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Millimeter Wave RADAR Power-Range Spectra Interpretation 93

(b) 30

20

10
3s bound
Error (dB) 0

0 20 40 60 80 100 120 140 160 180 200
Range (m)

FIGURE 2.30 Continued.

2.10 CONCLUSIONS

This chapter describes a new approach in predicting RADAR range bins which
is essential for SLAM with MMW RADAR.
A noise analysis during signal absence and presence was carried out. This
is to understand the MMW RADAR range spectrum and to predict it accur-
ately as it is necessary to know the power and range noise distributions in the
RADAR power–range spectra. RADAR range bins are then simulated using
the RADAR range equation and the noise statistics, which are then compared
with real results in controlled environments. In this chapter, it is demonstrated
that it is possible to provide realistic predicted RADAR power/range spectra,
for multiple targets down-range.
Feature detection based on target presence probability was also introduced.
Resultsare shown which compare probability-based feature detection with other
feature extraction techniques such as constant threshold on raw data and CFAR
techniques. A difficult compromise in the CA-CFAR method is the choice of
the window size which results in a play-off between false alarms and missed
detections. Variants of the CFAR method exist, which can be tuned to overcome
the problem of missed detections, but the problem of false alarms remains
inherent to these methods.
The target presence probability algorithm presented here does not rely on
adaptive threshold techniques, but estimates the probability of target presence

© 2006 by Taylor & Francis Group, LLC

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