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 .
Response to Amendment
The Amendment filed 08/29/2025 has been entered. Claims 1-20 are pending in the application. Claims 17-18 are amended.
Response to Arguments
Applicant argues that Rydstrom does not teach “for one or more respective radar data points of the plurality of radar data points, calculating, at the radar sensor, a data item indicative of a measurement accuracy corresponding to the respective radar data point”, however the examiner respectfully disagrees.
The measurement noise covariance matrix R in Rydstrom is a function of radar measurement (Para 0079: “According to some other aspects, the processing circuitry 910 is arranged to determine a transmitted energy as function of number of assigned subcarriers and time slots, and wherein the communications interface 920 is configured to report an estimated target detection range associated with the transmission resource assignment. This target detection range allows the target tracking function to selectively update its maintained tracks. For instance, with reference to Figure 4, suppose that the target tracking function is tracking some targets 450 close to the vehicle 100, and some other targets 410, 430 further away from the vehicle. Then, if the transmitted energy is too low for detecting the targets far away, perhaps indicated by the radar equation, then the target tracking function may skip the updating of such tracks and only update tracks corresponding to targets located close by. It is noted that the Kalman filtering equations can be used to automatically provide this type of selective track update function. This is because the covariance Rk of the measurement noise vk can be modelled as dependent on received energy, perhaps determined from the radar equation. Thus, as target range becomes large in comparison to transmitted energy, the Kalman gain will reflect this and not put any significant weight on the measurement”). Furthermore, Rydstrom also teaches that measurement noise is related to Radar measurement accuracy (Para 0043: “Generally, the detectability of an electromagnetic waveform which is received at a receiver depends on its energy in comparison to the receiver noise (often thermal noise) and distortion present in the receiver circuit. In communication systems this quantity is often denoted Eb/N0, where Eb is the energy per information bit and N0 is the power spectral density of the receiver noise. For a radar waveform, the actual waveform itself carries the information, since it is desired to estimate its delay (which is proportional to the distance between the receiver and the target), and its phase (which will vary over time as function of target radial velocity) relative to what was transmitted. The higher the received signal energy in comparison to the receiver noise, the more accurate the estimation of delay and phase will be.”). Taken in combination, recitations in paragraphs 0079 and 0043 show that the method disclosed by Rydstrom includes a step of calculating data item quantifying the accuracy of the Radar data measurement, and this data item is a noise covariance matrix.
For the reason articulated in the paragraph above rejection of Claim 1 under 35 U.S.C. 102(a)(1) is maintained.
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 (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 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.
Claim(s) 1,2,13-14 and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rydstrom (EP3943965A1).
Regarding claim 1 Rydstrom discloses: A method comprising: producing, at a radar sensor, a plurality of radar data points based on radar signal reflections received at the radar sensor (Detailed Description: “A radar transmission 114 from the radar transceiver 110 is reflected or scattered by a target 115, often referred to as radar signal backscatter, which is then detected by the radar transceiver 110. The radar transceiver 110 is connected to a central control unit 120 which may control at least some operations of the radar transceiver 110 and receive target data from the radar transceiver. This control may comprise transmission timing, transmission frequency content, as well as the actual transmitted time waveform.”); for one or more respective radar data points of the plurality of radar data points , calculating, at the radar sensor, a data item indicative of a measurement accuracy corresponding to the respective radar data point (Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined asPk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”); and transmitting, by the radar sensor, the plurality of radar data points and the data item for each respective radar data point to a central radar processor for object tracking. (Detailed Description: “According to some other aspects, the processing circuitry 910 is arranged to determine a transmitted energy as function of number of assigned subcarriers and time slots, and wherein the communications interface 920 is configured to report an estimated target detection range associated with the transmission resource assignment. This target detection range allows the target tracking function to selectively update its maintained tracks. For instance, with reference to Figure 4, suppose that the target tracking function is tracking some targets 450 close to the vehicle 100, and some other targets 410, 430 further away from the vehicle. Then, if the transmitted energy is too low for detecting the targets far away, perhaps indicated by the radar equation, then the target tracking function may skip the updating of such tracks and only update tracks corresponding to targets located close by.”)
Regarding claim 2 Rydstrom discloses all the limitations of claim 1. Rydstrom further teaches: wherein the data item is a covariance matrix (Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined asPk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”).
Regarding claim 3 Rydstrom discloses all the limitations of claim 2. Rydstrom further teaches:: calculating, at the radar sensor, the covariance matrix for each respective radar data point utilizing one or more radar parameters as inputs (Detailed Description:” According to some other aspects, the processing circuitry 910 is arranged to determine a transmitted energy as function of number of assigned subcarriers and time slots, and wherein the communications interface 920 is configured to report an estimated target detection range associated with the transmission resource assignment. This target detection range allows the target tracking function to selectively update its maintained tracks. For instance, with reference to Figure 4, suppose that the target tracking function is tracking some targets 450 close to the vehicle 100, and some other targets 410, 430 further away from the vehicle. Then, if the transmitted energy is too low for detecting the targets far away, perhaps indicated by the radar equation, then the target tracking function may skip the updating of such tracks and only update tracks corresponding to targets located close by. It is noted that the Kalman filtering equations can be used to automatically provide this type of selective track update function. This is because the covariance R.sub.k of the measurement noise v.sub.k can be modelled as dependent on received energy, perhaps determined from the radar equation. Thus, as target range becomes large in comparison to transmitted energy, the Kalman gain will reflect this and not put any significant weight on the measurement. A similar effect can be observed for a particle filter implementation, where the weighting will be achieved by the derived particle gain.”).
Regarding claim 4 Rydstrom discloses all the limitations of claim 3. Rydstrom further teaches: wherein the one or more radar parameters comprises one or more of: configuration information of a plurality of antennas coupled to the radar sensor, a radar acquisition bandwidth, a radar sequence chirp time, a radar sequence chirp frequency, a number of chirps per frame in a radar sequence, or a signal-to-noise ratio (SNR) of each respective radar data point in the plurality of radar data points (Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined asPk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”).
Regarding claim 5 Rydstrom discloses all the limitations of claim 2. Rydstrom further teaches: wherein the covariance matrix comprises a plurality uncertainty in measurement values corresponding to each of a plurality of radar sensor outputs for each respective radar data point (Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined asPk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”).
Regarding claim 6 Rydstrom discloses all the limitations of claim 5. Rydstrom further teaches: wherein the plurality of radar sensor outputs comprises one or more of a range estimate, a velocity estimate, or an angle estimate (Detailed Description: “The object is also obtained by a control unit for automotive applications, arranged to track one or more targets based on radar detections. The control unit is arranged to obtain the detections from a radar transceiver in frames having frame durations and to obtain respective range and/or radial velocity resolutions determined in dependence of a transmission resource assignment of the radar transceiver in each frame. The control unit is arranged to track the one or more targets by weighting the radar detections relative to a-priori track data in dependence of the range and/or radial velocity resolution. Thus, there is disclosed herein a control unit configured to make use of the variable resolution radar data from the radar transceiver discussed above in a robust manner by weighting radar data in dependence of resolution during tracking.”).
Regarding claim 13 Rydstrom discloses: A radar sensor comprising: a plurality of antennas to receive radar signals reflected off of one or more objects (Description: “The present disclosure relates to radar systems for automotive applications. There are disclosed methods and devices for operating a radar transceiver based on assigned subcarriers and time slots in an orthogonal frequency division multiplexed (OFDM) radar system. There are also disclosed radar systems comprising adaptive antenna arrays for mitigating radar self-interference which are particularly suitable for use in OFDM-based radar systems.”); a processor coupled to the plurality of antennas (Figure 5), the processor to: produce a plurality of radar data points based on the received radar signals; for one or more respective radar data points of the plurality of radar data points, calculate a data item indicative of a measurement accuracy for the respective radar data point (Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined asPk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”); and generate a signal for transmission, the signal comprising the plurality of radar data points and the data item for each respective radar data point (Detailed Description: “According to some other aspects, the processing circuitry 910 is arranged to determine a transmitted energy as function of number of assigned subcarriers and time slots, and wherein the communications interface 920 is configured to report an estimated target detection range associated with the transmission resource assignment. This target detection range allows the target tracking function to selectively update its maintained tracks. For instance, with reference to Figure 4, suppose that the target tracking function is tracking some targets 450 close to the vehicle 100, and some other targets 410, 430 further away from the vehicle. Then, if the transmitted energy is too low for detecting the targets far away, perhaps indicated by the radar equation, then the target tracking function may skip the updating of such tracks and only update tracks corresponding to targets located close by.”).
Regarding claim 14 Rydstrom discloses all the limitations of claim 13. Rydstrom further teaches: wherein the data item is a covariance matrix, and the processor calculates the covariance matrix for each respective radar data point utilizing a plurality of radar parameters as inputs(Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined as Pk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”).
Regarding claim 18 Rydstrom discloses: A plurality of radar sensors (Para 0014: “According to aspects, the radar transceiver comprises a transmit antenna array and/or a receive antenna array. The transmit antenna array and/or the receive antenna array is configured to reduce power of the transmitted radar signal from the transmit antenna array towards the receive antenna array while maintaining transmitted power in a main lobe direction.”), each radar sensor comprising: a plurality of antennas to receive radar signals reflected off of one or more objects (Description: “The present disclosure relates to radar systems for automotive applications. There are disclosed methods and devices for operating a radar transceiver based on assigned subcarriers and time slots in an orthogonal frequency division multiplexed (OFDM) radar system. There are also disclosed radar systems comprising adaptive antenna arrays for mitigating radar self-interference which are particularly suitable for use in OFDM-based radar systems.”) ; and a processor, coupled to the plurality of antennas, to: produce a plurality of radar data points based on the received radar signals ; for one or more respective radar data points of the plurality of radar data points, calculate a data item indicative of a measurement accuracy for the respective radar data point (Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined asPk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”); and generate a signal for transmission, the signal comprising the plurality of radar data points and the data item for each respective radar data point (Detailed Description: “According to some other aspects, the processing circuitry 910 is arranged to determine a transmitted energy as function of number of assigned subcarriers and time slots, and wherein the communications interface 920 is configured to report an estimated target detection range associated with the transmission resource assignment. This target detection range allows the target tracking function to selectively update its maintained tracks. For instance, with reference to Figure 4, suppose that the target tracking function is tracking some targets 450 close to the vehicle 100, and some other targets 410, 430 further away from the vehicle. Then, if the transmitted energy is too low for detecting the targets far away, perhaps indicated by the radar equation, then the target tracking function may skip the updating of such tracks and only update tracks corresponding to targets located close by.”); and a central radar processor coupled to the plurality of radar sensors and configured to receive the signal from each of the plurality of radar sensors, the central radar processor configured to track the one or more objects based on the plurality of radar data points and the data item for each respective radar data point from at least some of the plurality of radar sensors (Detailed Description: “The processing circuitry 910 is furthermore arranged to detect a target 115, 330, 340, 350 from backscatter of the transmitted radar signal, and to determine a range resolution and/or a radial velocity resolution for the target based on the transmission resource assignment. The resolution in range is determined mainly from the assigned bandwidth in a frame, i.e., the number of subcarriers that are available for transmission. This bandwidth may be used as input to a function which determines range resolution. For instance, a variation in bandwidth over a frame where a large number of subcarriers are initially assigned followed by an assignment of a reduced number of subcarriers may result in an effective bandwidth somewhere in-between the maximum and minimum bandwidths occupied during the frame duration. The resolution in radial velocity is, as discussed above, mainly determined from dwell-time. Given the assigned resources in a frame, a coherent integration time can be determined from which radial velocity resolution can be determined.”).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7-11 and 19-20 are rejected under 35 U.S.C 103 as being unpatentable over Rydstrom (EP3943965A1) in view of Wheeler (GB2564085A).
Regarding claim 7 Rydstrom discloses all the limitations of claim 1. Rydstrom does not teach “wherein the data item is a signal-to-noise ratio (SNR) for each respective radar data point”.
However, Wheeler in the analogous arts teaches: wherein the data item is a signal-to-noise ratio (SNR) for each respective radar data point (Description: “In one aspect of the system, at the end of the Azimuth/Elevation processing cycle we have a stored list of measurements, their SNR, and interpolated Range bin, interpolated Doppler bin, interpolated Azimuth bin and interpolated Elevation bin. These measurements are applied to a transform unit (413) module which converts from interpolated bin indices to real-world units in single precision floating point format, so Range bin becomes meters, Doppler bin becomes meters per second, Azimuth and Elevation bins become radians. Furthermore the SNR is converted to standard deviation for each respective real-world measurement by reference to the Cramer-Rao lower bound for the estimation method. Furthermore the conversion to real-world coordinates takes account of the mode in operation; PR, SRR, MRR or LRR so that an object detected in more than one mode is correctly mapped to the same natural real-world coordinates allowing the subsequent processing to take advantage of fusing measurements from different operational modes.”).
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Rydstrom with Wheeler to incorporate the feature of: wherein the data item is a signal-to-noise ratio (SNR) for each respective radar data point. Rydstrom and Wheeler are all considered analogous arts as they all disclose the use of radar technology to detect and track objects. However, Rydstrom fails to disclose a feature of using SNR values in data analysis. This feature is disclosed by Wheeler. It would have been obvious to someone in the art prior to the effective filling date of the claimed invention to modify Rydstrom with Wheeler to incorporate the feature of: wherein the data item is a signal-to-noise ratio (SNR) for each respective radar data point as such a feature would make the system more robust to noise, thereby increase its object tracking efficiency.
Regarding claim 8 the combination of Rydstrom and Wheeler discloses all the limitations of claim 7. Wheeler further teaches: further comprising providing one or more radar sensor parameters to the central radar processor (Description: “The present disclosure is of methods and apparatus for radar digital signal processing that operates on one or more TDM streams to perform fast chirp frequency and spatial analysis, detection, object association and tracking for a system that may be configured for operation modes PR, SRR, MRR and LRR.”).
The reason to combine Rydstrom with Wheeler is the same as one given in claim 7 above.
Regarding claim 9 the combination of Rydstrom and Wheeler discloses all the limitations of claim 8. Wheeler further teaches: wherein the one or more radar sensor parameters comprises one or more of: configuration information of a plurality of antennas coupled to the radar sensor, a radar acquisition bandwidth, a radar sequence chirp time, a radar sequence chirp frequency, or a number of chirps per frame in a radar sequence (Description: “The present disclosure is of methods and apparatus for radar digital signal processing that operates on one or more TDM streams to perform fast chirp frequency and spatial analysis, detection, object association and tracking for a system that may be configured for operation modes PR, SRR, MRR and LRR. In one aspect of the system the digital signal processing may be configured to operate on one or more TDM streams originating from a system configured for cycling or alternating between operation modes, one example of which is shown in Figure 3 where the first measurement cycle is from SRR and the next is from LRR. The reader will recognise that this example does not preclude alternation or cycling within other operation modes. In one aspect of the system the digital signal processing may be configured, according to a programmed operation, to operate on one or more TDM streams originating from any one of these antenna configurations (a) a co-located long aperture antenna array providing only Azimuth spatial angle (b) a co-located but partitioned three dimensional antenna array where some of the antennas provide Azimuth spatial angle and the remaining provide Elevation angle (c) a distributed set of antenna sub-arrays “).
The reason to combine Rydstrom with Wheeler is the same as one given in claim 7 above.
Regarding claim 10 the combination of Rydstrom and Wheeler discloses all the limitations of claim 8. Wheeler further teaches: wherein the central radar processor receives the SNR and the one or more radar sensor parameters to calculate a covariance matrix for object tracking (Description: “In one aspect of the system, at the end of the Azimuth/Elevation processing cycle we have a stored list of measurements, their SNR, and interpolated Range bin, interpolated Doppler bin, interpolated Azimuth bin and interpolated Elevation bin. These measurement are applied to a transform unit (413) module which converts from interpolated bin indices to real-world units in single precision floating point format, so Range bin becomes meters, Doppler bin becomes meters per second, Azimuth and Elevation bins become radians. Furthermore the SNR is converted to standard deviation for each respective real-world measurement by reference to the Cramer-Rao lower bound for the estimation method. Furthermore the conversion to real-world coordinates takes account of the mode in operation; PR, SRR, MRR or LRR so that an object detected in more than one mode is correctly mapped to the same natural real-world coordinates allowing the subsequent processing to take advantage of fusing measurements from different operational modes. The real world
04 18 measurements plus the associated standard deviation of those measurements are further transformed from a polar/spherical coordinate system to a Cartesian coordinate system. In so doing it derives the Cartesian measurement vector and the Cartesian measurement error covariance matrix, which are both stored in memory. The polar/spherical coordinates and their diagonal measurement error covariance matrix are useful for operating on by an Extended Kalman Filter, whereas the Cartesian measurement vector and associated measurement error covariance matrix are useful for operating on by a Linear Kalman Filter.
“).
The reason to combine Rydstrom with Wheeler is the same as one given in claim 7 above.
Claim 20 recites limitations that are similar to those of claim 10, therefore claim 20 is rejected under the same rationale.
Regarding claim 11 Rydstrom discloses all the limitations of claim 1. Rydstrom does not teach “further comprising utilizing, by the central radar processor, a covariance matrix for each radar data point of the plurality of radar data points for tracking the one or more objects”.
However, Wheeler in the analogous arts teaches: further comprising utilizing, by the central radar processor, a covariance matrix for each radar data point of the plurality of radar data points for tracking the one or more objects (Description: “In one aspect of the system it has a track association unit (414). This reads in each active track state prediction that the Kalman Filter is maintaining and calculates a cost function relating how close the measurement is to the track prediction, whilst taking the measurement error covariance matrix and track state prediction error covariance matrix into consideration to ensure all distances are normalised facilitating a direct comparison. Only those distances less than a programmed threshold are retained and the distance is written to a cost function matrix, indexed by track and measurement. The next step in the measurement association unit, once distances are calculated from all combinations of track and measurement pairs, is to perform a neighbour assignment algorithm for example Nearest Neighbour or Global Nearest Neighbour to find a possible association of tracks and measurements in some optimum sense.”).
The reason to combine Rydstrom with Wheeler is the same as one given in claim 7 above.
Regarding claim 19 Rydstrom discloses all the limitations of claim 18. Rydstrom does not teach “wherein the data item is a signal-to-noise ratio (SNR) for each respective radar data point, and the central radar processor is configured to receive one or more radar sensor parameters from the radar sensor processor”.
However, Wheeler in the analogous arts teaches: wherein the data item is a signal-to-noise ratio (SNR) for each respective radar data point (Description: “In one aspect of the system, at the end of the Azimuth/Elevation processing cycle we have a stored list of measurements, their SNR, and interpolated Range bin, interpolated Doppler bin, interpolated Azimuth bin and interpolated Elevation bin. These measurements are applied to a transform unit (413) module which converts from interpolated bin indices to real-world units in single precision floating point format, so Range bin becomes meters, Doppler bin becomes meters per second, Azimuth and Elevation bins become radians. Furthermore the SNR is converted to standard deviation for each respective real-world measurement by reference to the Cramer-Rao lower bound for the estimation method. Furthermore the conversion to real-world coordinates takes account of the mode in operation; PR, SRR, MRR or LRR so that an object detected in more than one mode is correctly mapped to the same natural real-world coordinates allowing the subsequent processing to take advantage of fusing measurements from different operational modes.”).
, and the central radar processor is configured to receive one or more radar sensor parameters from the radar sensor processor (Description: “The present disclosure is of methods and apparatus for radar digital signal processing that operates on one or more TDM streams to perform fast chirp frequency and spatial analysis, detection, object association and tracking for a system that may be configured for operation modes PR, SRR, MRR and LRR.”).
The reason to combine Rydstrom with Wheeler is the same as one given in claim 7 above.
Claims 12, 15-17 are rejected under 35 U.S.C 103 as being unpatentable over Rydstrom (EP3943965A1) in view of Wheeler (GB2564085A) and further in view of Li (CN103383261)
Regarding claim 12 the combination of Rydstrom and Wheeler discloses all the limitations of claim 11. Rydstrom does not teach “further comprising applying, by the central radar processor, a scaling factor to the covariance matrix for tracking the one or more objects, wherein the scaling factor reduces an effect of the covariance matrix used in a Kalman filter implemented by the central radar processor”.
However, Li in the analogous arts teaches: further comprising applying, by the central radar processor, a scaling factor to the covariance matrix for tracking the one or more objects (Abstract: “The invention claims an improved type lossless Kalman filtering moving target locating method for indoor positioning using traditional non-destructive Kalman filtering method for locating precision is not high, and the problem that the real-time property is bad. The invention claims a method for scaling factor dynamic adjustment iteration algorithm of noise covariance matrix to improve traditional Kalman filtering, that is, in the filtering process, continuously using the filtered value of last time into the time update equation, building up the prior current state estimation, and then calculating out estimated current time state variables and the error covariance.”), wherein the scaling factor reduces an effect of the covariance matrix used in a Kalman filter implemented by the central radar processor (Para [0022]: “Beneficial effect: The invention of improved type lossless Kalman filtering method through configured according to system noise and observation noise self-adaptive adjustment of scale factor when the system is in a motor state, the scale factor value is reduced; when the system has rapid maneuver, the scaling factor can be rapidly increased, so as to increase the noise covariance and observation noise covariance of the system to quickly real-time adjust system Kalman gain value, improve the filtering performance. through simple and flexible noise ratio adjustment, which can realize the online adjustment and estimation of the noise, so as to ensure the fast convergence of detection data, and to improve the traditional lossless real-time tracking performance of the Kalman filter, it can better realize real-time track of the irregular movement target than the traditional method.”).
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Rydstrom with Li to incorporate the feature of: further comprising applying, by the central radar processor, a scaling factor to the covariance matrix for tracking the one or more objects, wherein the scaling factor reduces an effect of the covariance matrix used in a Kalman filter implemented by the central radar processor. Rydstrom and Li are all considered analogous arts as they all disclose the use of radar technology to detect and track objects. However, Rydstrom fails to disclose a feature of a scaled covariance matrix. This feature is disclosed by Li. It would have been obvious to someone in the art prior to the effective filling date of the claimed invention to Rydstrom with Li to incorporate the feature of: further comprising applying, by the central radar processor, a scaling factor to the covariance matrix for tracking the one or more objects, wherein the scaling factor reduces an effect of the covariance matrix used in a Kalman filter implemented by the central radar processor as such a feature would increase lossless Kalman filter efficiency.
Regarding claim 15 the combination of Rydstrom, Wheeler and Li discloses all the limitations of claim 12. Wheeler further teaches: wherein the plurality of radar parameters comprises a signal-to-noise ratio (SNR) of each respective radar data point and one or more of (Description: “In one aspect of the system, at the end of the Azimuth/Elevation processing cycle we have a stored list of measurements, their SNR, and interpolated Range bin, interpolated Doppler bin, interpolated Azimuth bin and interpolated Elevation bin. These measurement are applied to a transform unit (413) module which converts from interpolated bin indices to real-world units in single precision floating point format, so Range bin becomes meters, Doppler bin becomes meters per second, Azimuth and Elevation bins become radians. Furthermore the SNR is converted to standard deviation for each respective real-world measurement by reference to the Cramer-Rao lower bound for the estimation method. Furthermore the conversion to real-world coordinates takes account of the mode in operation; PR, SRR, MRR or LRR so that an object detected in more than one mode is correctly mapped to the same natural real-world coordinates allowing the subsequent processing to take advantage of fusing measurements from different operational modes.”): configuration information of a plurality of antennas coupled to the radar sensor, a radar acquisition bandwidth, a radar sequence chirp time, a radar sequence chirp frequency (Description: “The present disclosure is of methods and apparatus for radar digital signal processing that operates on one or more TDM streams to perform fast chirp frequency and spatial analysis, detection, object association and tracking for a system that may be configured for operation modes PR, SRR, MRR and LRR. In one aspect of the system the digital signal processing may be configured to operate on one or more TDM streams originating from a system configured for cycling or alternating between operation modes, one example of which is shown in Figure 3 where the first measurement cycle is from SRR and the next is from LRR. The reader will recognise that this example does not preclude alternation or cycling within other operation modes. In one aspect of the system the digital signal processing may be configured, according to a programmed operation, to operate on one or more TDM streams originating from any one of these antenna configurations (a) a co-located long aperture antenna array providing only Azimuth spatial angle (b) a co-located but partitioned three dimensional antenna array where some of the antennas provide Azimuth spatial angle and the remaining provide Elevation angle (c) a distributed set of antenna sub-arrays “), or a number of chirps per frame in a radar sequence.
The reason for combining Rydstrom with Li is the same as one given in claim 7 above.
Regarding claim 16 the combination of Rydstrom and Wheeler discloses all the limitations of claim 15. Rydstrom further teaches: wherein the covariance matrix comprises a plurality uncertainty in measurement values for each of a plurality of radar sensor outputs corresponding to each respective radar data point (Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined asPk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”)., wherein the plurality of radar sensor outputs comprises a range estimate, a velocity estimate (Detailed Description: “The object is also obtained by a control unit for automotive applications, arranged to track one or more targets based on radar detections. The control unit is arranged to obtain the detections from a radar transceiver in frames having frame durations and to obtain respective range and/or radial velocity resolutions determined in dependence of a transmission resource assignment of the radar transceiver in each frame. The control unit is arranged to track the one or more targets by weighting the radar detections relative to a-priori track data in dependence of the range and/or radial velocity resolution. Thus, there is disclosed herein a control unit configured to make use of the variable resolution radar data from the radar transceiver discussed above in a robust manner by weighting radar data in dependence of resolution during tracking.”, or an angle estimate.
Regarding claim 17. A radar system comprising the radar sensor of claim 14, and further comprising: a central radar processor communicatively coupled to a plurality of radar sensors (Para 0014: “According to aspects, the radar transceiver comprises a transmit antenna array and/or a receive antenna array. The transmit antenna array and/or the receive antenna array is configured to reduce power of the transmitted radar signal from the transmit antenna array towards the receive antenna array while maintaining transmitted power in a main lobe direction.”), the central radar processor configured to: receive, from each of the plurality of radar sensors (Para 0015: “The object is also obtained by a control unit for automotive applications, arranged to track one or more targets based on radar detections. The control unit is arranged to obtain the detections from a radar transceiver in frames having frame durations and to obtain respective range and/or radial velocity resolutions determined in dependence of a transmission resource assignment of the radar transceiver in each frame. “), the plurality of radar data points and the covariance matrix for each respective radar data point(Detailed Description: “The covariance matrix estimate P.sub.k|k in a Kalman filter algorithm at sample k given radar data input up to time k is determined asPk|k=I−KkHkPk|k−1I−KkHkT+KkRkKkT,[AltContent: rect]where P.sub.k|k-1 = F.sub.kP.sub.k-1|k-1F.sub.k.sup.T + Q.sub.k, and where P.sub.k|k-1 is the covariance matrix estimate at time k given data up to time k-1, I is the identity matrix, K.sub.k is the Kalman gain at time step k, H.sub.k is an observation matrix at time step k, R.sub.k is an assumed covariance matrix of the measurement noise, i.e., describing the uncertainty in the input radar signals, F.sub.k is a state transition matrix at time step k, and Q.sub.k is a process noise at time step k.”) ; and track the one or more objects utilizing the plurality of radar data points (Para 0015: “The object is also obtained by a control unit for automotive applications, arranged to track one or more targets based on radar detections. The control unit is arranged to obtain the detections from a radar transceiver in frames having frame durations and to obtain respective range and/or radial velocity resolutions determined in dependence of a transmission resource assignment of the radar transceiver in each frame. “), the covariance matrix for each respective radar data point, and a scaling factor (Abstract: “The invention claims an improved type lossless Kalman filtering moving target locating method for indoor positioning using traditional non-destructive Kalman filtering method for locating precision is not high, and the problem that the real-time property is bad. The invention claims a method for scaling factor dynamic adjustment iteration algorithm of noise covariance matrix to improve traditional Kalman filtering, that is, in the filtering process, continuously using the filtered value of last time into the time update equation, building up the prior current state estimation, and then calculating out estimated current time state variables and the error covariance.”) , wherein the scaling factor reduces an effect of the covariance matrix used in a Kalman filter implemented by the central radar processor (Para [0022]: “Beneficial effect: The invention of improved type lossless Kalman filtering method through configured according to system noise and observation noise self-adaptive adjustment of scale factor when the system is in a motor state, the scale factor value is reduced; when the system has rapid maneuver, the scaling factor can be rapidly increased, so as to increase the noise covariance and observation noise covariance of the system to quickly real-time adjust system Kalman gain value, improve the filtering performance. through simple and flexible noise ratio adjustment, which can realize the online adjustment and estimation of the noise, so as to ensure the fast convergence of detection data, and to improve the traditional lossless real-time tracking performance of the Kalman filter, it can better realize real-time track of the irregular movement target than the traditional method.”).
The reason for combining Rydstrom with Li is the same as one given in claim 12 above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/BONGANI JABULANI MASHELE/Examiner, Art Unit 3645
/ROBERT W HODGE/Supervisory Patent Examiner, Art Unit 3645
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