Dead Reckoning

Unikie dead reckoning - ready for action

Do you need a functioning Dead Reckoning solution? Our market-proven solution is now ready for delivery. 

Accurate estimation of position - dead reckoning

Why is it useful?

We all know what happens to GPS / GNSS system when entering a tunnel - lost contact.

Dead Reckoning is able to generate the best possible estimation of the current position of a moving object, such as a vehicle, by using external reference points for position fixing such as:

  • Direction: Heading (yaw), Pitch and Roll
  • Velocity
  • Acceleration
  • Time period passed

Dead reckoning functioning in a tunnel compared to GNSS or GPS

Market-proven and commercially available

Do you want to make your built-in Navigation client, Apple CarPlay or Android Auto more accurate and effective? Our market-proven Dead Reckoning solution is now ready for delivery.

Dead Reckoning is useful since it can maintain position information without disruption when other position information, such as GPS / GNSS, is not available, therefore creating a smooth and constant estimate of vehicle location combining GPS / GNSS information as well as what is called “relative positioning”.


Relative positioning gives estimated positions during very short interval, typically 10-1000 per second. Relative positioning cannot be used alone, but number of measurements of relative positioning can be summed to get an estimate of movement.

Sensors utilized to provide relative positioning data:

  • Accelerometer
  • Gyroscope
  • Velocity (or wheel tick sensor) and steering angle


How dead reckoning works
Example setup. Solution compatible with Apple CarPlay and Android Auto.

Dead Reckoning module is composed of 4 main parts:

  • Sensor API that allows positioning module to interact with platform sensors. Each platform needs to implement Sensor API wrappers for system components so that the sensors become available for the Dead Reckoning Module. Currently such wrappers exist for example for ROS and ROS2 platforms and can be implemented for other platforms as needed.
  • Trackers that follow positioning based on each sensor. For example, trackers will filter outliers from sensor data and can use mathematical methods such as Kalman filters to estimate positioning per sensor.
  • Track Fusion that combines most accurate estimation at any moment in time based on data provided by individual trackers.
  • Support Libraries that provide functionality needed by the other components, such as coordinate system transformations, mathematical functions and other functionality.

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