Cascaded Least Square Algorithm for Strong Clutter Removal in Airborne Passive Radar

Sui, Jin xin; Wang, Jun; Zuo, Luo; Gao, Jie

doi:10.1109/taes.2021.3103705

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…3: The equivalent radial velocity v T is calculated by (10). 4: The velocity in the current cell is calculated by using the specified value for the j group velocity and the (11). 5: The bistatic range is calculated according to the obtained range R T and velocity v T and (6).…”

Section: Requirementioning

confidence: 99%

“…Additionally, the radial velocity is measured to be 120 m/s. The target velocity is calculated to be (−38.2, −58.3, −71.3) m/s using Equation (11). 1.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…Recent advancements have also opened up new perspectives and innovative research areas for investigation and practical applications [8,9]. One of the most challenging issues in deploying PMR systems on mobile platforms is the estimation of Doppler frequency, which can be affected by Doppler coupling [10], Doppler distortion [11] and Doppler resolution [12,13]. These effects can result in adverse outcomes such as low SNR, increased errors, and the emergence of ghost targets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

Zhang,

Liu,

Dong

et al. 2023

Remote Sensing

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Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy.

show abstract

Section: Requirementioning

confidence: 99%

“…Additionally, the radial velocity is measured to be 120 m/s. The target velocity is calculated to be (−38.2, −58.3, −71.3) m/s using Equation (11). 1.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

Zhang,

Liu,

Dong

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Airborne passive radars exploit the existing transmitters as emitters of opportunity, having attracted widespread attention in recent years [1][2][3][4][5][6][7]. Compared with airborne active radars, the lack of dedicated transmitters offers the advantages of low cost, strong survivability, and antijamming.…”

Section: Introductionmentioning

confidence: 99%

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

Deng

Pei

et al. 2023

Applied Sciences

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In an airborne passive radar, multipath (MP) clutter, which is caused by MP signals contained in the contaminated reference signal, degrades the space-time adaptive processing (STAP) performance. The MP clutter suppression algorithm before STAP can mitigate the influence of impure reference signals. However, the performances of the existing MP clutter suppression methods deteriorate when the intrinsic clutter motion (ICM) exists because the sparse model of MP clutter is disturbed. To eliminate the impacts of ICM on MP clutter suppression, a joint optimization algorithm is developed for airborne passive radar. Firstly, the sparse model of MP clutter is modified by taking ICM fluctuation into account. Subsequently, the joint optimization function of the ICM fluctuation and MP clutter profile is derived. Finally, based on the local search technique, MP clutter is suppressed with ICM error calibration and off-grid effects mitigation. A range of simulations verify the reliability and superiority of the proposed method.

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“…The performance of this technique mainly depends on the estimated accuracy of CCM. According to the Reed-Mallet-Brennan (RMB) criterion [7], to achieve approximately optimal performance, the training samples used to estimate CCM should be independent and identically distributed (IID) with respect to CUT, and the number of IID training samples should be at least twice as many as the degrees of freedom (DOFs) [8,9]. This condition can be satisfied when the airborne radar is in a homogeneous clutter environment.…”

Section: Introductionmentioning

confidence: 99%

Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression

Wang

Xue

et al. 2022

Remote Sensing

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Due to clutter inhomogeneity, the clutter suppression ability of space–time adaptive processing (STAP) is usually constrained by the insufficient number of independent and identically distributed (IID) clutter training samples and, as a result, is sacrificed to achieve the demanded sample reduction. Moreover, since clutter heterogeneity is exacerbated in the real environment, the IID training sample size can be heavily reduced, leading to the deterioration in clutter suppression. To solve this problem, a novel robust space–time joint sparse processing method with airborne active array is proposed. This method has several outstanding advantages: (1) only the single snapshot cell under test (CUT) data is used for the superior clutter suppression performance; and (2) the proposed method completely removes the dependence of the system processing ability on IID training samples. In this paper, the signal model of uniform transmitting subarray diversity is first established to obtain the single snapshot echo observed CUT data. Then, with the matched reconstruction, the single snapshot data are equivalently converted into multi-frame echo data. Finally, a fast multi-frame echo data joint sparse Bayesian algorithm is used to achieve heterogeneous clutter suppression. Numerous experiments were performed to verify the advantages of the proposed method.

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Cascaded Least Square Algorithm for Strong Clutter Removal in Airborne Passive Radar

Cited by 11 publications

References 24 publications

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression

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