# Implementing a Kalman Filter in Postgres to Smooth GPS Data

DevFeed: [Implementing a Kalman Filter in Postgres to Smooth GPS Data](<https://devfeed.tech/articles/implementing-a-kalman-filter-in-postgres-to-smooth-gps-data-5436.md>)

Original publisher: [Read original article](<https://neon.com/blog/implementing-a-kalman-filter-in-postgres-to-smooth-gps-data>)

Author: Thorsten Rieß

Published: 2025-09-26T16:47:33Z

Content type: tutorial

Language: en

Sources: [Blog -- Neon Docs](<https://devfeed.tech/sources/blog-neon-docs.md>)

Topics: [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [Database](<https://devfeed.tech/topics/database.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [Code](<https://devfeed.tech/topics/code.md>), [data](<https://devfeed.tech/topics/data.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [code](<https://devfeed.tech/tags/code.md>), [community](<https://devfeed.tech/tags/community.md>), [data](<https://devfeed.tech/tags/data.md>), [database](<https://devfeed.tech/tags/database.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [devices](<https://devfeed.tech/tags/devices.md>), [errors](<https://devfeed.tech/tags/errors.md>), [gps](<https://devfeed.tech/tags/gps.md>), [iot](<https://devfeed.tech/tags/iot.md>), [open](<https://devfeed.tech/tags/open.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [smartphones](<https://devfeed.tech/tags/smartphones.md>), [sql](<https://devfeed.tech/tags/sql.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

## AI overview

This tutorial explains how to implement a Kalman Filter in Postgres to smooth noisy GPS data. It describes the filter's prediction and update steps, uncertainty modeling, and the challenges of maintaining state across SQL rows, while highlighting the benefits of in-database processing for large datasets.

## Source excerpt

Modern GPS datasets are notoriously noisy: satellites drift, buildings scatter signals, and consumer devices introduce frequent errors. When working with millions of position samples from vehicles, smartphones, or IoT devices, this noise makes analysis unreliable. Routes jump, tr...