From 1f0226d7800f1382fa15aec6703966a3846619af Mon Sep 17 00:00:00 2001
From: Jcparkyn <51850908+Jcparkyn@users.noreply.github.com>
Date: Mon, 9 Oct 2023 21:38:04 +1100
Subject: [PATCH] Update readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index cb9adbe..4699ae5 100644
--- a/README.md
+++ b/README.md
@@ -28,6 +28,6 @@ The VPE process involves the four main steps:
 
 ### Inertial fusion
 
-We use an Extended Kalman Filter (EKF) to fuse the VPE estimates with the inertial data from the accelerometer and gyroscope, and refine the estimates in real-time using the Rauch-Tung-Striebel (RTS) algorithm. To account for time delay from the camera frames, we use a negative-time measurement update algorithm.
+We use an Extended Kalman Filter (EKF) to fuse the VPE estimates with the inertial data from the accelerometer and gyroscope, and refine the estimates in real-time using the Rauch-Tung-Striebel (RTS) algorithm. To account for time delay from the camera frames, we use a negative-time measurement update algorithm. The EKF is implemented using NumPy and [Numba](https://numba.pydata.org/).
 
 The use of inertial measurements dramatically reduces latency compared to a camera-only implementation, while also improving accuracy and report rate for fast movements.