Ternary-Event-Based State Estimation With Joint Point, Quantized, and Set-Valued Measurements

Mohammadi, Arash; Davar, Somayeh; Plataniotis, Konstantinos N.

doi:10.1109/lsp.2018.2812152

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…For an expanding system that heavily relies on estimations at every level, accuracy and minimized latency will become paramount with minimal tolerance for error. In different IoT applications that are mainly concerned with indoor micro localization-tracking, message passing and cooperative distributed estimation algorithms [14][15][16][17] are essential for proper integration of the aforementioned three layers, to take advantage of the richness of underlying data, and to achieve the high accuracy and low latency requirements of IoT applications. In this regard, to perform micro-localization based on BLE tags in IoT applications, the main-stream methodology is to use the Received Signal Strength Indicator (RSSI) [18].…”

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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