Doppler distributed clutter model of airborne radar and its parameters estimation

Xu, Jia; Peng, Yingning; Wan, Qun; Wang, Xiutan; Xia, Xiang-Gen

doi:10.1360/03yf0095

Cited by 16 publications

(13 citation statements)

References 8 publications

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“…When the RCS of the target increases k dB, the CRBs decrease k dB by (19) and (20). However, from (18), the CRB of RCS estimation will be invariant to the RCS change.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 5 shows the MLE errors and the CRBs of the Doppler parameter estimation versus the 2-D velocity and the SCNR of the moving target. The lines with * are the accurate CRBs obtained by the numerical calculation from (10), the lines with o are the approximate CRBs obtained form (19) and (20), and the dashed lines are the Monte-Carlo results of the MLE errors. It is shown that the approximate CRBs accord with the accurate CRB; therefore, the approximation during the derivation of the CRB is reasonable.…”

Section: Crbs Of the Doppler Parameter Estimationmentioning

confidence: 99%

“…So the localization parameter is C t , and the CRBs of the localization parameter estimation in both domains can be obtained from (19) as…”

Section: Crb Of the Motion And Localization Parameter Estimationmentioning

confidence: 99%

“…[15][16][17] gave the CRB of the parameter estimation of the LFM signal in white Gaussian-distributed noise, and proposed some methods to approach the CRB. In [18][19][20][21], we derived the CRB of the parameter estimation of the single-frequency signal in the colored clutter background, and further proposed the maximum likelihood estimation (MLE) method in some relative applications, e.g., the SAR autofocusing, clutter suppression and velocity SAR (VSAR) processing. However, the parameter estimation of the 2-D distributed LFM signal in the CID with the colored noise background is less studied.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation for SAR moving target in complex image domain

Zuo

Peng

et al. 2010

Sci. China Inf. Sci.

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Based on the proposed parametric model of SAR ground moving target in the complex image domain, this paper derives the Cramer-Rao bounds (CRB) for the unknown model parameters estimation and obtains the inherent limitations for the moving target motion estimation in the SAR complex image domain. Firstly, the relationships are analyzed among the estimation performance and the target's scattering intensity, Doppler parameters, motion parameters, as well as the power spectra of the target and the clutter. Furthermore, the parameter estimation performance is compared in the signal domain and the complex image domain (CID) to draw some useful conclusions. Besides, the reduction resolution processing is proposed to improve the parameter estimation, and its performance improvement is also presented with analytic expressions. For the homogeneous background, it is shown that the accurate parameter estimation and azimuth location may be realized for SAR ground moving target in the complex image domain. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed methods.

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“…When the RCS of the target increases k dB, the CRBs decrease k dB by (19) and (20). However, from (18), the CRB of RCS estimation will be invariant to the RCS change.…”

Section: Discussionmentioning

confidence: 99%

Section: Crbs Of the Doppler Parameter Estimationmentioning

confidence: 99%

“…So the localization parameter is C t , and the CRBs of the localization parameter estimation in both domains can be obtained from (19) as…”

Section: Crb Of the Motion And Localization Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter estimation for SAR moving target in complex image domain

Zuo

Peng

et al. 2010

Sci. China Inf. Sci.

Self Cite

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“…Multifunctional SAR with large-area static scene imaging and ground moving target indication (SAR/GMTI) has drawn much more attentions in recent past decades [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]. In the most of applications, not only the point moving targets but also the distributed moving targets are interested.…”

Section: Introductionmentioning

confidence: 99%

SAR Ground Moving Target Indication Based on Relative Residue of DPCA Processing

Huang

Liang

et al. 2016

Sensors

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For modern synthetic aperture radar (SAR), it has much more urgent demands on ground moving target indication (GMTI), which includes not only the point moving targets like cars, truck or tanks but also the distributed moving targets like river or ocean surfaces. Among the existing GMTI methods, displaced phase center antenna (DPCA) can effectively cancel the strong ground clutter and has been widely used. However, its detection performance is closely related to the target’s signal-to-clutter ratio (SCR) as well as radial velocity, and it cannot effectively detect the weak large-sized river surfaces in strong ground clutter due to their low SCR caused by specular scattering. This paper proposes a novel method called relative residue of DPCA (RR-DPCA), which jointly utilizes the DPCA cancellation outputs and the multi-look images to improve the detection performance of weak river surfaces. Furthermore, based on the statistics analysis of the RR-DPCA outputs on the homogenous background, the cell average (CA) method can be well applied for subsequent constant false alarm rate (CFAR) detection. The proposed RR-DPCA method can well detect the point moving targets and distributed moving targets simultaneously. Finally, the results of both simulated and real data are provided to demonstrate the effectiveness of the proposed SAR/GMTI method.

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Target tracking in wireless sensor networks using adaptive measurement quantization

Zhou

Wang

2011

Sci. China Inf. Sci.

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Quantization/compression is usually adopted in wireless sensor networks (WSNs) since each sensor node typically has very limited power supply and communication bandwidth. We consider the problem of target tracking in a WSN with quantized measurements in this paper. Attention is focused on the design of measurement quantizer with adaptive thresholds. Based on the probability density function (PDF) of the signal amplitude measured at a random location and by maximizing the entropy, an adaptive design method for quantization thresholds is proposed. Due to the nonlinear measuring and quantization models, particle filtering (PF) is adopted in the fusion center (FC) to estimate the target state. Posterior Cramér-Rao lower bounds (CRLBs) for tracking accuracy using quantized measurements are also derived. Finally, a simulation example on tracking single target with noisy circular trajectories is provided to illustrate the effectiveness of the proposed approach.

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Doppler distributed clutter model of airborne radar and its parameters estimation

Cited by 16 publications

References 8 publications

Parameter estimation for SAR moving target in complex image domain

Parameter estimation for SAR moving target in complex image domain

SAR Ground Moving Target Indication Based on Relative Residue of DPCA Processing

Target tracking in wireless sensor networks using adaptive measurement quantization

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