Method and apparatus for object detection system
The disclosed systems, structures, and methods are directed to an object detection system, employing a receiver configured to receive a signal reflected from an object, an analog-to-digital converter (ADC) configured to convert the received signal into a digital signal, a pre-processor configured to improve a signal-to-noise (SNR) of the digital signal and to generate a pre-processed signal corresponding to the digital signal, a parameter extractor configured to calculate a number of reference cells M and a multiplication factor K 0 , and a Constant False Alarm Rate (CFAR) processor configured to analyze a cell-under-test (CUT) and M reference cells in accordance with the number of reference cells M and the multiplication factor K 0 to detect the presence of the object.
1. An object detection system, comprising:
a receiver configured to receive a signal reflected from an object;
an analog-to-digital converter (ADC) configured to convert the received signal into a digital signal;
a pre-processor configured to improve a signal-to-noise (SNR) of the digital signal and to generate a pre-processed signal corresponding to the digital signal;
a parameter extractor configured to:
compute a SNR corresponding to the received signal,
compute a probability of detection P d in accordance with the SNR and a probability of false alarm P fa ,
compute a number of reference cells M in accordance with the probability of detection P d , the probability of false alarm P fa and the SNR, and
compute a value of the multiplication factor K 0 in accordance with the probability of false alarm, SNR and the number of reference cells M; and
a Constant False Alarm Rate (CFAR) processor configured to analyze a cell-under-test (CUT) and M reference cells in accordance with the number of reference cells M and the multiplication factor K 0 to detect the presence of the object.
2. The object detection system of claim 1 , wherein the pre-processor further comprising a low-pass filter and a square-law detector.
3. The object detection system of claim 1 , wherein the CFAR processor is configured to:
compute an average power P A corresponding to the M reference cells;
mix the average power P A with the multiplication factor K 0 and to generate a threshold value K 0 P A ;
compare the threshold value K 0 P A with a power P C corresponding to the CUT; and
detect the presence of object if the power P C is greater than the threshold value K 0 P A .
4. The object detection system of claim 1 , wherein the CFAR processor comprises a controller configured to supply at least M+3 samples associated with the pre-processed signal to a moving window.
5. The object detection system of claim 4 , wherein the controller is further configured to provide the multiplication factor K 0 to a mixer.
6. An object detection method, comprising:
receiving a signal reflected from an object;
converting the received signal into a digital signal;
pre-processing the digital signal and generating a pre-processed signal corresponding to the digital signal;
computing a signal-to-noise (SNR) corresponding to the received signal;
computing a probability of detection P d in accordance with the SNR and a probability of false alarm P fa ;
computing the number of reference cells Min accordance with the probability of detection P d , probability of false alarm P fa and SNR;
computing the value of the multiplication factor K 0 in accordance with the probability of false alarm, SNR and the number of reference cells M; and
analyzing a cell-under-test (CUT) and M reference cells in accordance with the number of reference cells M and the multiplication factor K 0 to detect the presence of the object.
7. The method of claim 6 , wherein the pre-processing of the digital signal includes filtering.
8. The method of claim 6 , wherein the pre-processing of the digital signal includes processing the digital signal in a square-law detector.
9. The method of claim 6 , wherein the number of reference cells M is computed according to following relationship:
P
d
=
[
1
+
(
P
fa
-
1
M
-
1
)
(
1
+
SNR
)
]
-
M
10. The method of claim 9 , wherein a first order solution for the number of reference cells M is given by:
M
=
(
ln
P
fa
)
2
2
(
ln
P
fa
-
(
1
+
SNR
)
ln
P
d
)
11. The method of claim 9 , wherein a second order solution for the number of reference cells M is given by:
M
=
2
3
ln
P
fa
+
(
3
ln
P
fa
)
2
-
24
(
ln
P
fa
-
(
1
+
SNR
)
ln
P
d
)
(
ln
P
fa
)
2
12. The method of claim 6 , wherein the number of reference cells M is a time varying value.
13. The method of claim 6 , wherein the multiplication factor K 0 is computed according to following relationship:
K
0
=
M
(
P
fa
-
1
M
-
1
)
14. The method of claim 6 , wherein the analyzing the cell-under-test (CUT) and M reference cells further comprises:
computing an average power P A corresponding to the M reference cells;
mixing the average power P A with the multiplication factor K 0 to generate a threshold value K 0 P A ; and
comparing the threshold value K 0 P A with a power P C corresponding to the CUT.
15. The method of claim 14 , further comprising supplying at least M+3 samples associated with the pre-processed signal to a moving window.
16. The method of claim 14 , further comprising detecting the object if the power P C is greater than the threshold value K 0 P A .