DETAILED ACTION
This communication is responsive to Application No. #18/608,201 filed on March 18, 2024. Claims 1-20 are subject to examination.
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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stockmaster et.al. (US Patent Number US10554451B1, hereinafter, “Stockmaster”) in view of Calvanese Strinati (US Patent Application Publication, 20090013235, hereinafter, “Calvanese Strinati”), further in view of Le-Ngoc (US Patent Application Publication, 20170264347, hereinafter, “Le-Ngoc”).
Regarding claim 1, Stockmaster teaches:
An adaptive signal processing system, the system comprising (Stockmaster: [Column 5 lines 14-15] Referring to FIG. 1, one example embodiment of a system for signal processing is depicted ... Figs. 1, 2):
a fast Fourier transform (FFT) circuit configured to (Stockmaster: [Column 5, lines 32-26] … The signal processor (sometimes referred to as a digital signal processor) 100 can include time-domain to transform-domain transform (ITT) engines 110, that can carry out a ITT such as a fast Fourier transform (FFT) ... Figs. 1, 2) convert T blocks of time domain signals to T blocks of frequency domain signals for each of C channels, the T blocks of time domain signals derived from each of the C channels of an antenna array, each frequency domain signal comprising N frequency domain bins (Stockmaster: [Column 10, lines 4-5] Referring now to step 200, and in some embodiments, an antenna array can receive signal information … [Column 10, lines 28-36] Referring now to step 202, and in some embodiments, a signal processor can perform a time domain to transform domain transform (TTT) with N transform domain bins, on the signal information. The signal processor 100 can perform the TTT (e.g., a FFT or wavelet transform) on the digital signal. The signal processor 100 can include TTT engines 110 for each receive chain, that can each carry out a TTT such as a fast Fourier transform (FFT), in which N points are taken resulting in N frequency bins … Figs. 1, 2);
a covariance calculator circuit configured to (Stockmaster: [Column 5, line 28] … A spatial processor 112 ... Figs. 1, 2) calculate N covariance matrices based on the T blocks of the C frequency domain signals for each of the N frequency domain bins (Stockmaster: [Column 10, lines 45-59] Referring now to step 204, and in some embodiments, the signal processor can determine N covariance matrices each corresponding to a respective one of the N transform domain bins. The spatial processor 112 can establish covariance matrices for each of the N bins. The signal processor can include a spatial processor 112 that can establish covariance matrices for each of the N bins. The TTT engine 110 can produce data for each antenna element (or channel) of the antenna array 102. The spatial processor 112 can use the data corresponding to each antenna element to form a covariance matrix for each of the N bins. For instance, a covariance matrix of size K×K can be established for an antenna array 102 with K antenna elements. The covariance matrix can be indicative of a spatial orientation of jamming signal(s) relative to the antenna elements. Figs. 1, 2);
a covariance combiner circuit configured to (Stockmaster: [Column 5, line 28] … A spatial processor 112 ... Figs. 1, 2) generate M combined covariance matrices by combining groups of covariance matrices from the N covariance matrices, each group corresponding to a respective grouping of adjacent frequency domain bins from the N frequency domain bins (Stockmaster: [Column 10, lines 60-67] Referring now to step 206, and in some embodiments, the signal processor can group covariance matrices from the N covariance matrices into groups of M covariance matrices. Each group can correspond to a respective group of M adjacent transform domain bins from the N transform domain bins. A user or an algorithm (e.g., using a feedback system) can set a value for M to achieve a desired minimum level of system performance … Figs. 1, 2);
a time segment extractor configured to (Stockmaster: [Column 5, line 41] … the excision component 118 ... Figs. 1, 2);and
a weight solver circuit configured to (Stockmaster: [Column 5, line 28] … A spatial processor 112 ... Figs. 1, 2) calculate weights based on the M reduced covariance matrices to control the antenna array (Stockmaster: [Column 11, lines 43-53] Referring now to step 210, and in some embodiments, the signal processor can calculate spatial weights from each of the spatial covariance matrices, for anti-jamming or interference mitigation processing, to control or steer the antenna array. The spatial processor 112 can calculate spatial weights corresponding to each of the spatial covariance matrices. The spatial processor 122 can use an adaptive weight solver to calculate spatial weights corresponding to each of the spatial covariance matrices. The spatial weights can represent a solution for maximizing SINR (and/or satisfying a certain constraint) for the antenna array 102. Figs. 1, 2).
Although Stockmaster teaches keeping a fixed transform-domain structure with N bins but varying spatial-processing resolution by grouping adjacent covariance matrices into sets of M and combining them into fewer spatial covariance matrices, Stockmaster does not explicitly teach:
each of the covariance matrices of size C times T by C times T;
generate M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T.
However, in the same field of endeavor, Calvanese Strinati teaches:
each of the covariance matrices of size C times T by C times T (Calvanese Strinati: [0065] where K is the number of carrier signals of the OFDM symbol, P is the number of receiving antennae, IKP is the matrix unit of size KP x KP ...).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Stockmaster to include the features as taught by Calvanese Strinati above in order to avoid burdening its resources needlessly. (Calvanese Strinati, ¶ [0011]).
Although Stockmaster-Calvanese Strinati teaches keeping a fixed transform-domain structure with N bins but varying spatial-processing resolution by grouping adjacent covariance matrices into sets of M and combining them into fewer spatial covariance matrices, and splitting CSI-derived channel behavior into fast-varying and slow-varying characteristics, then assigning those characteristics to different matrix stages in the transmit and receive paths, Stockmaster-Calvanese Strinati does not explicitly teach:
generate M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T.
However, in the same field of endeavor, Le-Ngoc teaches:
generate M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T (Le-Ngoc: [0051] A particular example manner of identifying the first and second time-varying characteristics of a wireless resource will now be described for an embodiment where transmit antenna array 180 has N antenna elements and is communicating over the downlink channel C.sub.D 144 with UE devices 192, 194 having M total antenna elements. CSI 142 includes measured rows c.sub.(i), i=1, . . . , N, and measured columns c.sup.(j), j=1, . . . , M, of a channel matrix ζ.sub.D representing the downlink channel's state. The channel correlation can be represented by its co-variance matrix R.sub.C.sub.D and can be well approximated by the Kronecker product (custom-character) of the covariance matrices seen from both transmitting and receiving ends. That is. R.sub.C.sub.D=R.sub.C.sub.D.sup.Txcustom-characterR.sub.C.sub.D.sup.Rx where R.sub.C.sub.D.sup.Tx=E{([c.sub.(i)].sup.Hc.sub.(i)).sup.T}, R.sub.C.sub.D.sup.Rx=E{c.sup.(j)[c.sup.(j)].sup.H}, c.sub.(i), i=1, . . . . . N, is the i.sup.th row and c.sup.(j), j=1, . . . , M, is the j.sup.th column of the channel matrix ζ.sub.D. [.].sup.T is the transpose operator, [.].sup.H is the complex conjugate transpose operator, and E{.} denotes the expectation, for example as computed by long-time averaging or filtering. It is well known that the channel matrix ζ.sub.D=[R.sub.C.sub.D.sup.Rx].sup.1/2G.sub.D[(R.sub.C.sub.D.sup.Tx).sup.H].sup.1/2 where G.sub.D is a stochastic N by M matrix with independent and identically distributed zero-mean, normalized-variance Gaussian-distributed random elements, and [.].sup.1/2 denotes the matrix square root operation. i.e., R=[R].sup.1/2([R].sup.1/2).sup.H. Therefore, for a fast time-varying channel C.sub.D, the channel co-variance matrix R.sub.C.sub.D represents the slowly time-varying aspects of the channel, while G.sub.D represents the same fast time-varying aspects of C.sub.D. Thus, to determine the fast and slowly time-varying aspects of the channel C.sub.D, the controller 140 processes the CSI 142 to extract the channel co-variance matrix R.sub.C.sub.D indicating the slowly time-varying aspects of the channel and to extract the matrix G.sub.D indicating fast time-varying aspects of C.sub.D …).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Stockmaster-Calvanese Strinati to include the features as taught by Le-Ngoc above in order to mitigate self-interference between transmitted and received signals. (Le-Ngoc, ¶ [Abstract]).
Regarding claim 8, Stockmaster teaches:
A computer program product including one or more non-transitory machine-readable mediums encoded with instructions that when executed by one or more processors cause a process to be carried out for adaptive signal processing, the process comprising (Stockmaster: [Column 13 lines 10-17] … Embodiments within the scope of the inventive concepts disclosed herein include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a special purpose computer or other machine with an operational flow... Figs. 1, 2):
buffering T blocks of time domain signals received from each of C channels of an antenna array;
converting the T blocks of time domain signals to T blocks of frequency domain signals for each of the C channels, each frequency domain signal comprising N frequency domain bins (Stockmaster: [Column 10, lines 4-5] Referring now to step 200, and in some embodiments, an antenna array can receive signal information … [Column 10, lines 28-36] Referring now to step 202, and in some embodiments, a signal processor can perform a time domain to transform domain transform (TTT) with N transform domain bins, on the signal information. The signal processor 100 can perform the TTT (e.g., a FFT or wavelet transform) on the digital signal. The signal processor 100 can include TTT engines 110 for each receive chain, that can each carry out a TTT such as a fast Fourier transform (FFT), in which N points are taken resulting in N frequency bins … Figs. 1, 2);
calculating N covariance matrices based on the T blocks of the C frequency domain signals for each of the N frequency domain bins (Stockmaster: [Column 10, lines 45-59] Referring now to step 204, and in some embodiments, the signal processor can determine N covariance matrices each corresponding to a respective one of the N transform domain bins. The spatial processor 112 can establish covariance matrices for each of the N bins. The signal processor can include a spatial processor 112 that can establish covariance matrices for each of the N bins. The TTT engine 110 can produce data for each antenna element (or channel) of the antenna array 102. The spatial processor 112 can use the data corresponding to each antenna element to form a covariance matrix for each of the N bins. For instance, a covariance matrix of size K×K can be established for an antenna array 102 with K antenna elements. The covariance matrix can be indicative of a spatial orientation of jamming signal(s) relative to the antenna elements. Figs. 1, 2);
generating M combined covariance matrices by combining groups of covariance matrices from the N covariance matrices, each group corresponding to a respective grouping of adjacent frequency domain bins from the N frequency domain bins (Stockmaster: [Column 10, lines 60-67] Referring now to step 206, and in some embodiments, the signal processor can group covariance matrices from the N covariance matrices into groups of M covariance matrices. Each group can correspond to a respective group of M adjacent transform domain bins from the N transform domain bins. A user or an algorithm (e.g., using a feedback system) can set a value for M to achieve a desired minimum level of system performance … Figs. 1, 2); and
calculating weights based on the M reduced covariance matrices to control the antenna array (Stockmaster: [Column 11, lines 43-53] Referring now to step 210, and in some embodiments, the signal processor can calculate spatial weights from each of the spatial covariance matrices, for anti-jamming or interference mitigation processing, to control or steer the antenna array. The spatial processor 112 can calculate spatial weights corresponding to each of the spatial covariance matrices. The spatial processor 122 can use an adaptive weight solver to calculate spatial weights corresponding to each of the spatial covariance matrices. The spatial weights can represent a solution for maximizing SINR (and/or satisfying a certain constraint) for the antenna array 102. Figs. 1, 2).
Although Stockmaster teaches keeping a fixed transform-domain structure with N bins but varying spatial-processing resolution by grouping adjacent covariance matrices into sets of M and combining them into fewer spatial covariance matrices, Stockmaster does not explicitly teach:
each of the covariance matrices of size C times T by C times T;
generating M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T.
However, in the same field of endeavor, Calvanese Strinati teaches:
each of the covariance matrices of size C times T by C times T (Calvanese Strinati: [0065] where K is the number of carrier signals of the OFDM symbol, P is the number of receiving antennae, IKP is the matrix unit of size KP x KP ...).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Stockmaster to include the features as taught by Calvanese Strinati above in order to avoid burdening its resources needlessly. (Calvanese Strinati, ¶ [0011]).
Although Stockmaster-Calvanese Strinati teaches keeping a fixed transform-domain structure with N bins but varying spatial-processing resolution by grouping adjacent covariance matrices into sets of M and combining them into fewer spatial covariance matrices, and splitting CSI-derived channel behavior into fast-varying and slow-varying characteristics, then assigning those characteristics to different matrix stages in the transmit and receive paths, Stockmaster-Calvanese Strinati does not explicitly teach:
generating M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T.
However, in the same field of endeavor, Le-Ngoc teaches:
generating M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T (Le-Ngoc: [0051] A particular example manner of identifying the first and second time-varying characteristics of a wireless resource will now be described for an embodiment where transmit antenna array 180 has N antenna elements and is communicating over the downlink channel C.sub.D 144 with UE devices 192, 194 having M total antenna elements. CSI 142 includes measured rows c.sub.(i), i=1, . . . , N, and measured columns c.sup.(j), j=1, . . . , M, of a channel matrix ζ.sub.D representing the downlink channel's state. The channel correlation can be represented by its co-variance matrix R.sub.C.sub.D and can be well approximated by the Kronecker product (custom-character) of the covariance matrices seen from both transmitting and receiving ends. That is. R.sub.C.sub.D=R.sub.C.sub.D.sup.Txcustom-characterR.sub.C.sub.D.sup.Rx where R.sub.C.sub.D.sup.Tx=E{([c.sub.(i)].sup.Hc.sub.(i)).sup.T}, R.sub.C.sub.D.sup.Rx=E{c.sup.(j)[c.sup.(j)].sup.H}, c.sub.(i), i=1, . . . . . N, is the i.sup.th row and c.sup.(j), j=1, . . . , M, is the j.sup.th column of the channel matrix ζ.sub.D. [.].sup.T is the transpose operator, [.].sup.H is the complex conjugate transpose operator, and E{.} denotes the expectation, for example as computed by long-time averaging or filtering. It is well known that the channel matrix ζ.sub.D=[R.sub.C.sub.D.sup.Rx].sup.1/2G.sub.D[(R.sub.C.sub.D.sup.Tx).sup.H].sup.1/2 where G.sub.D is a stochastic N by M matrix with independent and identically distributed zero-mean, normalized-variance Gaussian-distributed random elements, and [.].sup.1/2 denotes the matrix square root operation. i.e., R=[R].sup.1/2([R].sup.1/2).sup.H. Therefore, for a fast time-varying channel C.sub.D, the channel co-variance matrix R.sub.C.sub.D represents the slowly time-varying aspects of the channel, while G.sub.D represents the same fast time-varying aspects of C.sub.D. Thus, to determine the fast and slowly time-varying aspects of the channel C.sub.D, the controller 140 processes the CSI 142 to extract the channel co-variance matrix R.sub.C.sub.D indicating the slowly time-varying aspects of the channel and to extract the matrix G.sub.D indicating fast time-varying aspects of C.sub.D …).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Stockmaster-Calvanese Strinati to include the features as taught by Le-Ngoc above in order to mitigate self-interference between transmitted and received signals. (Le-Ngoc, ¶ [Abstract]).
Regarding claim 15, Stockmaster teaches:
A method for adaptive signal processing, the method comprising (Stockmaster: [Column 5 lines 14-15] Referring to FIG. 1, one example embodiment of a system for signal processing is depicted ... Figs. 1, 2):
converting T blocks of buffered time domain signals to T blocks of frequency domain signals for each of C channels, the T blocks of buffered time domain signals received from each of the C channels of an antenna array, each frequency domain signal comprising N frequency domain bins (Stockmaster: [Column 10, lines 4-5] Referring now to step 200, and in some embodiments, an antenna array can receive signal information … [Column 10, lines 28-36] Referring now to step 202, and in some embodiments, a signal processor can perform a time domain to transform domain transform (TTT) with N transform domain bins, on the signal information. The signal processor 100 can perform the TTT (e.g., a FFT or wavelet transform) on the digital signal. The signal processor 100 can include TTT engines 110 for each receive chain, that can each carry out a TTT such as a fast Fourier transform (FFT), in which N points are taken resulting in N frequency bins … Figs. 1, 2);
calculating N covariance matrices based on the T blocks of the C frequency domain signals for each of the N frequency domain bins (Stockmaster: [Column 10, lines 45-59] Referring now to step 204, and in some embodiments, the signal processor can determine N covariance matrices each corresponding to a respective one of the N transform domain bins. The spatial processor 112 can establish covariance matrices for each of the N bins. The signal processor can include a spatial processor 112 that can establish covariance matrices for each of the N bins. The TTT engine 110 can produce data for each antenna element (or channel) of the antenna array 102. The spatial processor 112 can use the data corresponding to each antenna element to form a covariance matrix for each of the N bins. For instance, a covariance matrix of size K×K can be established for an antenna array 102 with K antenna elements. The covariance matrix can be indicative of a spatial orientation of jamming signal(s) relative to the antenna elements. Figs. 1, 2);
generating M combined covariance matrices by combining groups of covariance matrices from the N covariance matrices, each group corresponding to a respective grouping of adjacent frequency domain bins from the N frequency domain bins (Stockmaster: [Column 10, lines 60-67] Referring now to step 206, and in some embodiments, the signal processor can group covariance matrices from the N covariance matrices into groups of M covariance matrices. Each group can correspond to a respective group of M adjacent transform domain bins from the N transform domain bins. A user or an algorithm (e.g., using a feedback system) can set a value for M to achieve a desired minimum level of system performance … Figs. 1, 2); and
calculating weights based on the M reduced covariance matrices to control the antenna array (Stockmaster: [Column 11, lines 43-53] Referring now to step 210, and in some embodiments, the signal processor can calculate spatial weights from each of the spatial covariance matrices, for anti-jamming or interference mitigation processing, to control or steer the antenna array. The spatial processor 112 can calculate spatial weights corresponding to each of the spatial covariance matrices. The spatial processor 122 can use an adaptive weight solver to calculate spatial weights corresponding to each of the spatial covariance matrices. The spatial weights can represent a solution for maximizing SINR (and/or satisfying a certain constraint) for the antenna array 102. Figs. 1, 2).
Although Stockmaster teaches keeping a fixed transform-domain structure with N bins but varying spatial-processing resolution by grouping adjacent covariance matrices into sets of M and combining them into fewer spatial covariance matrices, Stockmaster does not explicitly teach:
each of the covariance matrices of size C times T by C times T;
generating M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T.
However, in the same field of endeavor, Calvanese Strinati teaches:
each of the covariance matrices of size C times T by C times T (Calvanese Strinati: [0065] where K is the number of carrier signals of the OFDM symbol, P is the number of receiving antennae, IKP is the matrix unit of size KP x KP ...).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Stockmaster to include the features as taught by Calvanese Strinati above in order to avoid burdening its resources needlessly. (Calvanese Strinati, ¶ [0011]).
Although Stockmaster-Calvanese Strinati teaches keeping a fixed transform-domain structure with N bins but varying spatial-processing resolution by grouping adjacent covariance matrices into sets of M and combining them into fewer spatial covariance matrices, and splitting CSI-derived channel behavior into fast-varying and slow-varying characteristics, then assigning those characteristics to different matrix stages in the transmit and receive paths, Stockmaster-Calvanese Strinati does not explicitly teach:
generating M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T.
However, in the same field of endeavor, Le-Ngoc teaches:
generating M reduced covariance matrices by extracting a portion from each of the M combined covariance matrices, the portion corresponding to Z of the T blocks, wherein Z is less than or equal to T (Le-Ngoc: [0051] A particular example manner of identifying the first and second time-varying characteristics of a wireless resource will now be described for an embodiment where transmit antenna array 180 has N antenna elements and is communicating over the downlink channel C.sub.D 144 with UE devices 192, 194 having M total antenna elements. CSI 142 includes measured rows c.sub.(i), i=1, . . . , N, and measured columns c.sup.(j), j=1, . . . , M, of a channel matrix ζ.sub.D representing the downlink channel's state. The channel correlation can be represented by its co-variance matrix R.sub.C.sub.D and can be well approximated by the Kronecker product (custom-character) of the covariance matrices seen from both transmitting and receiving ends. That is. R.sub.C.sub.D=R.sub.C.sub.D.sup.Txcustom-characterR.sub.C.sub.D.sup.Rx where R.sub.C.sub.D.sup.Tx=E{([c.sub.(i)].sup.Hc.sub.(i)).sup.T}, R.sub.C.sub.D.sup.Rx=E{c.sup.(j)[c.sup.(j)].sup.H}, c.sub.(i), i=1, . . . . . N, is the i.sup.th row and c.sup.(j), j=1, . . . , M, is the j.sup.th column of the channel matrix ζ.sub.D. [.].sup.T is the transpose operator, [.].sup.H is the complex conjugate transpose operator, and E{.} denotes the expectation, for example as computed by long-time averaging or filtering. It is well known that the channel matrix ζ.sub.D=[R.sub.C.sub.D.sup.Rx].sup.1/2G.sub.D[(R.sub.C.sub.D.sup.Tx).sup.H].sup.1/2 where G.sub.D is a stochastic N by M matrix with independent and identically distributed zero-mean, normalized-variance Gaussian-distributed random elements, and [.].sup.1/2 denotes the matrix square root operation. i.e., R=[R].sup.1/2([R].sup.1/2).sup.H. Therefore, for a fast time-varying channel C.sub.D, the channel co-variance matrix R.sub.C.sub.D represents the slowly time-varying aspects of the channel, while G.sub.D represents the same fast time-varying aspects of C.sub.D. Thus, to determine the fast and slowly time-varying aspects of the channel C.sub.D, the controller 140 processes the CSI 142 to extract the channel co-variance matrix R.sub.C.sub.D indicating the slowly time-varying aspects of the channel and to extract the matrix G.sub.D indicating fast time-varying aspects of C.sub.D …).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Stockmaster-Calvanese Strinati to include the features as taught by Le-Ngoc above in order to mitigate self-interference between transmitted and received signals. (Le-Ngoc, ¶ [Abstract]).
Regarding claims 2, 9, and 16, the combination of Stockmaster-Calvanese Strinati and Le-Ngoc discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Stockmaster further teaches:
wherein the weights are calculated using a constraint minimization process to provide one or more of interference mitigation, beamforming, or nullforming (Stockmaster: [Column 6, lines 40-57] The antenna array 102 can be electronically steered. The antenna array 102 can operate with anti-jamming and/or interference removal mechanisms. For example, the spatial processor 112 can calculate spatial weights to steer the antenna array's antenna elements to null or remove jamming signals. The spatial weights can represent a solution for maximizing SINR (and/or satisfying other constraints, such as nulling a certain jammer, or limiting measurement errors below a threshold) for the antenna array 102. The system can use signal processing techniques such as space-frequency adaptive processing using FFTs for instance, to filter jamming or other interference signals from communications or positioning signals. The antenna array 102 can be controlled or configured to null, reduce or remove jamming and/or interference signals in a received GNSS signal for instance. The antenna array 102 can perform beamforming with its antenna elements to increase or improve reception of a desired signal …).
Regarding claims 3, 10, and 17, the combination of Stockmaster-Calvanese Strinati and Le-Ngoc discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Stockmaster further teaches:
a finite impulse response (FIR) filter configured to apply the weights to the frequency domain signals to generate weighted frequency bins (Stockmaster: [Column 12, lines 10-20] In some embodiments, the system can incorporate a higher resolution second stage, which corresponds to an excision or filtering stage. An excision component 118 can filter a result of the anti-jamming or interference mitigation processing, for example on antenna data from the bins after the spatial weights have been applied, at a resolution matched to the N bins. For instance, the spatial weights can be applied to data in the corresponding N bins, and the filtering can be performed on the data at a resolution matched to the N bins. This second stage can be optional and can be performed prior to the inverse TTT, for example.); and/or
an inverse fast Fourier transform (IFFT) circuit configured to convert the weighted frequency domain bins to the time domain to generate adaptively processed time domain signals (Stockmaster: [Column 11, lines 64-67, Column 12, lines 1-9] The spatial weights solved for a particular combined spatial covariance matrix can be applied to transform domain (e.g., frequency domain) data corresponding to the N bins. The spatial weights solved for a particular combined spatial covariance matrix can for example be applied to each bin associated with the group of covariances from which the combined spatial covariance matrix is formed, for example, before excision and/or an inverse TTT is performed. The spatial weights can be applied to data in respective ones of the N bins, and an inverse TTT (e.g., IFFT) can be performed by the ITTT engine 114 based on the N bins, to provide data in the time domain to be processed or used by a GNSS receiver for instance…).
Regarding claims 4, 11, and 18, the combination of Stockmaster-Calvanese Strinati and Le-Ngoc discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Stockmaster further teaches:
wherein a first value of M and a first value of Z are selected at a first time instance, and a second value of M and a second value of Z are selected at a second time instance (Stockmaster: [Column 2, lines 13-25] In some embodiments, the signal processing device can control the antenna array using a first value for M at a first time instance, and a second value for M at a second time instance. The signal processing device can set or receive a value for M in response to a level of interference detected in signals received via the antenna array. For instance, the value of M can be user controlled or automatically set in response to the type and level of interference detected in the signals. The system can further include an excision component configured to filter a result of the anti-jamming or interference mitigation processing, at a resolution matched to the N bins. The signal processing device can perform an inverse FFT (IFFT) with N bins …).
Regarding claims 5, 12, and 19, the combination of Stockmaster-Calvanese Strinati and Le-Ngoc discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Stockmaster further teaches:
wherein a value of Z is selected to be less than T to provide reduced resolution spatial time adaptive processing (Stockmaster: [Column 11, lines 20-42] Referring now to step 208 ... The spatial processor 112 can establish a number of spatial covariance matrices, each obtained by performing a weighted combination of covariance matrices within a corresponding group of M (or other number of) covariance matrices. This can effectively change the number of bins used for spatial processing and provides a means to vary the resolution of spatial processing without changing the underlying TTT structure (of N bins) … [Column 12, lines 33-50] The amount of weighted combination or summation (e.g., the value of M) or the amount of spatial processing can be controllable, and can be updated or adjusted in real-time based on monitoring the system performance or under control of an external user of the system. For instance, the signal processing device can control the antenna array using a first value for M at a first time instance, and a second value for M at a second time instance that is later than the first time instance. Where the first value is lower than the second value, this increase can be to implement an adjustment to the spatial processing to reduce a resolution of spatial processing to a level that still meets a desired performance requirement under reduced complexity jamming conditions …).
Regarding claims 6, 13, and 20, the combination of Stockmaster-Calvanese Strinati and Le-Ngoc discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Stockmaster further teaches:
wherein a value of Z is selected to be less than T to provide reduced resolution spatial time adaptive processing (Stockmaster: [Column 11, lines 20-42] Referring now to step 208 ... The spatial processor 112 can establish a number of spatial covariance matrices, each obtained by performing a weighted combination of covariance matrices within a corresponding group of M (or other number of) covariance matrices. This can effectively change the number of bins used for spatial processing and provides a means to vary the resolution of spatial processing without changing the underlying TTT structure (of N bins) … [Column 12, lines 33-50] The amount of weighted combination or summation (e.g., the value of M) or the amount of spatial processing can be controllable, and can be updated or adjusted in real-time based on monitoring the system performance or under control of an external user of the system. For instance, the signal processing device can control the antenna array using a first value for M at a first time instance, and a second value for M at a second time instance that is later than the first time instance. Where the first value is lower than the second value, this increase can be to implement an adjustment to the spatial processing to reduce a resolution of spatial processing to a level that still meets a desired performance requirement under reduced complexity jamming conditions …).
Regarding claims 7 and 14, the combination of Stockmaster-Calvanese Strinati and Le-Ngoc discloses on the features with respect to claims 1 and 8 as outlined above.
Stockmaster further teaches:
wherein a value of M and a value of Z are selected based on a level of interference detected in signals received from the antenna array (Stockmaster: [Column 2, lines 13-25] In some embodiments, the signal processing device can control the antenna array using a first value for M at a first time instance, and a second value for M at a second time instance. The signal processing device can set or receive a value for M in response to a level of interference detected in signals received via the antenna array. For instance, the value of M can be user controlled or automatically set in response to the type and level of interference detected in the signals. The system can further include an excision component configured to filter a result of the anti-jamming or interference mitigation processing, at a resolution matched to the N bins. The signal processing device can perform an inverse FFT (IFFT) with N bins …).
Conclusion
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/LIEM H. NGUYEN/Primary Examiner, Art Unit 2416
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