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ATC II -  Description & Datasheet

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FreeScopes ATC II empowers the students to assemble new complex blocks on their own. Also they are equipped with statistical algorithms like the interactive Multiple Model or the Kalman filter.

  • Zero Velocity filter
  • Clutter Map (subtraction)
  • Signal Delay Block
  • Subtraction and Sum
  • Kalman filter
  • iMM (interactiving Multiple Model)

kalman_filter

 

POSSIBLE EXERCISES

ATC II is the module for the advanced user. It allows to apply complex signal manipulations. 
Possible exercises are

  • creating own complex filters for moving or standing targets
  • improving or varying known composite algorithms
  • improving the radar image by adding additional processing

Clutter Map

The clutter map records the initial radar image. It can be used in many subsequent algorithm to detect changes to the initial state (e.g., one or several targets)

Signal Delay Filter

The signal delay block is required in many composite applications where we need to compare the actual sweep with a previous sweep (n+1, n+2, n+3).

Doppler Filter

The Doppler Filter is implemented as an MTI block with post processing and subsequent phase shift detection of two consecutive pulses.

Two consecutive pulses which have a Doppler-frequency will not have a regular/expected phase shift in comparison with two consecutive pulses of stationary targets

Zero Velocity Filter

The zero velocity filter filters out only targets which are not moving.  The filter is needed in many applications like MTD.

Subtraction & Sum

The subtraction and sum feature allow to add or subtract several signals. This is needed in many composite filters.

Kalman Filter

One of the most powerful statistical estimation techniques, which is widely applied in navigation, radar tracking, jamming defense, satellite orbit determination, autonomous driving, and many other fields is the Kalman filter. This digital filter provides a quite accurate estimation of the next state (position, movement, temperature, etc.) from any possible noisy input signal, in real time, which makes it very suitable for radar navigation and tracking purposes.

For the tracking feature in ATC II, we make use of the Kalman filter principle, to estimate the detected moving targets. In the following figure, the block diagram of the tracking feature is shown.

The tracking feature of ATC II consist of four steps:

  • Signal detection – DET
  • Phase Shift detector – PH - DET
  • Association – ASO
  • Kalman filter – KF

interacting Multiple Model

The interacting multiple model (iMM) filter is a sophisticated and compiste algorithm for object tracking.

The main difference between the linear Kalman filter, is the extension of object prediction and correction of the object positions also on the lateral positions.

The linear Kalman filter is mainly taking as track updates the longitudinal object positions, where with the iMM also the track updates on the lateral positions are considered, and in this way more robustness on object tracking is added. 

The FreeScopes applications in the 8 GHz get active in a plug-and-play approach, as soon as the radar on its own, or in conjunction with the SkyRadar CloudServer are plugged in.