Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
Wireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/4/203 |
| _version_ | 1827746238350491648 |
|---|---|
| author | Reiner Jedermann |
| author_facet | Reiner Jedermann |
| author_sort | Reiner Jedermann |
| collection | DOAJ |
| description | Wireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite impulse response (FIR) filters, entail a group delay of 10 s of samples, which is not acceptable regarding the typical interval of wireless sensors of seconds or minutes. The purpose of this paper is to verify the feasibility of single-delay resampling, i.e., the algorithm resamples the data without waiting for future samples. A new method to parametrize Kriging interpolation is presented and compared with two variants of Lagrange interpolation in detailed simulations for the resulting prediction error. Kriging provided the most accurate resampling in the group-delay scenario. The single-delay scenario required almost double the OSR to achieve the same signal-to-noise ratio (SNR). An OSR between 1.8 and 3.1 was necessary for single-delay resampling, depending on the required SNR and signal distortions in terms of jitter, missing samples, and noise. Kriging was the least noise-sensitive method. Especially for signals with missing samples, Kriging provided the best accuracy. The simulations showed that single-delay resampling is feasible, but at the expense of higher OSR and limited SNR. |
| first_indexed | 2024-03-11T05:18:36Z |
| format | Article |
| id | doaj.art-2141e7d1ff8e439a9eb748910a93b9b1 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T05:18:36Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-2141e7d1ff8e439a9eb748910a93b9b12023-11-17T17:59:13ZengMDPI AGAlgorithms1999-48932023-04-0116420310.3390/a16040203Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based MethodReiner Jedermann0Institute for Microsensors, Actuators and Systems (IMSAS), University Bremen, Otto-Hahn Allee 1, 28359 Bremen, GermanyWireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite impulse response (FIR) filters, entail a group delay of 10 s of samples, which is not acceptable regarding the typical interval of wireless sensors of seconds or minutes. The purpose of this paper is to verify the feasibility of single-delay resampling, i.e., the algorithm resamples the data without waiting for future samples. A new method to parametrize Kriging interpolation is presented and compared with two variants of Lagrange interpolation in detailed simulations for the resulting prediction error. Kriging provided the most accurate resampling in the group-delay scenario. The single-delay scenario required almost double the OSR to achieve the same signal-to-noise ratio (SNR). An OSR between 1.8 and 3.1 was necessary for single-delay resampling, depending on the required SNR and signal distortions in terms of jitter, missing samples, and noise. Kriging was the least noise-sensitive method. Especially for signals with missing samples, Kriging provided the best accuracy. The simulations showed that single-delay resampling is feasible, but at the expense of higher OSR and limited SNR.https://www.mdpi.com/1999-4893/16/4/203Lagrange interpolationkrigingnon-uniform resamplingarbitrary resamplingreal-timereconstruction |
| spellingShingle | Reiner Jedermann Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method Algorithms Lagrange interpolation kriging non-uniform resampling arbitrary resampling real-time reconstruction |
| title | Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method |
| title_full | Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method |
| title_fullStr | Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method |
| title_full_unstemmed | Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method |
| title_short | Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method |
| title_sort | feasibility of low latency single sample delay resampling a new kriging based method |
| topic | Lagrange interpolation kriging non-uniform resampling arbitrary resampling real-time reconstruction |
| url | https://www.mdpi.com/1999-4893/16/4/203 |
| work_keys_str_mv | AT reinerjedermann feasibilityoflowlatencysinglesampledelayresamplinganewkrigingbasedmethod |