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Slave to radar?

Slave to radar?

Slave to radar?

Well, yes. This is mainly into algorithms, but I have not found a specific forum for that.

We have a scientific radar we want to slave to tracking radars during aircraft trials etc.

These radars usually deliver range, bearing, elevation, track and ground speed referenced to some point, if not their own location.

We will, however, most likely be at another location, so we need to 'transform' the parameters.
The key input to our radar is range, range speed, bearing and w (omega) (dual axis!) for radar itself and platform.

We want to put a Kalman-like (or just simple alpha-beta) filter in between to smooth the incoming data.

I am quite familiar with Kalman etc. but only with a single input observation, for instance range, so this is a little on the edge of my knowledge.

How to implement several related inputs into a Kalman. Range and range speed for instance?
Do one simply weight the transformed speed input and the changed range since last measurement up against each other, or what?
I have so far not found any paper or otherwise dealing with this.

Can this very knowledgeable forum suggest any reading?

RE: Slave to radar?

There are lots of books on Kalman filters. The basic premise is a propagation of the equation of state through time. So, you would have position + velocity*time + position noise + velocity noise...

Nevertheless, depending on your accuracy requirements, etc., a simple alpha-beta filter might work just as well.

This is one such book: https://www.scribd.com/doc/115181721/Tracking-and-...

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Of course I can. I can do anything. I can do absolutely anything. I'm an expert!

RE: Slave to radar?

Have that book!
Not sure which chapter deals with more than one observation parameter, though.

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