Range and Velocity Estimation Using Kernel Maximum Correntropy Based Nonlinear Estimators in Non-Gaussian Clutter

Singh, Uttam; Mitra, Rangeet; Bhatia, Vimal; Mishra, Amit Kumar

doi:10.1109/taes.2019.2948518

“…• Performance of the proposed estimator is compared with the derived analytical expression for modified Cramer-Rao lower bound (MCRLB) of DOD, DOA, and Doppler shift. • The presented computer simulations indicate that the proposed estimator in addition to outperforming the other ITC based kernel estimators like KLMS-NC [21] and KMC-NC [25], also outperforms the estimators proposed in [13], [14], [15], and [16].…”

Section: Introductionmentioning

confidence: 88%

“…However, since KLMS utilizes mean square error (MSE) criterion [22]- [24], and is optimum for Gaussian noise, the estimation algorithm proposed in [20], [21] cannot be used for the estimation of parameters in MIMO radar system perturbed by non-Gaussian clutter. In a later attempt to deal with the effects of non-Gaussian clutter, kernel maximum correntropy (KMC) based estimator proves to be better than estimator based on KLMS [25]. Nevertheless, performance of KMC based estimator may not be good when facing more complicated non-Gaussian clutter [26].…”

Section: Introductionmentioning

confidence: 99%

Kernel Minimum Error Entropy Based Estimator for MIMO Radar in Non-Gaussian Clutter

Singh

¹

,

Mitra

²

,

Bhatia

³

et al. 2021

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In this paper, a kernel minimum error entropy (KMEE) based estimator is proposed for the estimation of multiple targets' direction of departure (DOD), the direction of arrival (DOA), and the Doppler shift with multiple input multiple output radar in the presence of non-Gaussian clutter. Most existing estimation approaches are based on optimization of a complex cost function which often leads to a sub-optimum solution. Therefore, for the accurate estimation of DOD, DOA and Doppler shift, an efficient, kernel adaptive filter (KAF) based estimation approach is proposed. The proposed estimator utilizes the minimum error entropy (MEE) criterion and minimizes the error entropy function. The MEE, being an information-theoretic criterion, optimizes the higher-order statistics of error and thus makes the proposed estimator robust against the effects of outliers like clutter. The KMEE based estimator without any sparsification suffers from a linear increase in computational complexity. Thus, subsequently, the computational complexity of the proposed KMEE based estimator is reduced by incorporation of novelty criterion (NC) based sparsification technique, and the resulting estimator is called KMEE-NC. The performance of the proposed KMEE-NC based estimator is compared with the recently introduced sparse estimators based on kernel maximum correntropy criterion, and kernel minimum mean square error criterion. Additionally, KMEE-NC based estimator is also compared with other existing conventional estimators. Further, for assessing the accuracy of the proposed estimator, the modified Cramer-Rao lower bound is derived using the modified Fisher information matrix.

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“…However, recent works promote the explicit Monte-Carlo approximations of the RKHS, called random Fourier features (RFF), which significantly improves convergence and reduces implementation complexity compared to the pre-existing dictionary based approaches [10]- [13]. In addition, other important works have extended the scope of RKHS based approaches to other applications, such as, detection for massive MIMO (m-MIMO) [13]- [15], parameter-estimation for radar [16], [17], and detection for ultraviolet communications [18]. Further, other works have proposed the RFF based DL (RFF-DL) [19] and hyperparameter-independent RFFs [13], [20], and have carried out a rigorous analysis of RFF-DL in the low-data regime.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Free MEE-FP based Learning for Next Generation Communication Systems

MItra¹,

Kaddoum²,

Costa³

2021

Preprint

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0

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Information theoretic learning (ITL) criteria have emerged useful for mitigating degradations caused by unknown non-Gaussian noise processes in future wireless communication systems. Specifically, the reproducing kernel Hilbert space (RKHS) based approaches relying on ITL based learning criteria are envisioned to provide nearoptimal mitigation of unknown hardware impairments and non-Gaussian noises. Among several ITL criteria, the recent works find the minimum error entropy with fiducial points (MEE-FP) promising due to its guarantee of unbiased estimation and generalization over generic noise distributions. However, MEE-FP based learning approaches are known to depend on an accurate kernel-width initialization. Also, the optimal value of this kernelwidth is well-known to vary temporally and across deployment scenarios. To remove the dependency on kernelwidth, a hyperparameter-free MEE-FP based adaptive algorithm is derived using random-Fourier features with sampled kernel widths (RFF-SKW). In addition, a detailed convergence analysis is presented for the proposed hyperparameter-free MEE-FP, which promises a near-optimal error-floor independent of step-size and guarantees convergence for a wide range of step sizes. The promised hyperparameter-independence and improved convergence for the proposed hyperparameter-free MEE-FP are validated by computer simulations considering different case studies.

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“…However, recent works promote the explicit Monte-Carlo approximations of the RKHS, called random Fourier features (RFF), which significantly improves convergence and reduces implementation complexity compared to the pre-existing dictionary based approaches [10]- [13]. In addition, other important works have extended the scope of RKHS based approaches to other applications, such as, detection for massive MIMO (m-MIMO) [13]- [15], parameter-estimation for radar [16], [17], and detection for ultraviolet communications [18]. Further, other works have proposed the RFF based DL (RFF-DL) [19] and hyperparameter-independent RFFs [13], [20], and have carried out a rigorous analysis of RFF-DL in the low-data regime.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Free MEE-FP based Learning for Next Generation Communication Systems

MItra¹,

Kaddoum²,

Costa³

2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

Information theoretic learning (ITL) criteria have emerged useful for mitigating degradations caused by unknown non-Gaussian noise processes in future wireless communication systems. Specifically, the reproducing kernel Hilbert space (RKHS) based approaches relying on ITL based learning criteria are envisioned to provide nearoptimal mitigation of unknown hardware impairments and non-Gaussian noises. Among several ITL criteria, the recent works find the minimum error entropy with fiducial points (MEE-FP) promising due to its guarantee of unbiased estimation and generalization over generic noise distributions. However, MEE-FP based learning approaches are known to depend on an accurate kernel-width initialization. Also, the optimal value of this kernelwidth is well-known to vary temporally and across deployment scenarios. To remove the dependency on kernelwidth, a hyperparameter-free MEE-FP based adaptive algorithm is derived using random-Fourier features with sampled kernel widths (RFF-SKW). In addition, a detailed convergence analysis is presented for the proposed hyperparameter-free MEE-FP, which promises a near-optimal error-floor independent of step-size and guarantees convergence for a wide range of step sizes. The promised hyperparameter-independence and improved convergence for the proposed hyperparameter-free MEE-FP are validated by computer simulations considering different case studies.

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Range and Velocity Estimation Using Kernel Maximum Correntropy Based Nonlinear Estimators in Non-Gaussian Clutter

Cited by 11 publications

References 37 publications

Kernel Minimum Error Entropy Based Estimator for MIMO Radar in Non-Gaussian Clutter

Kernel Minimum Error Entropy Based Estimator for MIMO Radar in Non-Gaussian Clutter

Hyperparameter Free MEE-FP based Learning for Next Generation Communication Systems

Hyperparameter Free MEE-FP based Learning for Next Generation Communication Systems

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