Method and system for cargo sensing estimation
A method at a computing device, the method including obtaining a cargo load signal measurement; and applying an estimator to the cargo load signal measurement, the estimator being in the form of {circumflex over (x)}=ay+bZ, where {circumflex over (x)} is an estimate of a true signal vector, y is the cargo load signal measurement, Z is a calculated or pre-determined reference vector, and a and b are weighting factors.
1. A method at a computing device, the method comprising:
obtaining a cargo load signal measurement, the cargo load signal measurement comprising a measurement from at least one of a time of flight sensor; an ultrasonic sensor; a radio frequency sensor; or a light array associated with the computing device, the cargo load signal measurement comprising a distance measurement;
applying an estimator to the cargo load signal measurement, the estimator being in the form of
{circumflex over (x)}=ay+bZ
where {circumflex over (x)} is an estimate of a true signal vector, y is the cargo load signal measurement, Z is a pre-determined reference vector, and a and b are weighting factors; and
sending the estimate of the true signal vector to a server, wherein the true signal vector allows the server to determine a cargo loading status.
2. The method of claim 1 , wherein a provides more weighting as a signal to noise ratio of the cargo load signal measurement increases.
3. The method of claim 1 , wherein b provides more weighting when a signal to noise ratio of the cargo load signal measurement decreases.
4. The method of claim 1 , wherein
a
=
SNR
SNR
+
1
and
b
=
1
SNR
+
1
thereby producing an estimator of:
x
^
=
SNR
SNR
+
1
y
+
1
SNR
+
1
Z
where SNR is the signal to noise ratio of the cargo load signal measurement.
5. The method of claim 4 , wherein the noise in the signal to noise ratio is set as a pre-determined value.
6. The method of claim 4 , wherein the noise in the signal to noise ratio is set based on system characterization testing at the computing device.
7. The method of claim 4 , wherein the noise in the signal to noise ratio varies based on a sensor at the computing device used for obtaining the cargo load signal measurement.
8. The method of claim 1 , wherein Z is determined based on a mean of the true signal vector, {circumflex over (x)}.
9. The method of claim 1 , wherein Z is set as a constant vector.
10. The method of claim 9 , wherein the phase of the constant vector is dependent on a length of a trailer, and the magnitude of the constant vector is based on an empty cargo load signal measurement.
11. The method of claim 1 , wherein the computing device is a server, and wherein the obtaining includes receiving a communication from a sensor apparatus containing the cargo load signal measurement.
12. The method of claim 1 , wherein the cargo load signal measurement is Kalman filtered.
13. The method of claim 1 , wherein the cargo load signal measurement is a mean of a plurality of cargo load signal samples.
14. A computing device comprising:
a processor; and
a communications subsystem,
wherein the computing device is configured to:
obtain a cargo load signal measurement, the cargo load signal measurement comprising a measurement from at least one of a time of flight sensor; an ultrasonic sensor; a radio frequency sensor; or a light array associated with the computing device, the cargo load signal measurement comprising a distance measurement;
apply an estimator to the cargo load signal measurement, the estimator being in the form of
{circumflex over (x)}=ay+bZ
where {circumflex over (x)} is an estimate of a true signal vector, y is the cargo load signal measurement, Z is a pre-determined reference vector, and a and b are weighting factors; and
send the estimate of the true signal vector to a server, wherein the true signal vector allows the server to determine a cargo loading status.
15. The computing device of claim 14 , wherein a provides more weighting as a signal to noise ratio of the cargo load signal measurement increases.
16. The computing device of claim 14 , wherein b provides more weighting when a signal to noise ratio of the cargo load signal measurement decreases.
17. The computing device of claim 14 , wherein
a
=
SNR
SNR
+
1
and
b
=
1
SNR
+
1
thereby producing an estimator of:
x
^
=
SNR
SNR
+
1
y
+
1
SNR
+
1
Z
where SNR is the signal to noise ratio of the cargo load signal measurement.
18. The computing device of claim 17 , wherein the noise in the signal to noise ratio is set as a pre-determined value.
19. The computing device of claim 17 , wherein the noise in the signal to noise ratio is set based on system characterization testing at the computing device.
20. The computing device of claim 17 , wherein the noise in the signal to noise ratio varies based on a sensor at the computing device used for obtaining the cargo load signal measurement.
21. The computing device of claim 14 , wherein Z is determined based on a mean of the true signal vector, {circumflex over (x)}.
22. The computing device of claim 14 , wherein Z is set as a constant vector.
23. The computing device of claim 22 , wherein the phase of the constant vector is dependent on a length of a trailer, and the magnitude of the constant vector is based on an empty cargo load signal measurement.
24. A computer readable medium for storing instruction code, which, when executed by a processor of a computing device cause the computing device to:
obtain a cargo load signal measurement, the cargo load signal measurement comprising a measurement from at least one of a time of flight sensor; an ultrasonic sensor; a radio frequency sensor; or a light array associated with the computing device, the cargo load signal measurement comprising a distance measurement;
apply an estimator to the cargo load signal measurement, the estimator being in the form of
{circumflex over (x)}=ay+bZ
where {circumflex over (x)} is an estimate of a true signal vector, y is the cargo load signal measurement, Z is a pre-determined reference vector, and a and b are weighting factors; and
send the estimate of the true signal vector to a server, wherein the true signal vector allows the server to determine a cargo loading status.