Inertial Navigation System

An inertial navigation system (INS) is a technology that uses a combination of accelerometers, gyroscopes, and compass to track the motion, position, and orientation of a moving object, such as a vehicle, aircraft, or even a person, without relying on external references.

The basic principle of an inertial navigation system is to measure the acceleration and rotation rates of the object using accelerometers, gyroscopes, and compass, respectively. These sensors provide information about linear acceleration along different axes and angular velocity around various axes. By integrating these measurements over time, the system can estimate the velocity, displacement, and orientation of the object.

In an INS, the accelerometers, gyroscopes, and compass are typically mounted on a stable platform or an inertial measurement unit (IMU) to isolate them from external vibrations and forces. The IMU continuously measures the accelerations and rotations, and the data is processed using algorithms, such as integration and filtering techniques, to estimate the object’s position and attitude.

INS has several advantages, including its ability to operate independently of external infrastructure or signals, such as GPS, and its high update rate, allowing for real-time positioning and navigation. It is widely used in various applications, including aviation, marine navigation, autonomous vehicles, robotics, and even in some personal navigation systems.

However, INS is also subject to errors and drift that can accumulate over time. These errors arise from sensor biases, noise, and integration errors. To mitigate these issues, INS is often combined with other positioning technologies, such as GPS, to periodically correct and update the position and orientation estimates. This integration process is known as sensor fusion, which combines the strengths of multiple sensors to enhance the overall accuracy and robustness of the navigation system.

In recent years, advancements in sensor technology and algorithms, such as advanced filtering techniques and machine learning, have improved the performance and reliability of inertial navigation systems, making them valuable tools for navigation in environments where external references may be limited or unavailable.

Inertial Navigation for Indoor Positioning

Using inertial navigation for indoor positioning involves utilizing the data from inertial sensors (accelerometers, gyroscopes, and compass) to estimate the device’s position and orientation as it moves within an indoor environment. Here’s a step-by-step guide on how to use inertial navigation for indoor positioning:

  1. Sensor data collection: Start by collecting data from the inertial sensors on the device. These sensors provide information about linear acceleration, angular velocity (rotation), and the orientation with respect to Earth’s magnetic field.
  2. Sensor fusion: To obtain more accurate and reliable information, sensor fusion techniques are used to combine data from multiple sensors. The most common method is the Extended Kalman Filter (EKF) or complementary filtering to estimate the device’s position and orientation.
  3. Initial position calibration: Before starting indoor positioning, it’s essential to calibrate the initial position. This calibration could be done manually by allowing the user to input their starting position or automatically by leveraging other positioning technologies like Wi-Fi, Bluetooth beacons, or visual recognition to get an initial reference point.
  4. Dead reckoning: Once the initial position is calibrated, the system starts tracking the device’s movements based on the sensor data. Dead reckoning is used to estimate the new position based on the previously known position and the incremental changes in acceleration and rotation.
  5. Update rate and error correction: Inertial navigation systems are prone to accumulated errors over time due to drift and noise in the sensor data. To improve accuracy, the system needs to update the estimated position frequently. Higher update rates can reduce the impact of errors between updates. Additionally, the system can apply error correction techniques by integrating the inertial navigation data with other positioning technologies available in the indoor environment, such as Wi-Fi, Bluetooth, or visual recognition.
  6. Map matching: Inertial navigation provides relative positioning information but may not provide an absolute position reference. Map matching is the process of aligning the estimated positions with the known features of the indoor environment, such as walls, hallways, or specific landmarks. By matching the estimated positions with the map, the system can correct any drift or positional errors that may have occurred during dead reckoning.
  7. User interface: Finally, the estimated position can be presented to the user on a digital map or through augmented reality (AR) to assist with indoor navigation and provide real-time location information.

It’s important to note that while inertial navigation can be useful for short-term indoor positioning, it tends to suffer from cumulative errors over time. Therefore, integrating inertial navigation with other indoor positioning technologies is a common approach to achieve better accuracy and robustness in an indoor positioning system.

VitalSigns VSI101 Indoor Navigation


VSI101 is a design that uses inertial navigation technology for indoor positioning.


Electrical Specification

  • Battery : 3.7V, 500mAh, Li-ion Battery
  • Working Hour : Maximum 24 hours
  • Charging Power : 5V DC


  • High Performance 32-bit 40 MHz ARM®-M4F
  • 1MB Flash and 256kB SRAM

Data Transfer

  • BLE 4.2/BLE 5.0
  • TX Power : 9 dB
  • Range : 10 meter (open area)


  • 3-Axis
  • FSR : ±250dps, ±500dps, ±1000dps, and ±2000dps


  • 3-Axis
  • FSR : ±2g, ±4g, ±8g, and ±16g


  • 3-Axis
  • Range : ±4900uT


  • Range : 300 – 1200 hPa±0.002hPa


  • GPS, GLONASS, Galileo, and BeiDou
  • Maximum Navigation update rate : 25Hz
  • Position accuracy (CEP) : 1.5m

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