RSSI as the Most Commonly Used Positioning Algorithm

RSSI (Received Signal Strength Indication) is a widely adopted technology in positioning algorithms that estimates location by measuring the strength of received wireless signals. Compared to other positioning methods, RSSI offers the advantage of requiring no additional hardware support, making it particularly popular in cost-sensitive application scenarios.

Among various RSSI-based positioning algorithms, the Triangular Centroid Method stands out as the most common approach. The fundamental concept involves using at least three reference points with known positions (such as Wi-Fi access points or Bluetooth beacons) to measure signal strength from the target device, convert these readings into distance estimates, and subsequently calculate the target's position through geometric relationships. Specifically, using signal attenuation models like the log-distance path loss model, RSSI values are converted to theoretical distances. The overlapping coverage areas of the three reference points are then determined on a plane, with the centroid of this intersection calculated as the estimated target location. In code implementation, this typically involves:

- Distance conversion using path loss models: `distance = 10^((transmitPower - RSSI)/(10 * pathLossExponent))` - Trilateration calculations to find intersecting circles - Centroid computation: `centroid_x = (x1+x2+x3)/3, centroid_y = (y1+y2+y3)/3`

The Triangular Centroid Method's advantages lie in its computational simplicity and ease of implementation, making it suitable for real-time applications like indoor navigation and asset tracking. However, its accuracy is limited by signal propagation environments where multipath effects and obstacle interference may cause distance estimation errors. Therefore, practical implementations often incorporate filtering algorithms such as Kalman filters to optimize positioning results through noise reduction and trajectory smoothing.