Complex-Valued Gaussian Sum Filter for Nonlinear Filtering of Non-Gaussian/Non-Circular Noise

Mohammadi, Arash; Plataniotis, Konstantinos N.

doi:10.1109/lsp.2014.2361459

Cited by 21 publications

(11 citation statements)

References 51 publications

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“…A first philosophy then consists in trying to estimate it in order to optimize the non-linearity of the beamformer. This estimation may be implemented through stochastic techniques, based, for example, on particle filtering [23], [24] or through a parametric model of the non-Gaussian observations, such as the Gaussian mixture model [25], well-suited to model non-Gaussian/non-circular noise [26]. However, in all cases, this philosophy is generally costly and difficult to implement.…”

Section: Introductionmentioning

confidence: 99%

Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

Chevalier

Delmas

Sadok

2018

IEEE Trans. Signal Process.

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Linear beamformers are optimal, in a mean square (MS) sense, when the signal of interest (SOI) and observations are jointly Gaussian and circular. Otherwise, linear beamformers become sub-optimal. When the SOI and observations are zero-mean, jointly Gaussian and non-circular, optimal beamformers become widely linear (WL). They become non-linear with a structure depending on the unknown joint probability distribution of the SOI and observations when the latter are jointly non-Gaussian, assumption which is very common in radiocommunications. In this context, the paper aims at introducing, for small-scale systems, third-order Volterra minimum variance distortionless response (MVDR) beamformers, for the reception of a SOI, whose waveform is unknown but whose steering vector is known, corrupted by non-Gaussian and potentially non-circular interference, omnipresent in practical situations. Properties, performance, complexity and adaptive implementation of these beamformers in the presence of non-Gaussian and potentially non-circular interference are analyzed in this paper. These new beamformers are shown to always improve, in the steady state, the performance of Capon beamformer for non-gaussian/circular interference, whereas some of them improve the performance of the WL MVDR beamformer for non-Gaussian/non-circular interference. These new beamformers open new perspectives for spectrum monitoring of non-Gaussian signals and for radiocommunication networks using such signals.

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Section: Introductionmentioning

confidence: 99%

Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

Chevalier

Delmas

Sadok

2018

IEEE Trans. Signal Process.

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“…The treatment of improper complex noise is carried out in various systems including CDMA [45], [116], discrete multitoned systems [27] and spectral image target detection [113]. Asymmetric noise characterization is necessary for appropriate estimation [111], detection [38], [62], [114], filtering [117], processing [42], [242], and compensation [8].…”

Section: Communication Systemsmentioning

confidence: 99%

A Journey From Improper Gaussian Signaling to Asymmetric Signaling

Javed

Amin

Shihada

et al. 2020

IEEE Commun. Surv. Tutorials

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The deviation of continuous and discrete complex random variables from the traditional proper and symmetric assumption to a generalized improper and asymmetric characterization (accounting correlation between a random entity and its complex conjugate), respectively, introduces new design freedom and various potential merits. As such, the theory of impropriety has vast applications in medicine, geology, acoustics, optics, image and pattern recognition, computer vision, and other numerous research fields with our main focus on the communication systems. The journey begins from the design of improper Gaussian signaling in the interference-limited communications and leads to a more elaborate and practically feasible asymmetric discrete modulation design. Such asymmetric shaping bridges the gap between theoretically and practically achievable limits with sophisticated transceiver and detection schemes in both coded/uncoded wireless/optical communication systems. Interestingly, introducing asymmetry and adjusting the transmission parameters according to some design criterion render optimal performance without affecting the bandwidth or power requirements of the systems. This dual-flavored article initially presents the tutorial base content covering the interplay of reality/complexity, propriety/impropriety and circularity/non-circularity and then surveys majority of the contributions in this enormous journey.

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“…To deal with these issues within BLE-based indoor localization, multitude of advanced signal processing solutions are utilized. Kalman filters (KFs) [20,21] are used to smooth RSSI, Particle filters (PFs) [22] are incorporated to deal with non-linearities of the underlying model, Combination of linear (KFs) and non-linear techniques (PFs) are utilized, Gaussian sum filters [23], and multiple-model solutions [24] are used to deal with non-Gaussian and unknown noise characteristics. In this paper, we introduce/incorporate an alternative solution to the existing filtering techniques used recently for indoor localization/tracking via BLE-enabled beacons.…”

Section: Introductionmentioning

confidence: 99%

Belief Condensation Filtering for RSSI-Based State Estimation in Indoor Localization

Mehryar

Malekzadeh

Mazuelas

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of recent interest in localization/tracking within indoor environments based on Bluetooth Low Energy (BLE) technology. The basic motive behind BLE-enabled IoT applications is to provide advanced residential and enterprise solutions in an energy efficient and reliable fashion. Although recently different state estimation (SE) methodologies, ranging from Kalman filters, Particle filters, to multiple-modal solutions, have been utilized for BLEbased indoor localization, there is a need for ever more accurate and real-time algorithms. The main challenge here is that multipath fading and drastic fluctuations in the indoor environment result in complex non-linear, non-Gaussian estimation problems. The paper focuses on an alternative solution to the existing filtering techniques and introduce/discuss incorporation of the Belief Condensation Filter (BCF) for localization via BLE-enabled beacons. The BCF is a member of the universal approximation family of densities with performance bound achieving accuracy and efficiency in sequential SE and Bayesian tracking. It is a resilient filter in harsh environments where nonlinearities and non-Gaussian noise profiles persist, as seen in such applications as Indoor Localization.

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Complex-Valued Gaussian Sum Filter for Nonlinear Filtering of Non-Gaussian/Non-Circular Noise

Cited by 21 publications

References 51 publications

Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

A Journey From Improper Gaussian Signaling to Asymmetric Signaling

Belief Condensation Filtering for RSSI-Based State Estimation in Indoor Localization

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