting (DTMB) [11], digital video broadcasting satellite (DVB-S) [12], global system for mobile communications (GSM) [13], beidou navigation satellite system (BDS) [14], global positioning system (GPS) [15], and so on. However, as these signals are not specifically designed for PBR, their ambiguity functions do not exhibit a spike-like nature.…”

Section: Introductionmentioning

confidence: 99%

Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery

Cui,

Wang,

Zhou

et al. 2024

Remote Sensing

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In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.

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“…As its features of no dedicated transmitter and no frequency assignment, passive radar has attracted wide attention from academia and industry. FM radio [1,2], digital TV [3,4], mobile communications [5,6], Wi-Fi [7,8], WiMAX [9][10][11], satellite communications [12,13], and many other IOs have been exploited for passive radar, which makes it achieve remarkable development over the years.…”

Section: Introductionmentioning

confidence: 99%

Using DTMB‐Based Passive Radar for Small Unmanned Aerial Vehicle Detection

Zhu¹,

Wang²,

Liu

2021

Wireless Communications and Mobile Computing

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There is inevitable polarization angle deviation between the target echo signal and the direct path signal of illuminator of opportunity (IO) in passive radar. In order to investigate the potential performance loss in target detection induced by the random deviation, small unmanned aerial vehicle (UAV) detection experiments with digital television terrestrial multimedia broadcasting- (DTMB-) based passive radar are conducted in this paper. Experimental results show that the polarization angles of the clutter signal and target echo signal are inconsistent. When the polarization diversity technology is used to suppress the clutter signal, the processing performance of the target echo signal may be reduced. On the premise that clutter is effectively suppressed by the processing algorithm, polarization synthesis can maximize the target echo signal processing gain. The effectiveness of the target localization algorithm combining time difference of arrival (TDOA) and direction of arrival (DOA) is also verified with polarization diversity reception in this paper.

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Range-Doppler Domain-Based DOA Estimation Method for FM-Band Passive Bistatic Radar

Cited by 7 publications

References 49 publications

Scalable ESPRIT Processor for Direction-of-Arrival Estimation of Frequency Modulated Continuous Wave Radar

Scalable ESPRIT Processor for Direction-of-Arrival Estimation of Frequency Modulated Continuous Wave Radar

Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery

Using DTMB‐Based Passive Radar for Small Unmanned Aerial Vehicle Detection

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