0

I have set of data which includes position of a car and unknown emitter signal level. I have to estimate the distance based on this. Basically signal levels varies inversely to the square of distance. But when we include stuff like multipath,reflections etc we need to use a diff equation. Here come the Hata Okumura Model which can give us the path loss based on distance. However , the distance is unknown as I dont know where the emitter is. I only have access to different lat/long sets and the received signal level.

What I am asking is could you guys please guide me to techniques which would help me estimate the distance based on current pos and signal strength.All I am asking for is guidance towards a technique which might be useful.

I have looked into How to calculate distance from Wifi router using Signal Strength? but he has 3 fixed wifi signals and can use the FSPL. However in an urban environment it doesnot work.

Community
  • 1
  • 1
Sayantan Roy
  • 134
  • 1
  • 15

1 Answers1

1

Since the car is moving, using any diffraction model would be very difficult. The multipath environment is constantly changing due to moving car, and any reflection/diffraction model requires well-known object geometry around the car. In your problem you have moving car position time series [x(t),y(t)] which is known. You also have a time series of rough measurement of the distance between the car and the emitter [r(t)] of unknown position. You need to solve the stationary unknown emitter position (X,Y). So you have many noisy measurement with two unknown parameters to estimate. This is a classic Least Square Estimation problem. You can formulate r(ti) = sqrt((x(ti)-X)^2 + (y(ti)-Y)^2) and feed your data into this equation and do least square estimation. The data obviously is noisy due to multipath but the emitter is stationary and with overtime and during estimation process, the noise can be more or less smooth out.

Least Square Estimation

Hun
  • 3,707
  • 2
  • 15
  • 15
  • Thanks I would look into this. I am also open to other solution and possibilities. I was looking at ML where I would feed it the signal strength and computed distance from current position to the unknown emitter while training. Note I wont know the actual distance in a unknown dataset ! Now once trained, my model could give me a distance estimation based on signal strength. Then I can take the distance estimation, and current position and compute the location of the emitter. Any thoughts on this ? – Sayantan Roy Mar 21 '16 at 10:53