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Home Backend Development C++ How Does This Algorithm Accurately Calculate Smartphone Position Using Sensor Data and GPS?

How Does This Algorithm Accurately Calculate Smartphone Position Using Sensor Data and GPS?

Jan 03, 2025 pm 02:16 PM

How Does This Algorithm Accurately Calculate Smartphone Position Using Sensor Data and GPS?

My Algorithm to Calculate Position of Smartphone - Integrated Algorithm Based on Sensor Data and GPS

Addressing the Algorithm Issue:

The provided algorithm does not correctly calculate the final velocity (speed) of the device. To address this error, the following formula should be used:

finalvelocity = initialVelocity + Double.Parse(currentAcceleration) * (t);

Revised Algorithm:

The revised algorithm incorporates the corrected speed calculation and additional improvements to ensure accurate position estimation:

var prevLocation = ServerHandler.getLatestPosition(IMEI);
var newLocation = new ReceivedDataDTO()
{
    LocationDataDto = new LocationDataDTO(),
    UsersDto = new UsersDTO(),
    DeviceDto = new DeviceDTO(),
    SensorDataDto = new SensorDataDTO()
};

//First Reading
if (prevLocation.Latitude == null)
{
    //Save GPS Readings
    newLocation.LocationDataDto.DeviceId = ServerHandler.GetDeviceIdByIMEI(IMEI);
    newLocation.LocationDataDto.Latitude = Latitude;
    newLocation.LocationDataDto.Longitude = Longitude;
    newLocation.LocationDataDto.Acceleration = float.Parse(currentAcceleration);
    newLocation.LocationDataDto.Direction = float.Parse(currentDirection);
    newLocation.LocationDataDto.Speed = (float) 0.0;
    newLocation.LocationDataDto.ReadingDateTime = date;
    newLocation.DeviceDto.IMEI = IMEI;

    // saving to database
    ServerHandler.SaveReceivedData(newLocation);
    return;
}

//If Previous Position not NULL --> Calculate New Position

**//Algorithm Starts HERE**

var oldLatitude = Double.Parse(prevLocation.Latitude);
var oldLongitude = Double.Parse(prevLocation.Longitude);
var direction = Math.PI * Double.Parse(currentDirection) / 180.0;
Double initialVelocity = prevLocation.Speed;

//Get Current Time to calculate time Travelling - In seconds
var secondsTravelling = date - tripStartTime;
var t = secondsTravelling.TotalSeconds;

//Calculate Distance using physice formula, s= Vi * t + 0.5 *  a * t^2
var distanceTravelled = initialVelocity * t + 0.5 * Double.Parse(currentAcceleration) * t * t;

//Calculate the Final Velocity/ Speed of the device.
// this Final Velocity is the Initil Velocity of the next reading
//Physics Formula: Vf = Vi + a * t
var finalvelocity = initialVelocity + Double.Parse(currentAcceleration) * (t);

//Convert from Degree to Radians (For Formula)
oldLatitude = Math.PI * oldLatitude / 180;
oldLongitude = Math.PI * oldLongitude / 180;

//Calculate the New Longitude and Latitude
var newLatitude = Math.Asin(Math.Sin(oldLatitude) * Math.Cos(distanceTravelled / earthRadius) + Math.Cos(oldLatitude) * Math.Sin(distanceTravelled / earthRadius) * Math.Cos(direction));
var newLongitude = oldLongitude + Math.Atan2(Math.Sin(direction) * Math.Sin(distanceTravelled / earthRadius) * Math.Cos(oldLatitude), Math.Cos(distanceTravelled / earthRadius) - Math.Sin(oldLatitude) * Math.Sin(newLatitude));

//Convert From Radian to degree/Decimal
newLatitude = 180 * newLatitude / Math.PI;
newLongitude = 180 * newLongitude / Math.PI;

Incorporating Sensor Data Integration:

This algorithm utilizes both accelerometer and magnetometer data to calculate the direction of movement. The following steps outline the process:

  1. Linear Acceleration: Use the accelerometer to calculate linear acceleration in x, y, and z axes.
  2. Direction of Movement: Combine the accelerometer readings with the magnetometer readings to determine the direction of movement.
  3. Velocity and Distance: Integrate the acceleration with respect to time to obtain velocity. Calculate distance using the velocity and time interval.
  4. Final Position: Update the current position using the calculated distance and direction.

Additional Considerations:

  1. Calibration: Calibrate the sensors periodically to ensure accuracy.
  2. Filtering: Implement noise and drift filters to improve data quality.
  3. Time Synchronization: Ensure accurate time synchronization between the sensors and the device's clock.
  4. Regular GPS Updates: Periodically correct the position estimate using GPS updates.

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