Filtering the Noise: Why Smoothing RSSI Data is Essential

In the world of Real-Time Location Services (RTLS), the quality of your data determines the accuracy of your insights. For BLE-based RTLS solutions like the Rapids RTLS engine, one of the most significant challenges is managing **noisy RSSI values**. This noise can disrupt location estimates, leading to inaccurate tracking and unreliable geofencing.

In this post, we’ll delve into the challenges of using RSSI for indoor tracking, explain why smoothing is crucial, and introduce the Kalman filter key component of our solution.

The RSSI Problem

The Received Signal Strength Indicator (RSSI) is a measure of how strong a signal is when received by a gateway. While RSSI provides a useful approximation of distance between a BLE device and a gateway, it’s far from perfect.

RSSI values fluctuate due to several factors:

• Environmental Obstacles: Walls, furniture, and people can weaken signals.
• Multi-Path Interference: Signals bounce off surfaces, creating echoes that confuse measurements.
• Device Movement: The orientation and speed of BLE devices can introduce variability.
• Noise from Other Signals: QBLE operates in the 2.4 GHz band, which is shared by Wi-Fi, microwaves, and other devices, leading to interference.

Impact of RSSI Noise

Raw RSSI data often appears chaotic, with spikes and dips that make it unsuitable for precise tracking. If left unprocessed, this noise can result in:

• Erratic Location Estimates: Unpredictable jumps in asset position.
• False Geofence Triggers: Inaccurate entry or exit events due to noise-induced position changes.
• Reduced Accuracy: Difficulty in distinguishing between closely spaced locations.

The Importance of Smoothing

To address RSSI variability, smoothing is essential. By filtering out short-term fluctuations, smoothing provides a clearer picture of the true signal trends. This step is crucial for:

• Stable Position Estimates: Ensuring location updates reflect real movements, not noise.
• Improved Accuracy: Enhancing the reliability of distance calculations.
• Better User Experience: Reducing false positives or negatives in location-based notifications.

Our Solution: The Kalman Filter

The Kalman filter is a mathematical algorithm designed to estimate the true state of a system in the presence of noise. Widely used in robotics, navigation, and tracking, it’s an ideal choice for smoothing RSSI data in RTLS applications.

How It Works

The Kalman filter operates in two main stages:

• Prediction: Based on the previous position and motion trends, the filter predicts the next position.
• Update: As new RSSI data arrives, the filter adjusts the prediction to match the observed data while minimizing noise impact.

By continuously iterating between these steps, the Kalman filter provides accurate and stable estimates of BLE device positions.

Advantages of Using a Kalman Filter

• Noise Reduction: Filters out short-term RSSI fluctuations.
• Predictive Accuracy: Accounts for device movement trends to improve tracking.
• Real-Time Processing: Efficient enough for high-frequency data streams, ensuring smooth real-time updates.

Visualizing the Impact

Here’s a simple comparison of raw RSSI data vs. Kalman-filtered data:

Raw RSSI	Kalman Filter Output
Chaotic and inconsistent	Smooth and stable
Prone to spikes and dips	Reflects realistic trends
Unreliable for tracking	Suitable for accurate position estimation

Real-World Example: Hospital Asset Tracking

Consider a hospital where infusion pumps equipped with BLE tags are tracked in real time. Raw RSSI data from these tags can fluctuate wildly due to:

• Walls and Medical Equipment: RSSI signals weaken or reflect off walls, beds, and diagnostic machines.
• Movement of Staff and Patients: The dynamic nature of hospital environments introduces unpredictable interference.
• Overlapping Signals from Nearby Floors: Signals from BLE tags on different floors can overlap, causing ambiguity.

Without filtering, these fluctuations lead to erratic location estimates. For instance, an infusion pump might appear to “jump” between rooms or even floors, triggering false geofence events.

By applying a Kalman filter, the Rapids RTLS engine smooths out these RSSI variations, providing:

• Stable Tracking: The infusion pump’s location updates consistently, accurately reflecting its actual position.
• Reliable Notifications: Alerts, such as when the pump leaves a designated zone, are triggered only under true conditions.
• Operational Efficiency: Staff can quickly locate critical equipment without wasting time on inaccurate location data.

With Kalman filtering, the RTLS engine ensures precise and reliable asset tracking, even in a bustling and interference-heavy hospital environment.

Conclusion:

Smoothing RSSI data is a cornerstone of reliable indoor tracking. With the Kalman filter, the Rapids RTLS engine transforms noisy signals into actionable insights, paving the way for accurate geofencing, floor discrimination, and real-time location updates.

In the next part of this series, we’ll explore how the Rapids RTLS engine uses floor discrimination and advanced filtering techniques to further enhance accuracy in complex environments. Stay tuned!