Office Action 5: Notice of Allowance (2025-02-19)

CLAIMS:
AMENDMENTS TO THE CLAIMS:

The following listing of claims replaces all prior listings:

1\. (Currently Amended) A system for wireless vital sign monitoring, comprising:
a transmitter configured to transmit a wireless signal through a wireless channel of a venue;
a receiver configured to receive the wireless signal through the wireless channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue; and
a processor configured for:obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal, wherein each CI is a vector associated with a time stamp with N components,
computing a time series of denoised CI (DCI) by denoising the time series of CI (TSCI), wherein each DCI is a vector associated with a time stamp with N components,
computing a time series of concatenated CI (TSCCI) based on a concatenation of the TSCI and the time series of DCI (TSDCI),
wherein:each concatenated CI (CCI) associated with a respective time stamp is a vector comprising 2N components and is computed by concatenating a respective CI associated with the same respective time stamp and a respective DCI associated with the same respective time stamp corresponding to the respective CI a first set of N components among the 2N components are associated with the respective CI, each of the N components in the first set being associated with a respective component of the respective CI,
a second set of N components among the 2N components are associated with the respective DCI, each of the N components in the second set being associated with a respective component of the respective DCI,
computing a one dimensional (1D) characteristic function of CCI of the TSCCI in a sliding time window,
computing a two dimensional (2D) decomposition of the TSCCI based on the 1D characteristic function in the sliding time window,
enhancing the 2D decomposition based on a vertical highpass horizontal lowpass filtering, and
monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition.

2\. (Currently Amended) The system of claim 1, wherein the processor is further configured for:
computing each CCI as a concatenation of a first basic feature of the respective CI and a second basic feature of the corresponding DCI,
wherein the first basic feature of any CI comprises at least one of: the CI, a magnitude of the CI, a phase of the CI, a magnitude of a component of the CI, a phase of the component of the CI, or a function of any magnitude or phase of the CI,
wherein the second basic feature of any DCI comprises at least one of: the DCI, a magnitude of the DCI, a phase of the DCI, a magnitude of a component of the DCI, a phase of the component of the DCI, or a function of any magnitude or phase of the DCI\[\[CI\]\],
wherein the TSDCI is computed by processing the TSCI with a first denoising filter, the first denoising filter comprising at least one of: a low-pass filter, a bandpass filter, a highpass filter, a median filter, or a percentile filter.

3\. (Previously Presented) The system of claim 2, wherein the processor is further configured for:
determining a series of sliding time windows, each sliding time window being associated with a time stamp;
computing a respective one dimensional (1D) characteristic function of the CCI of the TSCCI in each sliding time window; and
constructing the 2D decomposition based on the respective 1D characteristic function in each sliding time window.
4\. (Previously Presented) The system of claim 3, wherein:
the respective 1D characteristic function is computed based on a transformation of the CCI of the TSCCI in each sliding time window; and
the transformation comprises at least one of:
a frequency transformation, a transformation to frequency domain, a short-time Fourier transform (STFT), a wavelet transform, a filter-bank representation,
a time transformation, a transformation to a time-shift domain, an auto-correlation function (ACF), an auto-covariance function, or a time-frequency decomposition.
5\. (Original) The system of claim 4, wherein the processor is further configured for:
constructing a 2D matrix by lining up and aligning the 1D characteristic functions of the sliding time windows in time order according to the associated time stamps, wherein i-th column of the 2D matrix represents the 1D characteristic function associated with i-th sliding time window,j-th row of the 2D matrix is associated with a time-shift of j time units,
a matrix element at the i-th column and the j-th row of the 2D matrix is a value of the 1D characteristic function in the i-th sliding time window with a time-shift of j time units, and
the 2D matrix is a result of the 2D decomposition.
6\. (Previously Presented) The system of claim 5, wherein the processor is further configured for:
enhancing the 2D decomposition by applying, to each matrix element of the 2D matrix, the vertical highpass horizontal lowpass filtering.
7\. (Original) The system of claim 6, wherein the processor is further configured for:
filtering each matrix element of the 2D matrix with a respective 2D vertical highpass horizontal lowpass filter that is either separable or non-separable, either linear or nonlinear.
8\. (Original) The system of claim 7, wherein the processor is further configured for:

enhancing the 2D decomposition by filtering each matrix element of the 2D matrix with at least one of: a respective vertical highpass filter or a respective horizontal lowpass filter,
wherein each of the respective vertical highpass filter and the respective horizontal lowpass filter is either linear or nonlinear.
9\. (Original) The system of claim 8, wherein the processor is further configured for enhancing the 2D decomposition by at least one of:
filtering part of each column of the 2D matrix with a respective first common vertical highpass filter;
filtering part of each row of the 2D matrix with a respective second common vertical highpass filter;
filtering part of each column of the 2D matrix with a respective first common horizontal lowpass filter; or filtering part of each row of the 2D matrix with a respective second common horizontal lowpass filter.

10\. (Previously Presented) The system of claim 9, wherein:
each vertical highpass filter is a directional 1D highpass filter applied in a vertical direction or in a near-vertical direction.

11\. (Previously Presented) The system of claim 10, wherein:
each horizontal lowpass filter is a directional 1D lowpass filter applied in a horizontal direction or in a near-horizontal direction.

12\. (Original) The system of claim 11, wherein the processor is further configured for:
filtering each matrix element of the 2D matrix with a succession of at least two 1D filters, each being either the vertical highpass filter or the horizontal lowpass filter.

13\. (Original) The system of claim 12, wherein the processor is further configured for:
enhancing the 2D decomposition by applying histogram equalization to a number of consecutive columns of the 2D matrix to generate an enhanced 2D matrix.

14\. (Original) The system of claim 13, wherein the processor is further configured for:
computing a distance score based on dynamic time warping (DTW) between a reference column and a particular column of the enhanced 2D matrix associated with a particular time, wherein:the reference column is computed in a training session by clustering a number of training columns matched based on DTW,
the training columns are computed based on training TSCI obtained from training wireless signals that are received through the wireless channel when training objects with vital signs are present and impacting the wireless channel, and
when the distance score is larger than a first threshold, the periodic motion of the vital sign of the object is tentatively not detected at the particular time.

15\. (Original) The system of claim 14, wherein the processor is further configured for:
subtracting a quantity from each matrix element in the particular column of the enhanced 2D matrix; and
computing a zero-crossing count of the particular column,
wherein when the zero-crossing count is larger than a second threshold, the periodic motion of the vital sign of the object is tentatively not detected at the particular time.

16\. (Original) The system of claim 15, wherein the processor is further configured for:
computing multiple peaks of the particular column of the enhanced 2D matrix; and
computing an inter-peak distance based on the multiple peaks.

17\. (Original) The system of claim 16, wherein:
when the inter-peak distance is in an acceptable range, the periodic motion of the vital sign of the object is tentatively detected at the particular time, and the inter-peak distance is determined to be equal to a frequency or a period of the vital sign; and
when the inter-peak distance is not in an acceptable range, the periodic motion of the vital sign of the object is tentatively not detected at the particular time.

18\. (Original) The system of claim 17, wherein the processor is further configured for:
detecting the vital sign of the object when P is larger than a third threshold, wherein P is a percentage of the periodic motion of the vital sign being tentatively detected in a period of time.

19\. (Currently Amended) A wireless device of a system for wireless vital sign monitoring, comprising:
a processor;
a memory communicatively coupled to the processor; and
a receiver communicatively coupled to the processor, wherein:an additional wireless device in the system is configured to transmit a wireless signal through a wireless channel of a venue,
the receiver is configured to receive the wireless signal through the wireless channel,
the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue, and
the processor is configured for:obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal, wherein each CI is a vector associated with a time stamp with N components,
computing a time series of denoised CI (DCI) by denoising the time series of CI (TSCI), wherein each DCI is a vector associated with a time stamp with N components,
computing a time series of concatenated CI (TSCCI) based on a concatenation of the TSCI and the time series of DCI (TSDCI),
computed by concatenating a respective CI and a respective DCI corresponding to the CI,
wherein:each concatenated CI (CCI) associated with a respective time stamp is a vector comprising 2N components and is computed by concatenating a respective CI associated with the same respective time stamp and a respective DCI associated with the same respective time stamp corresponding to the respective CI a first set of N components among the 2N components are associated with the respective CI, each of the N components in the first set being associated with a respective component of the respective CI,
a second set of N components among the 2N components are associated with the respective DCI, each of the N components in the second set being associated with a respective component of the respective DCI,
computing a one dimensional (1D) characteristic function of CCI of the TSCCI in a sliding time window,
computing a two dimensional (2D) decomposition of the TSCCI based on the 1D characteristic function in the sliding time window,
enhancing the 2D decomposition by applying a vertical highpass filter followed by a histogram equalization, and
monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition.

20\. (Currently Amended) A method for wireless vital sign monitoring, comprising:
transmitting a wireless signal through a wireless channel of a venue;
receiving the wireless signal through the wireless channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue;
obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal, wherein each CI is a vector associated with a time stamp with N components;
computing a time series of denoised CI (DCI) by denoising the time series of CI (TSCI), wherein each DCI is a vector associated with a time stamp with N components;
computing a time series of concatenated CI (TSCCI) based on a concatenation of the TSCI and the time series of DCI (TSDCI), wherein:each concatenated CI (CCI) associated with a respective time stamp is a vector comprising 2N components and is computed by concatenating a respective CI associated with the same respective time stamp and a respective DCI associated with the same respective time stamp corresponding to the respective CI,
a first set of N components among the 2N components are associated with the respective CI, each of the N components in the first set being associated with a respective component of the respective CI,
a second set of N components among the 2N components are associated with the respective DCI, each of the N components in the second set being associated with a respective component of the respective DCI;
computing a one dimensional (1D) characteristic function of CCI of the TSCCI in a sliding time window;
computing a two dimensional (2D) decomposition of the TSCCI based on the 1D characteristic function in the sliding time window;
enhancing the 2D decomposition based on a vertical highpass filter and a histogram equalization; and
monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition.

OFFICE ACTION:
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection.  Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.  Applicant's submission filed on 11/24/2024 has been entered.
Allowable Subject Matter
Claims 1-20 are allowed.
The following is an examiner’s statement of reasons for allowance: No prior art has been found to fully teach the claimed vital sign monitoring system with specifically claimed signal analysis process.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

APPLICANT REBUTTAL: REMARKS
Claims 1-20 are pending in the application. By this Response, claims 1-2 and 19-20 have been amended. No new matter is added by these amendments, which are supported by the Applicant's specification as filed, e.g. paragraphs [00198], [00206], [00255]-[00257] and [00265]. Applicant respectfully requests reconsideration and allowance of the pending claims in view of the remarks presented below.
Claim Rejections under 35 U.S.C. §112
Claims 1-20 were rejected under 35 U.S.C. § 112(b) or 35 U.S.C. § 112 (pre-AIA), second paragraph, as allegedly being indefinite.
By this Response, Applicant has amended claims 1 and 19-20 to address the 112 rejections, taking into consideration the comments and suggestions in the Office Action.
Therefore, Applicant respectfully requests that the 112 rejections on claims 1-20 be withdrawn.
Claim Rejections under 35 U.S.C. §103
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al.
("Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi"; hereinafter "Liu"), in view of Xu et al. ("Attention-based Walking Gait and Direction Recognition in Wi-Fi Networks"; hereinafter "Xu"), and further in view of Suyambukesan et al. (US2006/0077306; hereinafter "Suyambukesan").
Applicant respectfully traverses each of the above rejections, and does not agree on any Official Notice taken in the Office Action.
Without agreeing on the above rejections and solely for expediting the allowance of the
application, independent claim 1 is amended to recite among other things:
obtaining a time series of channel information (CI) of the wireless channel based
on the received wireless signal, wherein each CI is a vector associated with a time
stamp with N components,
computing a time series of denoised CI (DCI) by denoising the time series of CI
(TSCI), wherein each DCI is a vector associated with a time stamp with N
components,
computing a time series of concatenated CI (TSCCI) based on a
concatenation of the TSCI and the time series of DCI (TSDCI), wherein:
each concatenated CI (CCI) associated with a respective time stamp is a vector comprising 2N components and is computed by concatenating a
respective CI associated with the same respective time stamp and a
respective DCI associated with the same respective time stamp
corresponding to the respective CI,
a first set of N components among the 2N components are associated with the respective CI, each of the N components in the first set being
associated with a respective component of the respective CI,
a second set of N components among the 2N components are associated with the respective DCI, each of the N components in the second
set being associated with a respective component of the respective DCI,
computing a one dimensional (1D) characteristic function of CCI of the TSCCI in
a sliding time window,
computing a two dimensional (2D) decomposition of the TSCCI based on the 1D
characteristic function in the sliding time window,
enhancing the 2D decomposition based on a vertical highpass horizontal lowpass
filtering, and
monitoring the periodic motion of the vital sign based on the enhanced 2D
decomposition.
(Emphasis added.)
The Office Action correctly noted that Liu does not disclose "computing a time series of
concatenated CI (TSCCI) based on a concatenation of the TSCI and the time series of DCI
(TSDCI)," as recited in claim 1. The Office Action then relied on Xu to teach these features.
See, Office Action, pp. 4-5. Xu performed CSI denoising using a Butterworth bandpass filter
with upper and lower cutoff frequency being 90Hz and 5Hz respectively. See, Xu, pp. 5-6. But
after denoising the CSI, Xu did not re-use the original CSI that is not denoised. Xu failed to
use
BOTH
the original CSI and the denoised CSI together. In particular, Xu failed to teach or suggest a concatenation of the original CSI and the denoised CSI.
In contrast, as required by Claim 1, after CI is denoised to generate DCI, the original CI continues to be used to form CCI, by concatenating the original CI with the DCI. In particular, Claim 1 requires that a N-dimensional original CI vector is concatenated with a N-dimensional DCI vector to form a 2N-dimensional CCI vector.
Although Xu mentioned "[b]y concatenating the first-order derivative, namely the first- order difference, of the primary profile, the proposed method gets a 384xT dimensional walking profile for each CSI stream," this concatenation in Xu is different from the concatenation in the amended claim 1. See, Xu, p. 7. First, Xu stacked several "PCA components" computed from denoised CSI "to form the primary walking profile with dimensions 192xT for each PCA component, where T is the length of the spectrogram (i.e., the amount of chunks)". Id. These PCA components in Xu are different from original CSI and denoised CSI. The T chunks of data in Xu are stacked with a different time stamp, rather than concatenating the original CSI and the denoised CSI at the same time stamp. Then, Xu concatenated first-order derivative of these "PCA components," which is also totally different from concatenating the original CSI and the denoised CSI. Id.
As such, Xu does not teach or suggest: "computing a time series of concatenated CI (TSCCI) based on a concatenation of the TSCI and the time series of DCI (TSDCI), wherein: each concatenated CI (CCI) associated with a respective time stamp is a vector comprising 2N components and is computed by concatenating a respective CI associated with the same respective time stamp and a respective DCI associated with the same respective time stamp corresponding to the respective CI, a first set of N components among the 2N components are
associated with the respective CI, each of the N components in the first set being associated with a respective component of the respective CI, a second set of N components among the 2N components are associated with the respective DCI, each of the N components in the second set being associated with a respective component of the respective DCI," as recited in the amended claim 1.
In addition, the Office Action correctly noted that Liu does not disclose "enhancing the 2D decomposition based on a [non-separable] vertical highpass horizontal lowpass filtering," as recited in claim 1. The Office Action then relied on Suyambukesan to teach these features. See, Office Action, p. 5. Suyambukesan applied a vertical high pass filter followed by a horizontal low pass filter to a video frame for a display device. See, Suyambukesan, [0028]. But the 2D decomposition of the TSCCI in Claim 1 is vastly different from video frames to a person with ordinary skill in the art (POSITA). While a vertical highpass horizontal lowpass filter can preserve horizontal lines and remove lines in other direction (e.g. vertical lines), such horizontal lines are common in video frames because of the many horizontal edges of tables, chairs, wall, floor, etc. But it was NOT well-known to POSITA that such line structures in video frames would also occur in the 2D decomposition of the TSCCI in Claim 1. Thus, there was no motivation for a POSITA to apply the vertical highpass filter and the horizontal lowpass filter of Suyambukesan to the 2D decomposition of the TSCCI in Claim 1.
It is well settled that establishing a prima facie case of obviousness requires that all claim limitations recited in a claim must be taught or suggested by the prior art. MPEP §2143.03. Clearly, taken individually or in combination, the cited references do not pass this test. Thus, a prima facie case of obviousness cannot be established with respect to claim 1. Thus, claim 1 is patentable.
Claims 2-18 depend from claim 1, and are thus likewise patentable for at least the same reasons as discussed above with respect to claim 1, and for the additional features recited therein.
Independent claims 19-20 have amended to include similar features as claim 1, and are thus likewise patentable for at least the same reasons as discussed above with respect to claim 1, and for the additional features recited therein.
In view of the above amendments and remarks, Applicant respectfully submits that all rejections under 35 U.S.C. 103 are overcome and should be withdrawn.
No Disclaimers or Disavowals
Although the present communication may include alterations to the application or claims, or characterizations of claim scope or referenced art, Applicant is not conceding in this application that previously pending claims are not patentable over the cited references. Rather, any alterations or characterizations are being made to facilitate expeditious prosecution of this application. Applicant reserves the right to pursue at a later date any previously pending or other broader or narrower claims that capture any subject matter supported by the present disclosure, including subject matter found to be specifically disclaimed herein or by any prior prosecution. Accordingly, reviewers of this or any parent, child or related prosecution history shall not reasonably infer that Applicant has made any disclaimers or disavowals of any subject matter supported by the present application.
Conclusion


CITED REFERENCE SUMMARIES: Ref US20200200887A1: Claims:
A method for removing ground clutter data from time series data, the method comprising: receiving first time series data; dividing the first time series data into a plurality of subsets of first time series data; applying a first regression filter to each respective subset first time series data of the plurality of subsets of first time series data to generate a plurality of regression filtered subsets of first time series data; and concatenating the plurality of regression filtered subsets of first time series data to generate a regression filtered first time series data.
receiving first time series data;
dividing the first time series data into a plurality of subsets of first time series data;
applying a first regression filter to each respective subset first time series data of the plurality of subsets of first time series data to generate a plurality of regression filtered subsets of first time series data; and
concatenating the plurality of regression filtered subsets of first time series data to generate a regression filtered first time series data.
The method of claim 1 , wherein each respective subset of the first time series data of the plurality of subsets of first time series data is a single subset size.
The method of claim 1 , wherein no two respective subsets of the first time series data of the plurality of subsets of first time series data include a shared time series data point.
The method of claim 1 , wherein each adjacent pair of subsets of the first time series data of the plurality of subsets of first time series data include at least one shared time series data point that other subsets of the plurality of subsets of the first time series do not include.
The method of claim 4 , wherein concatenating the plurality of regression filtered subsets of first time series data to generate the regression filtered first time series data further comprises: averaging the at least one shared time series data point for each respective regression filtered subset of first time series data for the adjacent pair of subsets of the first time series data to include in the regression filtered first time series data.
averaging the at least one shared time series data point for each respective regression filtered subset of first time series data for the adjacent pair of subsets of the first time series data to include in the regression filtered first time series data.
The method of claim 4 , wherein each adjacent pair of subsets of the first time series data of the plurality of subsets of first time series data include a same number of shared time series data points.
The method of claim 1 , wherein the first time series data corresponds to a first elevation angle, and the method further comprises: receiving a second time series data, corresponding to a second elevation angle that is higher than the first elevation angle; dividing the second time series data into a plurality of subsets of second time series data; applying a second regression filter to each respective subset second time series data of the plurality of subsets of second time series data to generate a plurality of regression filtered subsets of second time series data; concatenating the plurality of regression filtered subsets of first time series data to generate a regression filtered second time series data, wherein the first regression filter has a first regression filter length, and the second regression filter has a second regression filter length, the first regression filter length being larger than the second regression filter length.
receiving a second time series data, corresponding to a second elevation angle that is higher than the first elevation angle;
dividing the second time series data into a plurality of subsets of second time series data;
applying a second regression filter to each respective subset second time series data of the plurality of subsets of second time series data to generate a plurality of regression filtered subsets of second time series data;
concatenating the plurality of regression filtered subsets of first time series data to generate a regression filtered second time series data,
wherein the first regression filter has a first regression filter length, and the second regression filter has a second regression filter length, the first regression filter length being larger than the second regression filter length.
The method of claim 1 , wherein the first time series data corresponds to a first azimuth angle, and the method further comprises: receiving a second time series data, corresponding to a second azimuth angle that is different from the first azimuth angle; dividing the second time series data into a plurality of subsets of second time series data; applying a second regression filter to each respective subset second time series data of the plurality of subsets of second time series data to generate a plurality of regression filtered subsets of second time series data; concatenating the plurality of regression filtered subsets of first time series data to generate a regression filtered second time series data, wherein the first regression filter has a first regression filter length, and the second regression filter has a second regression filter length, the first regression filter length being larger than the second regression filter length.
receiving a second time series data, corresponding to a second azimuth angle that is different from the first azimuth angle;
The method of claim 1 , further comprising: determining one or more radar variables from the regression filtered first time series data.
determining one or more radar variables from the regression filtered first time series data.
A system for removing ground clutter data from time series data, the system comprising: a first data receiving module operable to receive first time series data; a first dividing module operable to divide the first time series data into a plurality of subsets of first time series data; a first filtering module operable to apply a first regression filter to each respective subset first time series data of the plurality of subsets of first time series data to generate a plurality of regression filtered subsets of first time series data; and a first concatenation module operable to concatenate the plurality of regression filtered subsets of first time series data to generate a regression filtered first time series data.
a first data receiving module operable to receive first time series data;
a first dividing module operable to divide the first time series data into a plurality of subsets of first time series data;
a first filtering module operable to apply a first regression filter to each respective subset first time series data of the plurality of subsets of first time series data to generate a plurality of regression filtered subsets of first time series data; and
a first concatenation module operable to concatenate the plurality of regression filtered subsets of first time series data to generate a regression filtered first time series data.
The system of claim 10 , wherein each respective subset of the first time series data of the plurality of subsets of first time series data is a single subset size.
The system of claim 10 , wherein no two respective subsets of the first time series data of the plurality of subsets of first time series data include a shared time series data point.
The system of claim 10 , wherein each adjacent pair of subsets of the first time series data of the plurality of subsets of first time series data include at least one shared time series data point.
The system of claim 13 , the system further comprises: an averaging module configured to average the at least one shared time series data point for each respective regression filtered subset of first time series data for each subset of the pair of subsets of the first time series data to include in the regression filtered first time series data.
an averaging module configured to average the at least one shared time series data point for each respective regression filtered subset of first time series data for each subset of the pair of subsets of the first time series data to include in the regression filtered first time series data.
The system of claim 13 , wherein each adjacent pair of subsets of the first time series data of the plurality of subsets of first time series data include a same number of shared time series data points.
The system of claim 10 , wherein the first time series data corresponds to a first elevation angle, and the system further comprises: a second data receiving module configured to receive a second time series data, corresponding to a second elevation angle that is higher than the first elevation angle; a second dividing module configured to divide the second time series data into a plurality of subsets of second time series data; a second filter module configured to apply a second regression filter to each respective subset second time series data of the plurality of subsets of second time series data to generate a plurality of regression filtered subsets of second time series data; a second concatenation module configured to concatenate the plurality of regression filtered subsets of first time series data to generate a regression filtered second time series data, wherein the first regression filter has a first regression filter length, and the second regression filter has a second regression filter length, the first regression filter length being larger than the second regression filter length.
a second data receiving module configured to receive a second time series data, corresponding to a second elevation angle that is higher than the first elevation angle;
a second dividing module configured to divide the second time series data into a plurality of subsets of second time series data;
a second filter module configured to apply a second regression filter to each respective subset second time series data of the plurality of subsets of second time series data to generate a plurality of regression filtered subsets of second time series data;
a second concatenation module configured to concatenate the plurality of regression filtered subsets of first time series data to generate a regression filtered second time series data,
The system of claim 10 , wherein the first time series data corresponds to a first azimuth angle, and the system further comprises: a second data receiving module configured to receive a second time series data, corresponding to a second azimuth angle that does not overlap the first range of azimuth angles; a second dividing module configured to divide the second time series data into a plurality of subsets of second time series data; a second filter module configured to apply a second regression filter to each respective subset second time series data of the plurality of subsets of second time series data to generate a plurality of regression filtered subsets of second time series data; a second concatenation module configured to concatenate the plurality of regression filtered subsets of first time series data to generate a regression filtered second time series data, wherein the first regression filter has a first regression filter length, and the second regression filter has a second regression filter length, the first regression filter length being larger than the second regression filter length.
a second data receiving module configured to receive a second time series data, corresponding to a second azimuth angle that does not overlap the first range of azimuth angles;
The system of claim 10 , further comprising: a radar variable determination module configured to determine one or more radar variables from regression filtered first time series data.
a radar variable determination module configured to determine one or more radar variables from regression filtered first time series data.