DETAILED 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted, IDS - 02/20/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
Claim 20 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 05/19/2026.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that
form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or
in public use, on sale or otherwise available to the public before the effective
filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151,
or in an application for patent published or deemed published under section
122(b), in which the patent or application, as the case may be, names another
inventor and was effectively filed before the effective filing date of the claimed
invention.
Claims 1-3, 6, 7, 10, and 12-17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by McCARTHY et al. (US-20250175283-A1) hereinafter “McCARTHY”.
Regarding Claim 1,
McCARTHY discloses, ‘A system, comprising: at least one processor configured to: identify a first multipath signal component to be a first signal of interest, wherein a first cooperative signal includes multipath signal components including the first multipath signal component, wherein the first cooperative signal is indicative of multipath signal component information associated with the multipath signal components associated with the first cooperative signal, wherein each of the multipath signal components is received by at least one of at least two intercept receivers, wherein each of the multipath signal components corresponds to one signal of (a) an original signal emitted by a first emitter of at least two emitters, (b) a relayed signal, corresponding to a relay of the original signal or of a previously relayed signal, emitted by another emitter of the at least two emitters, or (c) a reflected signal of the original signal or said relayed signal, wherein each of said one signal is a signal of interest, wherein at least one of the multipath signal components is a given said relayed signal or a given said reflected signal, wherein each of the at least one processor is communicatively coupled to one or more of the at least two intercept receivers’ (In Fig. 4 implements automatic RF signal identification using geolocation-aided unique signal recognition [0014]. And, first signal processing path includes geolocation, deinterleaving and assignation [0070].
System and technique for RF recognition [0001]. Signal analysis engine is configured to determine grouping of RF wave or subset of emitters [0003]. And analyzer uses to train and generate an RF recognition model; To optimize emitter based RF recognition [0004]. Determine emitters and RF bursts that are emitted by one/more emitters. Determination for identifying or recognizing RF bursts [0008]. The analyzer performs previously detected/labeled RF-signals [0006] and processed RF-signals. Obtain a combination of RF-signals and previously processed RF-signals [0114]. Multiple RF signals detection signal analyzer [0064]. The RF signal analyzer uses the geolocation module to determine combined or clustered RF pulses to particular geo-locations [0051]. The RF signal analysis engine 205 to perform one or more deinterleaving functions on received sets of RF received bursts of detected RF signals. And, collection from singular/multiple receivers [0043].);
And discloses, ‘based at least on the first signal of interest, generate at least one data structure associated with the first signal of interest, the at least one data structure including information associated with the first signal of interest such that the information allows for an identification of other multipath signal components of the multipath signal components of the first cooperative signal’ ( Initial data set uses by one neural network of the RF machine learning prediction module in Fig. 2 to Fig. 4 [0081].)
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And discloses, ‘based at least on the at least one data structure, identify at least two other multipath signal components of the other multipath signal components within at least one signal history data structure as being correlated to the at least one data structure associated with the first signal of interest, the at least one signal history data structure containing information of received signals received by the at least two intercept receivers’ ( initial data set [Wingdings font/0xE0] leading to further enhanced data set [Wingdings font/0xE0] used by RFML-prediction module [Wingdings font/0xE0] generate increasingly accurate versions of the RF signal recognition data model [0056].
And discloses, ‘analyze the at least two other multipath signal components to determine particular multipath signal component information associated with each of the at least two other multipath signal components’ (RF prediction module determined includes a RF Signal analyzer RFML-enhanced clustering used for more precise emitter geolocation. Initial data set [Wingdings font/0xE0] enhanced data set [Wingdings font/0xE0] generate increasing accurate versions [0056] and in Fig. 2 illustrates RF signal analyzer includes geolocation module.);
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And discloses, ‘and output at least one relatively high likelihood geolocation solution of at least one emitter of the at least two emitters.’ (signal recognition output in Fig. 2 and Fig. 3. Performed unique signal recognition of emitter-based RF energies base on a iterative processing [0061]. RFML-enhanced clusters to produce precise emitter iterative train and generate accurate versions of the RF signals [0056].)
Regarding Claim 2,
‘The system of Claim 1’ (disclosed above),
And discloses, ‘wherein the at least one processor is further configured to perform at least one iteration of:
for each of at least one given subset of subsets, based at least on a given subset of the at least one given subset of the multipath signal components and the particular multipath signal component information associated with each multipath signal component of the given subset of the multipath signal components, perform a given trial geolocation analysis to (a) determine a given geolocation solution associated with the given subset and (b) determine a given likelihood associated with the given geolocation solution, the given likelihood associated with the given subset’ (RF prediction module to Iteratively train to generate accurate versions of the RF signals recognition model [0056]. Determine a group-RF-signals that identifies subset of RF signal bursts for which an associated geolocation resolves to identify the emitter, disclosure Claim 6. In Fig. 6 illustrates perform geolocation solution. Obtain the set of RF-signals includes combination of RF signals from RF sensors and previously processed RF signals. RF-recognition model to optimize by iteratively processing and combine at signal analysis engine [0004]. Determine the geospatial likelihood [0071]. And RFML prediction model uses set of segmented burst [0082]. );
‘among a given solution group comprising all of the given geolocation solutions of a current iteration of the at least one iteration, determine whether there is at least one given relatively high likelihood solution among the given solution group, wherein each of the at least one given relatively high likelihood solution has a given relatively high likelihood’ ([0082]);
‘and upon a given determination that there is the at least one given relatively high likelihood solution, output the at least one given relatively high likelihood solution;
wherein the at least one relatively high likelihood geolocation solution includes the at least one given relatively high likelihood solution of each of the at least one iteration.’(In Fig. 6 perform geolocation computation. The determinations for identifying or recognizing which RF bursts belong to high-priority emitters may be based on probability or likelihood estimations [0008].)
Regarding Claim 3,
‘The system of Claim 2’ (disclosed above),
And discloses, ‘wherein the at least one processor is further configured to reduce an amount of multipath signal component information used for at least one performance of at least one given trial geolocation analysis of the at least one iteration by using signal component metadata associated with at least one individual component of the multipath signal components.’ (
With multipath assisted positioning, a new approach has emerged, that exploits the information in multipath components (MPCs) by treating them as line-of-sight signals from virtual transmitters. While the locations of the physi cal and virtual transmitters are generally unknown, they can be estimated with simultaneous localization and mapping (SLAM). One SLAM based multipath assisted positioning algorithm is Channel-SLAM, where the user location is estimated simultaneously with creating a map of physical and virtual transmitters, page-1.
a snapshot based ML estimator is applied to estimate the parameters of signal components. Three types: Multiple signal classification, para metric subspace based estimators, Expectation-Maximization (EM) algorithm similar SAGE algorithm. EM algorithm uses the separation the signal and noise subspace based on an eigen decomposition section 3.4.2 page-41. )
Regarding Claim 6,
‘The system of claim 2’ (disclosed above),
And discloses, ‘wherein each iteration of one or more iterations of the at least one iteration further comprises the at least one processor being configured to: upon the given determination that there is the at least one given relatively high likelihood solution, associate each of the at least one given relatively high likelihood solution's given subset of the multipath signal components as being emitted from a given common emitter of the at least two emitters’ (In Fig. 6 perform geolocation solution);
and upon the given determination that there is the at least one given relatively high likelihood solution’; (Disclosure variational encoder decision making subnetworks includes convolution NN classifier, autoencoder, trained USR objective function and derive output [0096] and in Fig. 2 and Fig. 3. )
And discloses, ‘and for each of the at least one given relatively high likelihood solution's given subset of the multipath signal components, restrict at least one member multipath signal component of the given subset of the at least two other multipath signal components from being used for any subsequent given trial geolocation analysis of any subsequent iteration of the at least one iteration.’ (RF signal mode optimized to perform USR of emitter based RF energies, disclosure Claim 11.)
Regarding Claim 7,
‘The system of Claim 6’ (disclosed above),
And discloses, ‘wherein, for performance of the at least one iteration, at least one of each of the given common emitter is the same as or different from the first emitter.’ (System 100 includes a different number of sensing devices/pairwise emissions. Configured to multiple pairwise emitters can be evaluated [0030]. )
Regarding Claim 10,
‘The system of Claim 1’ (disclosed above),
And discloses, ‘wherein the at least one data structure is a correlative template and/or wherein the correlative template is derived from the at least one data structure.’ (Disclosure includes labeled initial dataset and enhance data set in RF analyze engine and to RF prediction module and recognition data module in Fig. 2 and Fig. 3.)
Regarding Claim 12,
‘The system of Claim 1’ (disclosed above),
And discloses, ‘further comprising a first intercept receiver of the at least two intercept receivers, the first intercept receiver comprising at least one antenna and one or more of the at least one processor.’ (In Fig. 1 includes multiple sensing devices includes one/more RF receivers/sensors [0029].)
Regarding Claim 13,
‘The system of Claim 12’ (disclosed above),
And discloses, ‘further comprising a second intercept receiver of the at least two intercept receivers, the second intercept receiver communicatively coupled to the first intercept receiver, the second intercept receiver comprising at least one other antenna and one or more other processors of the at least one processor.’ (Sensing devices 102 operates as a interceptor any suitable platform as a spacecraft, aerial/terrestrial vehicle/ship/UAVs/drone also operate as a mobile apparatus in Fig. 1 [0028] communication link to the receiver station includes h/w processors [0063]. In Fig. 1 includes multiple sensing devices includes one/more RF receivers/sensors [0029] communication link [0036-0037] to the receiver station 120 implements the geo-location system includes RF signal analyzer engine in Fig. 2, RFML prediction module accessed/included by one/more system as the receiver station [0063].
Regarding Claim 14,
‘The system of Claim 13’ (disclosed above),
And discloses, ‘further comprising at least one computing device communicatively coupled to the at least two intercept receivers, the at least one computing device comprising at least one other processor of the at least one processor.’ (RF signal analyzer, RFML prediction module and RF signal recognition data mode accessed or included in one/more computer systems of system 100 in Fig. 1. Included in the receiver station h/w processors, h/w accelerators or special purpose NN [0063]. Fig. 5 includes the computing device [0099] to implement the system illustrates in Fig. 1.)
Regarding Claim 15,
‘The system of Claim 1’ (disclosed above),
And discloses, ‘further comprising at least one computing device communicatively coupled to the at least two intercept receivers, the at least one computing device comprising one or more of the at least one processor.’ (Sensing devices 102 operates as a interceptor any suitable platform as a spacecraft, aerial/terrestrial vehicle/ship/UAVs/drone also operate as a mobile apparatus in Fig. 1 [0028]. A communication link established between the sensing device and the receiver station [0036-0037]. Fig. 9 and Fig. 3 illustrates three sensing device in space/ terrestrial network alternatively aerial platforms operated in the communication link [0040]. )
Regarding Claim 16,
‘The system of Claim 1’ (disclosed above),
And discloses, ‘wherein the multipath signal component information for each of the multipath signal components includes information of at least one of: a time of arrival, an angle of arrival, a frequency, a phase, a polarization, a received signal strength (RSSI/RSRP) measured by one of the at least two intercept receivers, a velocity, or at least one potential error magnitude with respect to at least one of the time of arrival, the angle of arrival, the frequency, the phase, the polarization, the received signal strength, or the velocity.’ (Disclosure ToA, frequency arrival transmitted by emitter analyzed RF signal to locate emitter and geolocation for estimation/measurement [0021, 0077]. And configured to estimate velocity at ToA [0077]. Complex valued sampled represent in-phase for geolocation aided-unique search location [0083]. )
Regarding Claim 17,
‘The system of Claim 1’ (disclosed above),
And discloses, ‘further comprising a communication network configured to communicatively couple at least the at least two intercept receivers, wherein the communication network is at least one of a wired network, an optical network, or a wireless network.’ (Fig. 1 illustrates system computing architectures implements geo-location-aided unique signal recognition includes emitters, sensors and receiver station [0027] uses wireless communication network [0031] and communication link wireless/optical [0037]. )
Regarding Claim 19,
‘The system of Claim 1’ (disclosed above),
And discloses, ‘wherein each given trial geolocation analysis is performed at least by using at least one of:
(a) at least one time difference of arrival, wherein each time difference of arrival is based at least on a difference between two times of arrival for two of the multipath signal components for when each of the two of the multipath signal components were received by a given of the at least two intercept receivers’ (RF geo-location analyzer increase the precision in TDOA measurements to identify/locate RF signal [0074-0075].);
And discloses, ‘(b) at least one frequency difference of arrival (FDOA), wherein each FDOA is based at least on a difference between two frequencies of arrival for the two of the multipath signal components for when each of the two of the multipath signal components were received by the given of the at least two intercept receivers ‘ (RF geo-location analyzer includes FDOA measurement and combine TDOA/FDOA [0074-0075].);
or (c) at least one direction of arrival, wherein each direction of arrival is based at least on a direction of arrival of a given of the multipath signal components received at one of the at least two intercept receivers.’
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 (i.e., changing from AIA to pre-AIA ) 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
he claimed invention pertains. Patentability shall not be negated by the manner
in which the invention was made.
The factual inquiries for establishing a background for determining obviousness
under 35 U.S.C. 103 are summarized as follows:
• Determining the scope and contents of the prior art.
• Ascertaining the differences between the prior art and the claims at issue.
• Resolving the level of ordinary skill in the pertinent art.
• Considering objective evidence present in the application indicating
• obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the
claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any
evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to
point out the inventor and effective filing dates of each claim that was not commonly
owned as of the effective filing date of the later invention in order for the examiner to
consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2)
prior art against the later invention.
Claims 4, 5, 8, 9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over McCARTHY et al. in view of Markus et al. “Cooperative multipath assisted positioning. Diss. Hamburg University of Technology, 2021. (Year: 2021) hereinafter “Markus”.
Regarding Claim 4,
‘The system of Claim 3’ (disclosed above),
And discloses, ‘wherein the signal component metadata includes information of at least one of:’
And didn’t disclose, ‘at least one emitter fingerprint of each of one or more of the at least one individual component, an identification of each of the one or more of the at least one individual component as having one of a direct path or a reflected multipath, or a line of bearing of each of the one or more of the at least one individual component.’
Markus in relevant art discloses localization and estimation for wireless localization approach grouped into two types. First RF-signals uses fingerprint technique and then estimates location, page-10. In Fig. 1.1 In multipath assisted positioning, the MPC arriving at the user after being reflected at the wall is interpreted as a LoS signal from the virtual transmitter vTx, page-3. Different propagation path reflected and scattered multipath signal component, Fig. 2.4 page-15.
The LOS path and reflections and/or scattering are estimated. Fig. 3-4 illustrates the estimation of shortest propagation distance vs actual distance. Calculates channel impulse responses and creates multipath scenario page-43 to 44.
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Therefore, a person in the ordinary skill in the art before the effective filing date of
the claim invention would have recognized that the disclosure of McCARTHY and to include with that of Markus to come up with the claim invention,
McCARTHY motive to perform the geolocation analysis uses geolocation analyzer engine geolocation-aided unique signal recognition model as illustrated in Fig. 2 to Fig. 3 and process flow for the identification of RF signal illustrated in Fig. 4 [0014] and signal processing path [0070]. And, Geolocation-aided deinterleaving combines precise ToA and FoA information for received bursts [0071]. Markus complements the RF signal estimation and localization specifically uses multipath positioning LoS and the MPCs. This would enhance the capability geolocation analyzer engine of McCARTHY to derive solution in different propagation scenarios as illustrated by Markus.
Regarding Claim 5,
‘The system of Claim 4’ (disclosed above),
And discloses, ‘wherein the metadata’
And didn’t disclose, ‘further includes information of at least the line of bearing of each of the one or more of the at least one individual component, wherein the at least one processor is further configured to use the information of the line of bearing of each of the one or more of the at least one individual component to weight said component as having one of the direct path or the reflected multipath, wherein each of the one or more of the at least one individual component being weighted as having said direct path is given priority over each of the one or more of the at least one individual component being weighted as having said reflected multipath.’
Markus in the relevant discloses, localization and estimation techniques includes angle of arrival, phase and signal strength, page-11. In Figure 2.2, angle of arrival measurements, a wave front arrives at an antenna array of three elements with element spacing da under an angle θ, page-13. In Figure 2.3, page-14, the signals from two network nodes Tx1 and Tx2 arrive at the user under angles θ and θ, respectively, relative to the user orientation u. The user is located at the intersection of the corresponding lines. Disclosure include multipath assisted positioning [Wingdings font/0xE0] simultaneous location and mapping (Channel-SLAM) [Wingdings font/0xE0] algorithm, Chapter 3. The Multipath propagation model in different scenarios, direct/LoS, reflected/scattered MPC in Fig. 2.4, page-15. And, in Fig. 2.7 page-18 direct path blocked and indirect two different propagation. And, non-LoS propagation includes the AoA of the first MPC received by the user is different from the blocked LoS component. Several technique to address NLoS propagation page-18.
Figure 3.11 illustrates the estimates of the transmitter locations of transmitters and user depicted. Both ToA and AoA measurements are considered. The channel-SLAM-algorithm includes the weights and normalized weights, page-56.
RF signal analyzer determine covariance estimates for each measurements uses weight of each measurement Motive would be identical disclosed above in Claim 4.
Regarding Claim 8,
‘The system of Claim 6’ (disclosed above),
And discloses, ‘wherein each given trial geolocation analysis of the at least one iteration is performed at least by using an emitter radiofrequency (RF)’,
And didn’t disclose, ‘fingerprint approach to associate one or more of the multipath signal components exhibiting a given common emitter RF fingerprint with said given common emitter of a given iteration of the at least one iteration of the at least two emitters,
for which said given trial geolocation analysis is undertaken for said given common emitter of the at least two emitters.’
Markus in the relevant discloses, different pairwise emissions can be compared between versions of delays and sensor instances for computing distance metrics and evaluating candidate locations [0030]. The system 100 may combine multiple targeted emitters within a single decision-making subnetwork 330 by expanding the number of classification bins within the subnetwork [0096]. )
And didn’t disclose, ‘to reduce an amount of the multipath signal component information’
Markus in the relevant art discloses, The Kalman filters track not only the means, but also the covariance matrices of the signal components’ parameters, page-44. In Figure 3.6: In KEST, the signal components’ parameters are estimated with Kalman Filters (KFs) in parallel for different numbers of signal components, page-46.
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Regarding Claim 9,
‘The system of Claim 2’ (disclosed above),
And discloses, ‘wherein the at least one iteration comprises multiple iterations, wherein for a collection of at least some of the multiple iterations, one or more of the given geolocation solutions of the at least some of the multiple iterations are refined by the at least one processor to reduce a solution space of the one or more of the given geolocation solutions by weighting some of the multipath signal components’ (The RF-signal analyze engine to determine set of weightings to weight the importance of each measurement individually [0077].
And didn’t disclose, ‘as having a reflected multipath so as to restrict said some of the multipath signal components from further geolocation analysis.’
Markus in the relevant art discloses, uses filter technique and the signal components to be discarded are chosen such that the residual when subtracting the reconstructed signal with the remaining signal components from the received signal is the lowest. And based on time instant in Fig. 3.7.
Motive would identical to Claim 4 disclosed above. In addition, McCARTHY motive to operate the RF signal analyzer engine to tune input series and apply the normalization preprocessing [0085]. Uses variable autoencoder ML-building block reconstruct the objective function [0086]. Therefore enhance the normalization and preprocessing.
Regarding Claim 11,
‘The system of Claim 10’ (disclosed above),
Markus discloses, ‘wherein use of the correlative template to identify the at least two other multipath signal components reduces a threshold signal to noise ratio (SNR) for detection of the at least two other multipath signal components as compared to detection of the at least two other multipath signal components without use of the correlative template.’ (A snapshot of the KEST algorithm at a traveled distance of 4m along the reference track in Fig. 7.1 is illustrated in Fig. 7.3, page-124. The KEST estimates regarding the propagation distances and amplitudes. The amplitudes are normalized to the maximum amplitude of the received signal. Snapshot-Based Channel Parameter Estimation. Kalman filter in KEST. a snapshot based ML estimator is applied to estimate the parameters of signal components. Few categories of estimation and one of which multiple signal classification, page-41
Motive would be identical to Claim 4 and Claim 9. McCARTHY motive to optimize the RF recognition model to perform accurate signal recognition of RF signals [0004]. noise reduction/adjustment [0072]. Disclosure includes assignation module generate/apply one/more assignation label based on results from deinterleaving module, geolocation module or both. The assignation labels can indicate an assignment of RF signals to a corresponding emitter. In some implementations, the assignation model relies on information from outside of the RF signal analysis engine 225 to provide time and identity labels for emitter positions and uses automated information system dataset. And, for these implementations, temporal and spatial proximity between positions in the external data and geolocations produced by the geolocation module 220 allow for propagating the labels to the individual RF bursts. In some implementations, the RF signal analysis engine 225 creates an identifier for a cluster of bursts defined by the deinterleaving module 215 or for a geolocation produced by the geolocation module 220 and all of the bursts used to calculate that geolocation [0052]. Detection of SNR could improve the preprocessing and normalization of geolocation-aided data processing McCARTHY [0084] and inclusion/exclusion of clustered and reduces error [0056]. Optimization of preprocessing, normalization and to reduce error could be complemented in multipath scenario where radio wave propagation likely to be complexed when propagation have single/double reflections, and scattering page-43 as depicted Markus, fig. 3-1, page-36 and prone to error and therefore need to reduce/identify the SNR.
Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over McCARTHY et al. in view of Mark-HSU et al (US-9201132-B2) hereinafter “Mark-HSU”.
Regarding Claim 18,
‘The system of Claim 1’ (disclosed above),
And didn’t disclose, ‘wherein each of the at least one cooperative signal has a low probability of exploitation waveform.’
Mark-HSU in the relevant art discloses, to locate a hidden source by observing its emissions, a geolocation procedure may first use triangulation to determine the position of the multipath scatterers reflecting the energy, and then use the identified scatterers as a secondary virtual antenna array to geolocate the hidden source. This may require N+1 discrete scatterers, where N is the number of spatial dimensions to which we are constrained, to enable both the direction and the distance of the source to be determined uniquely. Low probability of intercept (LPI) communication techniques, such as DIRECT SEQUENCE SPREAD SPECTRUM AND FREQUENCY HOPPING, operate at instantaneous or average power levels that may be lower than ambient noise power levels. Such communication techniques present difficult scenarios for geolocation of non-cooperative transmitters. Low Probability of Intercept techniques may not communicate reliably in Non-Line-of-Sight (NLoS) multipath environments without incorporating Multiple-Input-Multiple-Output (MIMO) technologies. Geolocating signal transmitters in NLoS environments is more difficult, but possible with multipath scattering, Col. 2 [0030-0037].
Therefore, a person in the ordinary skill in the art before the effective filing date of
the claim invention would have recognized that the disclosure of McCARTHY and to include with that of Mark-HSU to come up with the claim invention,
McCARTHY motive to generate accurate version of RF signal recognition model iteratively train the RF prediction model. Uses “RFML-enhanced clusters” can be used to produce more precise emitter geolocations leading to better assignments in the enhanced labelled dataset [0056]. Inclusion of low probability interceptor emitter dataset would complement the capability and most importantly motive of RF prediction model to trin and lead towards accurate version of RF signal recognition model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure:
Eyal et al. (US9841489B2) “Mitigation of multipath distortions for TDOA-based geolocation”, disclosure claim 1, A method for determining a location of a radio frequency (RF) transmitter in the presence of multipath interference, the method comprising the procedures of: providing a plurality of spatially separated RF receivers at known locations in a multipath environment in the vicinity of said transmitter;
detecting an active RF signal associated with said transmitter received by at least one of said receivers, and directing said receivers to acquire measurements of the detected RF signal; calculating time difference of arrival (TDOA) measurements between pairs of said receivers based on the obtained measurements; averaging the TDOA measurements for each of said pairs of receivers to provide a respective updated TDOA measurement value, without requiring a direct determination of the multipath component of the RF signal;
Armando et al. (US 20230124883 A1) “SYSTEM, METHOD, AND APPARATUS FOR PROVIDING DYNAMIC, PRIORITIZED SPECTRUM MANAGEMENT AND UTILIZATION” geolocation -algorithm ; ANN/DL/NLP/stat learning tech
(US 11474189 B1) Cluster Track Identification, multiple emitter [Wingdings font/0xE0] geolocation algorithm -> SBI/LBI [Wingdings font/0xE0] identify multiple emitters
Norman et al. US-20150241545-A1 A geolocation system to identify a location of a emitter [0005]. The EM wave received by a UAV in Fig. 2 [0015]. Signals transmitted by a emitting device include multi-path signal 30 received by UAV and then reflected/relayed by UAV in Fig. 1 and Fig. 2. Disclosure, the emitter location processor is part of ground or the airborne station processing in Fig. 3 [0018].In Fig. 3 receiver and the frequency estimator to perform the geolocation processing in Fig. 4; Estimation of geolocation processing technique [0017] and Fig. 4.
Scott et al. (US20240393421A1) “Geolocation method for detection and tracking of maneuvering emitters”. In Fig. 5, emitter trajectory and each pair of collectors in Fig. 2 and [0040]. In Fig. 2 includes RF-data, geolocation architecture and data fusion element. And, RF snapshot table in Fig. 4. Emitter object RF-snapshot data table created; a list of RF collectors in use that detected the RF signals of the emitter [0038].
Jefferson et al. (US20200191529A1) “Systems, Methods and Computer-Readable Media for Improving Platform Guidance or Navigation Using Uniquely Coded Signals”. systems and methods for determining the position and relative motion (if any) of a non-cooperative object and the position and relative motion of a cooperative platform while guiding or navigating the cooperative platform relative to the non-cooperative object [0002].
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Info-disclosure-US8878725B2
L. Li and J. L. Krolik, "Simultaneous Target and Multipath Positioning via multi-hypothesis single-cluster PHD filtering," 2013 Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2013, pp. 461-465. The tracking a RF source in dense multipath environments using a uniform linear receiver array (ULA) where multipath propagation is modeled as specular reflections from planar reflectors. A single cluster for simultaneous Target and Multipath Positioning.
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interview, applicant is encouraged to use the USPTO Automated Interview Request
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to Syed Ahmed whose telephone number is (703)-756-5308. The examiner can normally be reached from Monday-Friday 9am-6pm. The examiner can also be reached on alternate If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faruk Hamza can be reached on (571) 272-7969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service
Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.A./Examiner, Art Unit 2466
/CHRISTOPHER M CRUTCHFIELD/Primary Examiner, Art Unit 2466
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