DETAILED ACTION
This action is in response to the initial filing filed on January 8, 2025, claim 1-20 have been examined this application.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 1/8/2025 has been acknowledged.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-6, 8-9, 11-13, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US 2013/0201054 A1) in view of Nitzberg (IEEE, 1986).
Regarding Claim 1, Wang teaches a RADAR system comprising a Constant False Alarm Rate (CFAR), function [0061 for applying CFAR to adaptive window],
wherein the CFAR function is arranged such that a detection threshold is determined at least partly on the basis of a window length which is of a variable length and the variable length [0061 for reference window is a rectangle which is increased in size or stretched along the range dimension and reduced in size]
is determined on the basis of a degree of variability [0057 for variance of the range-Doppler values in the reference window. In an alternative embodiment, the statistic can be the standard deviation].
Wang fails to explicitly teach in a number of previous amplitude measurements of received signals.
Nitzberg has an analysis of the probability of target detection for a clutter map CFAR [page 419, left column, first paragraph] and teaches in a number of previous amplitude measurements of received signals [page 419, right column, first paragraph for output of each resolution cell is averaged over several scans and equation (3)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the CFAR function techniques, as disclosed by Wang, further including the amplitude calculations as taught by Nitzberg for the purpose to reduce storage by smoothing each resolution cell (Nitzberg, page 419, right column, third paragraph).
Regarding Claim 8, Wang teaches method of Constant False Alarm Rate (CFAR) signal processing in a RADAR system [0061 for applying CFAR to adaptive window],
the method comprising: determining a degree of variability [0061]
assigning a CFAR window length on the basis of the determined degree of variability [0061 for reference window is a rectangle which is increased in size or stretched along the range dimension and reduced in size];
and setting a detection threshold for CFAR operation on the basis of the assigned window length [0057 for variance of the range-Doppler values in the reference window. In an alternative embodiment, the statistic can be the standard deviation].
Wang fails to explicitly teach in a first number of previous amplitude measurements of received signals.
Nitzberg has an analysis of the probability of target detection for a clutter map CFAR [page 419, left column, first paragraph] and teaches in a first number of previous amplitude measurements of received signals [page 419, right column, first paragraph for output of each resolution cell is averaged over several scans and equation (3)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the CFAR function techniques, as disclosed by Wang, further including the amplitude calculations as taught by Nitzberg for the purpose to reduce storage by smoothing each resolution cell (Nitzberg, page 419, right column, third paragraph).
Regarding Claim 2 and 9, Wang teaches the degree of variability is classified on the basis of one or more thresholds [0059 for preset threshold is determined by comparing the classification results with the signal spectrum].
Regarding Claim 4, 11, 15, and 16, Wang teaches the CFAR function is arranged such that a square of an amplitude value of a Cell Under Test, (CUT) [0057-0058 for square detector can be applied to the range-Doppler values],
is compared with squares of the values of the first number of measurements and the degree of variability is classified as high, medium, or low according to three thresholds [0059 for reset threshold is determined by comparing the classification results with the signal spectrum to verify that the classification results].
Wang fails to explicitly teach is compared with squares of the values of the first number of previous amplitude measurements.
Nitzberg has an analysis of the probability of target detection for a clutter map CFAR [page 419, left column, first paragraph] and teaches is compared with squares of the values of the first number of previous amplitude measurements [page 419, right column, first paragraph for output of each resolution cell is averaged over several scans and equation (3)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the CFAR function techniques, as disclosed by Wang, further including the amplitude calculations as taught by Nitzberg for the purpose to reduce storage by smoothing each resolution cell (Nitzberg, page 419, right column, third paragraph).
Regarding Claim 5 and 12, Wang teaches the one or more thresholds are fixed and are arranged such that: a high degree of variability is determined if a top 10% of measurements are divergent from a recent mean [0061 for reference window is a rectangle which is increased in size or stretched along the range dimension and reduced in size];
a medium degree of variability is determined if a middle-high 20% of measurements are divergent from the recent mean [0062 for reference window is near the ionospheric clutter region and has been oriented such that it has a rectangular shape (means to change window based on different parameters)];
and a low degree of variability is determined if a bottom 70% of measurements are divergent from the recent mean [063 for ionospheric clutter is declared at the top of the reference window and the reference window is shifted downwards. If the ratio is smaller than the reciprocal of the threshold].
Regarding Claim 6 and 13, Wang teaches the window length is arranged to be longer for a relatively lower degree of variability, and shorter for a relatively higher degree of variability [0061 for reference window is a rectangle which is increased in size or stretched along the range dimension and reduced in size].
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US 2013/0201054 A1) in view of Nitzberg (IEEE, 1986), as applied to Claim 1 above, and further in view of Watts (US 2009/0315757 A1).
Regarding Claims 3 and 10, Wang fails to explicitly teach the one or more thresholds are arranged to be one of: fixed at time of configuration; or variable under the control of an operator.
Watts has a method of analysing return signals of successive range cells in a scene (abstract) and teaches the one or more thresholds are arranged to be one of: fixed at time of configuration; or variable under the control of an operator [0029 for a small global offset, with the control having a maximum range of perhaps ±1 dB, to allow for cases where their clutter does not quite match the mode].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the CFAR function techniques, as disclosed by Wang, further including the offset calculations as taught by Watts for the purpose to achieve a rapid response (Watts, 0031).
Claims 7, 14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US 2013/0201054 A1) in view of Nitzberg (IEEE, 1986), as applied to Claim 1 above, and further in view of Tabet et al (SIViP, 2009).
Regarding Claim 7 and 14, Wang fails to explicitly teach the CFAR function is further arranged such that an amplitude of a received pulse in a current Cell Under Test (CUT), is compared to a plurality of recently received pulses to determine if the current CUT lies in an upper range of recently received pulses and, if so, a predetermined false alarm rate is applied for the current cell CUT, wherein the predetermined false alarm rate is lower than would be applied if the current CUT did not lie in the upper range.
Tabet has new constant false alarm rate (CFAR) thresholding algorithm which is a generalisation of the switching CFAR (Abstract) teaches the CFAR function is further arranged such that an amplitude of a received pulse in a current Cell Under Test (CUT), is compared to a plurality of recently received pulses to determine if the current CUT lies in an upper range of recently received pulses and [page 268, right column, last paragraph for using a threshold multiplied in the high probability for cells under test],
if so, a predetermined false alarm rate is applied for the current cell CUT, wherein the predetermined false alarm rate is lower than would be applied if the current CUT did not lie in the upper range [page 269, left column, first paragraph for demonstrated for a CFAR window size of 2N =24.Inorder to detect targets near the clutter edge (edge cells)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the CFAR function techniques, as disclosed by Wang, further including the threshold calculations as taught by Tabet for the purpose to minimize losses (Tabet, page 268, right column, last paragraph).
Regarding Claim 17, Wang teaches RADAR system comprising a Constant False Alarm Rate (CFAR) function [0061 for applying CFAR to adaptive window],
wherein the CFAR function is arranged such that a detection threshold is determined at least partly on the basis of a window length which is of a variable length and the variable length [0061 for reference window is a rectangle which is increased in size or stretched along the range dimension and reduced in size]
is determined on the basis of a degree of variability [0061],
wherein the window length is arranged to be longer for a relatively lower degree of variability [0061 for reference window is a rectangle which is increased in size or stretched along the range dimension and reduced in size],
and shorter for a relatively higher degree of variability [0061].
Wang fails to explicitly teach in a number of previous amplitude measurements of received signals.
Nitzberg has an analysis of the probability of target detection for a clutter map CFAR [page 419, left column, first paragraph] and teaches in a number of previous amplitude measurements of received signals [page 419, right column, first paragraph for output of each resolution cell is averaged over several scans and equation (3)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the CFAR function techniques, as disclosed by Wang, further including the amplitude calculations as taught by Nitzberg for the purpose to reduce storage by smoothing each resolution cell (Nitzberg, page 419, right column, third paragraph).
Wang fails to explicitly teach and wherein the CFAR function is further arranged such that an amplitude of a received pulse in a current Cell Under Test (CUT) is compared to a plurality of recently received pulses to determine if the current CUT lies in an upper range of recently received pulses and, if so, a predetermined false alarm rate is applied for the current CUT, wherein the predetermined false alarm rate is lower than would be applied if the current CUT did not lie in the upper range.
Tabet has new constant false alarm rate (CFAR) thresholding algorithm which is a generalisation of the switching CFAR (Abstract) and teaches and wherein the CFAR function is further arranged such that an amplitude of a received pulse in a current Cell Under Test (CUT) is compared to a plurality of recently received pulses to determine if the current CUT lies in an upper range of recently received pulses [page 268, right column, last paragraph for using a threshold multiplied in the high probability for cells under test],
if so, a predetermined false alarm rate is applied for the current cell CUT, wherein the predetermined false alarm rate is lower than would be applied if the current CUT did not lie in the upper range [page 269, left column, first paragraph for demonstrated for a CFAR window size of 2N =24.Inorder to detect targets near the clutter edge (edge cells)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the CFAR function techniques, as disclosed by Wang, further including the threshold calculations as taught by Tabet for the purpose to minimize losses (Tabet, page 268, right column, last paragraph).
Regarding Claim 18, Wang teaches the degree of variability is classified on the basis of one or more thresholds [0059 for preset threshold (fixed) is determined by comparing the classification results];
and the one or more thresholds are arranged to be fixed at time of configuration or variable under the control of an operator [0065 for maximum and minimum percentage thresholds can be set based on extremely noisy and quiet scenarios for the environment of the CUT].
Regarding Claim 19, Wang teaches the CFAR function is arranged such that a square of an amplitude value of a Cell Under Test, (CUT) [0057-0058 for square detector can be applied to the range-Doppler values],
is compared with squares of the values of the first number of measurements and the degree of variability is classified as high, medium, or low according to three thresholds [0059 for reset threshold is determined by comparing the classification results with the signal spectrum to verify that the classification results].
Wang fails to explicitly teach is compared with squares of the values of the first number of previous amplitude measurements.
Nitzberg has an analysis of the probability of target detection for a clutter map CFAR [page 419, left column, first paragraph] and teaches is compared with squares of the values of the first number of previous amplitude measurements [page 419, right column, first paragraph for output of each resolution cell is averaged over several scans and equation (3)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the CFAR function techniques, as disclosed by Wang, further including the amplitude calculations as taught by Nitzberg for the purpose to reduce storage by smoothing each resolution cell (Nitzberg, page 419, right column, third paragraph).
Regarding Claim 20, Wang teaches the one or more thresholds are fixed and are arranged such that: a high degree of variability is determined if a top 10% of measurements are divergent from a recent mean [0061 for reference window is a rectangle which is increased in size or stretched along the range dimension and reduced in size];
a medium degree of variability is determined if a middle-high 20% of measurements are divergent from the recent mean [0062 for reference window is near the ionospheric clutter region and has been oriented such that it has a rectangular shape (means to change window based on different parameters)];
and a low degree of variability is determined if a bottom 70% of measurements are divergent from the recent mean [063 for ionospheric clutter is declared at the top of the reference window and the reference window is shifted downwards. If the ratio is smaller than the reciprocal of the threshold].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Smith et al (IEEE, 2000) has an intelligent constant false alarm rate (CFAR) processor to perform adaptive threshold target detection.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
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