The Alpha-Beta Family of Filters to Solve the Threshold Problem: A Comparison

Bogdan Ilie Sighencea, Rareș Ion Stanciu, Ciprian Șorândaru and Cătălin Daniel Căleanu
Additional contact information
Bogdan Ilie Sighencea: Department of Applied Electronics, Faculty of Electronics, Telecommunications, and Information Technologies, Politehnica University Timisoara, 300223 Timisoara, Romania
Rareș Ion Stanciu: Department of Applied Electronics, Faculty of Electronics, Telecommunications, and Information Technologies, Politehnica University Timisoara, 300223 Timisoara, Romania
Ciprian Șorândaru: Department of Electrical Engineering, Faculty of Electrical and Power Engineering, University Politehnica Timisoara, 300223 Timisoara, Romania
Cătălin Daniel Căleanu: Department of Applied Electronics, Faculty of Electronics, Telecommunications, and Information Technologies, Politehnica University Timisoara, 300223 Timisoara, Romania

Mathematics, 2022, vol. 10, issue 6, 1-20

Abstract: Typically, devices work to improve life quality, measure parameters, and make decisions. They also signalize statuses, and take actions accordingly. When working, they measure different values. These are to be compared against thresholds. Some time ago, vision systems came into play. They use camera(s) to deliver(s) images to a processor module. The received images are processed to perform detections (typically, they focus to detect objects, pedestrians, mopeds, cyclists, etc.). Images are analyzed and thresholds are used to compare the computed values. The important thing is that images are affected by noise. Therefore, the vision system performance can be affected by weather in some applications (for example, in automotive). An interesting case in this domain is when the measured/computed values show small variations near the threshold (not exceeding) but very close to it. The system is not able to signalize/declare a state in this case. It is also important to mention that changing the threshold does not guarantee solving the problem in any future case, since this may happen again. This paper proposes the Alpha-Beta family of filters as a solution to this problem. The members can track a signal based on measured values. This reveals errors when the tracked-signal’s first derivative changes sign. These errors are used in this paper to bypass the threshold problem. Since these errors appear in both situations (when the first derivative decreases from positive to negative and increases from negative to positive), the proposed method works when the observed data are in the vicinity of the threshold but above it.

Keywords: algorithms; tracking; filters; threshold; processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/6/880/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/6/880/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:6:p:880-:d:768013

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().