Patent Publication Number: US-11662455-B2

Title: Radar data processing using neural network classifier and confidence metrics

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. patent application No. 16/204,457, filed Nov. 29, 2018, which claims priority to U.S. Provisional Patent Application No. 62/689,446, filed Jun. 25, 2018, titled “Improvements to Object Detection in Radars,” both of which are hereby incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     In a Frequency Modulated Continuous Wave (FMCW) radar system, a sinusoid signal whose frequency increases linearly over time, also known as a “chirp”, is transmitted, and one or more objects around the FMCW radar system reflect the transmitted chirp. A sequence of equispaced chirps are transmitted in a unit called a frame. The FMCW radar system processes chirps and related reflections to analyze characteristics of the object. Exemplary characteristics include the range of an object relative to the radar origin, the velocity of an object relative to the radar original, and the angle of an object relative to a radar origin. 
     Each chirp and related reflections are signals with varying amplitude as a function of time. One example technique to analyze characteristics of the object(s) involves obtaining Fast Fourier Transform (FFT) output samples of a plurality of input signals, where each input signal includes a chirp and related reflections. By analyzing the FFT output samples, FMCW radar system have been used to identify presence of an object, range of an object relative to a radar origin, velocity of an object relative to a radar origin, and angle of an object relative to a radar origin. For angle identification, FFT output samples based at least in part on reflections received by different receiver antennas are analyzed. 
     SUMMARY 
     In accordance with at least one example of the disclosure, a radar data processing device comprises at least one analog-to-digital converter (ADC) configured to digitize a plurality of input signals, wherein each input signal includes radar chirp and radar chirp reflection information. The radar data processing device also comprises Fast Fourier Transform (FFT) logic configured to generate FFT output samples based on each digitized input signal, wherein the generated FFT output samples are associated with at least two of the plurality of receiver antennas. The radar data processing device also comprises a processor configured to determine a plurality of object parameters based on the generated FFT output samples, wherein the processor uses a neural network classifier trained to provide a confidence metric for at least one of the plurality of object parameters. 
     In accordance with at least one example of the disclosure, an integrated circuit comprises FFT logic configured to receive digitized input signals that include radar chirp and radar chirp reflection information received at a plurality of receiver antennas and to generate FFT output samples based on the digitized input signals, wherein the generated FFT output samples are associated with at least two of the plurality of receiver antennas. The integrated circuit also comprises a processor configured to determine a plurality of object parameters based on the generated FFT output samples, wherein the processor uses a neural network classifier trained to provide a confidence metric for at least one of the plurality of object parameters. 
     In accordance with at least one example of the disclosure, a method comprises digitizing a plurality of input signals, wherein each input signal includes radar chirp and radar chirp reflection information received at one of a plurality of receiver antennas. The method also comprises generating FFT output samples based on each digitized input signal, wherein the generated FFT output samples are associated with at least two of the plurality of receiver antennas. The method also comprises storing the generated FFT output samples. The method also comprises determining a plurality of object parameters based on the stored FFT output samples. The method also comprises providing a confidence metric for at least one of the plurality of object parameters based on at least some of the stored FFT output samples and a neural network classifier. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a detailed description of various examples, reference will now be made to the accompanying drawings in which: 
         FIG.  1    is a block diagram showing an example radar data processing technique; 
         FIG.  2    is a block diagram showing a radar system in accordance with various examples; 
         FIG.  3    is a block diagram showing part of a radar data processing system in accordance with various examples; 
         FIG.  4    is a block diagram showing other radar data processing operations in accordance with various examples; 
         FIG.  5    is a block diagram showing a radar data processing technique in accordance with various examples; 
         FIG.  6    is a block diagram showing another radar data processing technique in accordance with various examples; 
         FIG.  7 A  is a perspective view showing a radar data processing test scenario in accordance with various examples; 
         FIG.  7 B  is a graph showing results of the radar data processing test scenario of  FIG.  7 A  in accordance with various examples; 
         FIG.  7 C  is a table showing results of the radar data processing test scenario of  FIG.  7 A  in accordance with various examples; 
         FIG.  8    includes graphs showing Fast-Fourier Transform (FFT) output sample signal strength as a function election and azimuth in accordance with various examples; 
         FIG.  9    includes other graphs showing FFT output sample signal strength as a function election and azimuth in accordance with various examples; 
         FIG.  10    is a block diagram showing a radar data processing device in accordance with various examples; and 
         FIG.  11    is a flow chart showing a radar data processing method in accordance with various examples. 
     
    
    
     DETAILED DESCRIPTION 
     Disclosed herein are radar data processing devices, systems, and methods, where a neural network classifier is used to provide a confidence metric for at least one object parameter. Example object parameters include, but are not limited to, an object range relative to a radar origin, an object velocity relative to a radar origin, and an object angle relative to a radar origin. As used herein, a “confidence metric” refers to a probability value that varies between two thresholds. An example probability value range is 0.0-1.0 (0%-100%). As used herein, a “neural network classifier” refers to a function that has been trained to provide an output for new inputs based on one or more training inputs. Over time, the training for the function can be updated as additional training inputs become available. In different examples, confidence metrics provided by the neural network classifier are used for object detection, object tracking (e.g., tracking object position, velocity, and/or angle over time), and/or updating a constant false alarm rate (CFAR) detection threshold. 
     In some examples, a neural network classifier is configured to provide a confidence metric for an object parameter based on Fast-Fourier Transform (FFT) output samples obtained for each of a plurality of input signals, where each input signal include chip and chirp reflection information. Also, in some examples, the plurality of input signals are obtained from different antennas to support object angle (e.g., azimuth/elevation) analysis relative to the radar origin. 
       FIG.  1    shows a block diagram of an example radar data processing technique  100 . In the technique  100 , sets of FFT output samples  102 A- 102 N obtained from the input signals of different antennas are represented. The sets of FFT output samples  102 A- 102 N are combined by an accumulation process  110 , resulting in a set of accumulated FFT output samples  112 . The accumulation process is typically a non-coherent summation such as a summation of the absolute value of the corresponding bins of each the FFT output samples  102 A- 102 N. The accumulated set of FFT output samples  112  are analyzed using a constant false alarm rate (CFAR) detection process  120 . Thereafter, an angle estimation process  130  is performed for each detected object using the sets of FFT output samples  102 A- 102 N. With the technique  100 , detection and/or tracking of smaller objects and multiple objects is difficult. 
     By using a neural network classifier to provide confidence metric values as described herein, detection and/or tracking of smaller objects and/or multiple objects is improved compared to the radar data processing technique  100  described in  FIG.  1   . In some examples, the radar data processing device, system, and method options described herein are suitable for Frequency Modulated Continuous Wave (FMCW) radar data processing. To provide a better understanding, various radar data processing device, system, and method options involving a neural network classifier are described using the figures as follows. 
       FIG.  2    is a block diagram showing a radar system  200  in accordance with various examples. In the example of  FIG.  2   , the radar system  200  includes a synthesizer  201  configured to generate chirps, a TX antenna  202  for transmitting chirps generated by the synthesizer  201 , and a RX antenna  203  for receiving chirp reflections in response to transmitted chirps. The radar system  200  also includes a mixer  210  that provides input signals  213  to a low-pass filter  220 , where each input signal  213  includes information regarding a chirp  214  and any chirp reflections  216 . The graph  212  shows frequency relative to time to illustrate a chirp  214  and a chirp reflection  216  (i.e., the chirp  214  is a signal with increasing frequency as a function of time, and the chirp reflection  216  is a delayed version of the chirp  214 ). In some examples, the mixer  210  outputs a sinusoid wave with a frequency equal to the difference between the instantaneous frequency of the chirp  214  and the instantaneous frequency of the chirp reflection  216  (delayed by time τ) in graph  212 . In other words, the phase of the sinusoid wave generated by the mixer  210  is equal to the difference of the phase of the chirp  214  and the phase of the chirp reflection  216 . 
     Each of the input signals  213  is filtered by the low-pass filter  220  and is digitized by an analog-to-digital converter (ADC)  230 . The output of the ADC  230  is digitized input signals  233 , where each of the digitized input signals  233  includes chirp and chirp reflection information. Each of the digitized input signals  233  is provided to an FFT engine  240 , which provides FFT output samples for each of the digitized input signals  233 . In different examples, the component topology for the FFT engine  240  varies. Regardless of the particular component topology, the FFT engine  240  provides or stores FFT samples for use by a processor  250  to determine object parameters for one or more objects that cause chirp reflections  316 . Example object parameters include object position, object velocity, and object angle relative to a radar origin (the location of the radar system  200 ). 
     As represented in  FIG.  2   , the processor  250  also uses a neural network classifier  260  to determine confidence metrics. In some examples, the neural network classifier  260  is implemented using hardware of the processor  250 . In other examples, the neural network classifier  260  is implemented using software or instructions executed by the processor  250 . In either case, the neural network classifier  260  provides a confidence metric for at least one object parameter based on FFT output samples provided by the FFT engine  240 . In different examples, confidence metrics provided by the neural network classifier  260  are used for object detection, object tracking (e.g., tracking object position, velocity, and/or angle over time), and/or updating a CFAR detection threshold. 
     In some examples, the neural network classifier  260  is configured to provide a confidence metric for an object parameter based on FFT output samples obtained for each of a plurality of input signals, where each input signal include chip and chirp reflection information. Also, in some examples, the plurality of input signals are obtained from different antennas to support object angle (e.g., azimuth/elevation direction of arrival) analysis relative to the radar origin. In different examples, the confidence metric is based on at least one of: strength of signals received by at least two receiver antennas; a pattern of signals received by at least two receiver antennas; a distribution of FFT output samples as a function of range and Doppler; and a distribution of FFT output samples as a function of elevation and azimuth. 
     In some examples, the neural network classifier  260  comprises an artificial neural network (ANN) trained to provide a confidence metric for direction of arrival (angle) data obtained using the available FFT output samples. In such examples, the direction of arrival data is a function of azimuth and elevation. Also, in some examples, the neural network classifier  260  is trained to only analyze FFT output samples and to provide a confidence metric for an object parameter associated with a previously detected object. In other examples, the neural network classifier  260  is trained to analyze FFT output samples and to provide a confidence metric used for object detection. In some examples, the neural network classifier  260  is trained to analyze FFT output samples and to provide a confidence metric used for object tracking. In some examples, the processor  250  adjusts a threshold used for CFAR detection based on confidence metric results provided by the neural network classifier. 
       FIG.  3    is a block diagram showing part of a radar data processing system  300  in accordance with various examples. In the example of  FIG.  3   , the radar data processing system  300  includes the processor  250  introduced in  FIG.  2   . Also represented in  FIG.  3    is a graph  330  showing example radar data processing operations related to frame  332  and intra-frame time  334 . More specifically, radar data processing operations related to four chirps (C 1 -CN) of frame  332  are represented, where the radar data processing operations involve obtaining FFT output samples  320  by an FFT engine such as the FFT engine  240  in  FIG.  2   . More specifically, during frame  332 , FFT output samples  320  are organized into range bins indexed by chirp number. These FFT output samples  320  are stored (e.g., in a computer-readable memory device) for use by the processor  250 . 
     For example, the FFT output samples  320  are used to perform a range analysis  322 . Example operations for the range analysis  322  involve using at least some of the FFT output samples  320  for individual chirps to determine the range of one or more objects relative to a radar origin. Thereafter, during the intra-frame time  334 , the FFT output samples  320  are used to perform a Doppler analysis  324 . Example operations for the Doppler analysis  324  involve analyzing each range bin across chirps using the FFT output samples  320  to determine the velocity of one or more objects relative to a radar origin. More specifically, in some examples, the range analysis  322  and Doppler analysis  324  are performed by the processor  250 . 
     As previously discussed, the processor  250  includes a neural network classifier  260  configured to provide confidence metrics. In some examples, confidence metrics provided by the neural network classifier  260  is used for object detection that precedes or is part of the range analysis  322  and/or the Doppler analysis  324 . Additionally or alternatively, confidence metrics provided by the neural network classifier  260  applies to one or more range values obtained from the range analysis  322 . Additionally or alternatively, confidence metrics provided by the neural network classifier  260  applies to one or more velocity values obtained from the Doppler analysis  324 . 
       FIG.  4    is a block diagram showing other radar data processing operations  400  in accordance with various examples. In  FIG.  4   , a plurality of sets of FFT output samples  402 A- 402 N are obtained from respective receiver antennas. When sets of FFT output samples  402 A- 402 N corresponding to different receiver antennas are available, angle analysis  420  can be performed using at least some of the sets of FFT output samples  402 A- 402 N. Example operations for the angle analysis  420  involve using at least some FFT output samples from two or more sets of FFT output samples  402 A- 402 N to determine the angle (e.g., azimuth/elevation or direction of arrival) of one or more objects relative to a radar origin. In different examples, the angle analysis  420  is performed during a frame (e.g., frame  332 ) in which FFT output samples are generated and stored by an FFT engine and/or during an intra-frame time (e.g., the intra-frame time  334 ). Example operations for the angle analysis  420  involve analyzing particular bins (i.e., bins with the same range/Doppler index) across different sets of FFT output samples  402 A- 402 N to determine the angle of one or more objects relative to a radar origin. 
     As previously discussed, the processor  250  includes a neural network classifier  260  configured to provide a confidence metric. In some examples, the confidence metric provided by the neural network classifier  260  is used for object detection that precedes or is part of the angle analysis  420 . Additionally or alternatively, the confidence metric provided by the neural network classifier  260  applies to one or more angle values obtained from the angle analysis  420 . 
       FIG.  5    is a block diagram showing a radar data processing technique  500  in accordance with various examples. In the radar data processing technique  500  of  FIG.  5   , a plurality of sets of FFT output samples  502 A- 502 N are obtained from respective receiver antennas and an FFT engine (e.g., FFT engine  240 ). In some examples, the radar data processing technique  500  involves across antenna 2D-FFT operations  510  (e.g., performed by an FFT engine such as the FFT engine  240 , or performed by a processor such as the processor  250 ) that result in a set of across antenna FFT output samples  512  for different elevations and azimuths. The across antenna 2D-FFT operations  510  involve an FFT operation on corresponding bins (bins with the same range/Doppler index) across the Antennas  1  through N. The other operations represented in the radar data processing technique  500  are performed by a processor such as the processor  250 . For example, after the set of across antenna FFT output samples  512  are obtained, ANN classifier operations  520  are performed to determine confidence metrics associated with angle values (direction of arrival values) determined from the set of across antenna FFT output samples  512 . In some examples, the ANN classifier operations  520  are performed by a neural network classifier such as the neural network classifier  260 . 
     In some examples, the confidence metrics provided by the ANN classifier operations  520  are used to perform threshold adjustment operations  530 . More specifically, the threshold adjustment operations  530  may involve decreasing a CFAR threshold in response to at least one confidence metric from the ANN classifier operations  520  being greater than a threshold. When the CFAR threshold is decreased, object detection is more sensitive (the probability of detecting smaller and/or more objects is increased at the cost of increasing the probability of false object detection). On the other hand, when the CFAR threshold is increased, object detection is less sensitive (the probability of detecting smaller and/or more objects is decreased with the benefit of decreasing false object detection). In some examples, the threshold adjustment operations  530  given above, confidence metrics provided by the ANN classifier operations  520  are used to determine when to increase or decrease the CFAR threshold. Also, confidence metrics provided by the ANN classifier operations  520  can be used to determine the amount of increase or decrease in the CFAR threshold. 
     In some examples, the ANN classifier operations  520  identify presence of one or more objects by analyzing signal strength and/or coherency information provided by the across antenna FFT output samples  512 . For example, if the ANN classifier operations  520  determine that an object is present, a confidence metric indicating a probability of the object being present is output. More specifically, in some examples, the ANN classifier operations  520  involve determining the confidence metric based on a signal strength cleanliness analysis of the across antenna FFT output samples  512 . In one example, if the spectrum the across antenna FFT output samples  512  indicates that signal strength is scattered around a large number of elevation-azimuth bins, the ANN classifier operations  520  provide a confidence metric that indicates the probability of an object present in the corresponding range-Doppler bin is low. Conversely, if the spectrum of the across antenna FFT output samples  512  indicates that signal strength is concentrated around a limited number of elevation-azimuth bins, the ANN classifier operations  520  provide a confidence metric that indicates the probability of an object present in the corresponding range-Doppler bin is high. In this manner, object parameters determined by the radar data processing technique  500  of  FIG.  5    are not based solely on signal-to-noise ratio (SNR). 
     As part of the threshold adjustment operations  530  and/or after the threshold adjustment operations  530 , the set of FFT output samples  502 A- 502 N are used to determine object parameters such as range, velocity, and direction of arrival (DOA). As desired, the ANN classifier operations  520  involve analyzing available FFT output samples (e.g., the sets of FFT output samples  502 A- 502 N and/or the FFT output samples  512 ) to provide a confidence metric for one or more object parameters such as range, velocity, and DOA. In some examples, confidence metrics provided by the ANN classifier operations  520  are used for object tracking. 
       FIG.  6    is a block diagram showing another radar data processing technique  600  in accordance with various examples. In the radar data processing technique  600  of  FIG.  6   , a plurality of sets of FFT output samples  602 A- 602 N are obtained from respective receiver antennas and an FFT engine (e.g., FFT engine  240 ). In some examples, the radar data processing technique  600  involves accumulation operations  610  (e.g., performed by an FFT engine such as the FFT engine  240 , or performed by a processor such as the processor  250 ) that result in a set of accumulated FFT output samples  612  with accumulated bins values as a function of range and Doppler. The radar data processing technique  600  also includes CFAR detection operations  620  performed by a processor such as the processor  250 , where the CFAR detection operations  620  use the set of accumulated FFT output samples  612  provided by the accumulation operations  610  to detect the presence of objects. 
     If one or more objects are detected by the CFAR detection operations  620 , across antenna 2D-FFT operations  630  are performed (e.g., by an FFT engine such as the FFT engine  240 , or by a processor such as the processor  250 ), resulting in a set of across antenna FFT output samples  631  for different elevations and azimuths. Note that the operation  630  is repeated for the range-Doppler bin corresponding to each detected object. In some examples, the radar data processing technique  600  includes ANN classifier operations  640  that provide confidence metrics based on the set of across antenna FFT output samples  631 . 
     In some examples, the confidence metrics obtained from the ANN classifier operations  640  are used for threshold adjustment operations  650 . More specifically, the threshold adjustment operations  650  may involve decreasing a CFAR threshold in response to at least one confidence metric from the ANN classifier operations  640  being greater than a threshold. When the CFAR threshold is decreased, object detection is more sensitive (the probability of detecting smaller and/or more objects is increased at the cost of increasing the probability of false object detection). On the other hand, when the CFAR threshold is increased, object detection is less sensitive (the probability of detecting smaller and/or more objects is decreased with the benefit of decreasing false object detection). In the example threshold adjustment operations  650 , confidence metrics provided by the ANN classifier operations  640  are used for determining when to increase or decrease the CFAR threshold. Also, confidence metrics provided by the ANN classifier operations  640  can be used to determine the amount of increase or decrease in the CFAR threshold. 
     In some examples, the ANN classifier operations  640  provide a confidence metric based on a signal strength cleanliness analysis of the across antenna FFT output samples  631 . In one example, if the spectrum the across antenna FFT output samples  631  indicates that signal strength is scattered around a large number of azimuth-elevation bins, the ANN classifier operations  640  provide a confidence metric that indicates the probability of an object present at the corresponding range-Doppler bin is low. Conversely, if the spectrum of the across antenna FFT output samples  631  indicates that signal strength is concentrated around a limited number of azimuth-elevation bins, the ANN classifier operations  640  provide a confidence metric that indicates the probability of an object present at the corresponding range-Doppler bin is high. In this manner, object parameters determined by the radar data processing technique  600  of  FIG.  6    are not based solely on SNR. 
     As part of the threshold adjustment operations  650  and/or after the threshold adjustment operations  650 , the set of FFT output samples  602 A- 602 N are used to determine object parameters such as range, velocity, and direction of arrival (DOA). As desired, the ANN classifier operations  640  involve analyzing available FFT output samples (e.g., the sets of FFT output samples  602 A- 602 N, the accumulated FFT output samples  612  and/or the across antenna FFT output samples  631 ) to provide a confidence metric for one or more object parameters such as range, velocity, and DOA. In some examples, confidence metrics provided by the ANN classifier operations  640  are used for object tracking. 
       FIG.  7 A  is a perspective view showing a radar data processing test scenario  700  in accordance with various examples. In  FIG.  7 A , the object to be detected by radar system  720  is a plastic cone  710 , where x, y, and z coordinates are represented. In  FIG.  7 B , a graph  730  showing results of the radar data processing test scenario of  FIG.  7 A  is represented. In graph  730 , FFT output samples are represented by curve  741  according to range index and magnitude. Also represented in the graph  730  are the confidence metrics (the diamond icons in graph  730 ) computed for each range-bin. Note that in graph  730 , the y-axis represents the value of the confidence metric as a percentage (i.e., in the range 0-100 corresponding to a metric value between 0 and 1). Also note that for convenience, the curve  741  representing the FFT samples has been suitably scaled by a proportionality constant to fit in the same y-axis scale. The line  740  represents the value of the detection threshold (all range-bins with a confidence metric above this value are considered to be bins with an object present). As shown in graph  730 , bins with range indices  12 ,  13 ,  14 ,  16 ,  17 , and  20  are above line  740 , which indicates the cut-off probability of the radar system  720 . In other words, for the example of  FIG.  7 B , the radar system  720  ignores the bins with a confidence metric less than 80% (0.8), which is below the line  740 . The bins with a confidence metric lower than 80% include the bins with range indices  15  and  24 . The area  750  in the graph  730  indicates a leakage range (e.g., too close to the radar system  720 ). For the graph  730 , a concentration of FFT output samples above the line  740  and outside of the area  750  are used to provide a range value for the cone  710  relative to the radar system  720 . 
       FIG.  7 C  is a table  760  showing results of the radar data processing test scenario of  FIG.  7 A . The values in the table  760  are relative to the origin of the radar system  720 . More specifically, the radar system  720  is assumed to be 0.5 meters above ground, and the center of the plastic cone  710  is located at x, y coordinate (0.45, 0.3). The FFT output sample information for range index  12  informs the radar system  720  of an object present at x, y, z coordinate (0.35562, 0.20817, −0.23791). Similarly, the FFT output sample information of range index  13  informs of an object at x, y, z coordinate (0.37659, 0.22709, −0.27576). 
     Because the confidence metric of each of bins with range indices  12 ,  13 ,  14 ,  16 ,  17 , and  20  are above the cut-off line  740 , the radar system  720  recognizes that the object information from these bins are not false. The radar system  720  further ignores the FFT output sample information from bins with range indices  17  and  20  because the z coordinate associated with these bins indicates an object below ground level (the z coordinate values are greater than 0.5 meters below the level of the radar system  720 ). 
     Similarly, the radar system  720  recognizes that the FFT output sample information from bins with range indices  15  and  24  has a confidence metric lower than the line  740  and thus interpret these bins as false positives. As desired, the radar system  720  relies on the information available from multiple bins with different range indices, where each bin&#39;s probability is taken into account. In this manner, the radar system  720  acquires more holistic information for the plastic cone  710  reflecting a radar chirp. (The radar system  720  identifies multiple reflecting points (corresponding to range indices  12 ,  13 ,  14 , and  16 ) from the plastic cone  710 . It thus recognizes that the plastic cone  710  is an extended object presenting multiple reflections to the signals from the radar). In contrast, the radar data processing technique  100  discussed in  FIG.  1    would provide a single x, y, z coordinate (0.37686, 0.22725, −0.27595) for a plastic cone. 
       FIG.  8    includes graphs  802 ,  804 ,  806 , and  808  showing FFT output sample signal strength as a function of elevation and azimuth in accordance with various examples. More specifically, each of the graphs  802 ,  804 ,  806 , and  808  corresponds to range indexes from  FIGS.  7 A- 7 C  that have a high probability of object presence (e.g., range indexes  12 ,  13 ,  14 ,  16 ). In graphs  802 ,  804 ,  806 , and  808 , there is signal strength coherency for the range indexes represented as a function elevation and azimuth (a single peak is present). Thus, presence of an object at a range corresponding to these range indexes represented is interpreted by a radar system such as the radar system  720  as likely. 
       FIG.  9    includes graphs  902  and  904  showing FFT output sample signal strength as a function of elevation and azimuth in accordance with various examples. More specifically, each of the graphs  902  and  904  corresponds to range indexes (e.g., range indexes  15  and  24  in  FIGS.  7 A- 7 C ) with a low probability of object presence. As shown in graphs  902  and  904 , there is not signal strength coherency for the range indexes represented as a function elevation and azimuth (multiple peaks are present). Thus, presence of an object at a range corresponding to these range indexes represented is interpreted by a radar system such as the radar system  720  as unlikely. 
       FIG.  10    is a block diagram showing a radar data processor device  1000  in accordance with various examples. In different examples, the radar data processor device  1000  corresponds to an integrated circuit, a multi-die module, a printed circuit board (PCB) with components, and/or other radar data processing device options. As shown, the radar data processor device  1000  includes a synthesizer  201 A (an example of the synthesizer  201  in  FIG.  2   ), a transmitter antenna  202 A (an example of the transmitter antenna  202  in  FIG.  2   ), a plurality of receiver antennas  203 A- 203 N (examples of the receiver antenna  203  in  FIG.  2   ), and mixers  210 A- 210 N (examples of the mixer  210  in  FIG.  2   ). The output of the mixers  210 A- 210 N are used as input signals  213 A- 213 N (examples of the input signal  213 ) that include chirp and chirp reflection information. In some examples, the transmitter antenna  202 A and/or the receiver antennas  203 A- 203 N are separate from the other components represented for the radar data processor device  1000  (e.g., the transmitter antenna  202 A and/or the receiver antennas  203 A- 203 N are on-chip or off-chip in different examples). Also, it should be appreciated that the receiver antennas  203 A- 203 N are spaced from each other. Also, in some examples, the transmitter antenna  202 A is usable as one of the receiver antennas  203 A- 203 N and vice versa (with appropriate circuitry to connect/disconnect an antenna to other components as desired). At least one antenna is needed, with multiple antennas providing redundancy, angle information, and/other radar system options. 
     The radar data processor device  1000  also includes an ADC/filter  1002 A- 1002 N for each of the input signals  213 A- 213 N to filter and digitize the input signal  213 A- 213 N. As shown, the radar data processor device  1000  also includes an FFT engine  240 A (an example of the FFT engine  240  in  FIG.  2   ) configured to provide FFT output samples as described herein for each of the digitized input signals  233 A- 233 N. A processor  250 A (an example of the processor  250  in  FIG.  2   ) with a neural network classifier  260 A (an example of the neural network classifier  260  in  FIG.  2   ) provides confidence metrics based on FFT output samples provided by the FFT engine  240 A and/or the processor  250 A. In different examples, the confidence metrics provided by the neural network classifier  260 A are used for object detection, object tracking (e.g., tracking object position, velocity, and/or angle over time), and/or updating a CFAR detection threshold. 
     As shown, the radar data processor device  1000  also includes a host interface  1004  configured to receive object detection and tracking info from the processor  250 A. As desired, confidence metrics for each detected object and/or related object parameters is provided to the host interface  1004 . The host interface  1004  supports communications to or from other components so that results of the radar data processor device  1000  are displayed and/or are used by a radar system. Also, certain features of the radar data processing device  1000  are programmable via the host interface  1004 . 
       FIG.  11    is a flow chart showing a radar data processing method  1100  in accordance with various examples. The method  1100  is performed, for example, by the radar data processing device  1000  of  FIG.  10   . As shown, the method  1100  comprises digitizing a plurality of input signal, where each input signal includes radar chirp and radar chirp reflection information receiver by at least one of a plurality of receiver antennas at block  1102 . At block  1104 , FFT output samples are generated based on each digitized input signal. In some examples, at least some of the generated FFT output samples are across antennas FFT output samples associated with at least two of the plurality of receiver antennas. At block  1106 , the generated FFT output samples are stored. Example FFT output samples includes FFT output samples for respective receiver antennas (e.g., the set of FFT output samples  602 A- 602 N in  FIG.  6   ), accumulated FFT output samples (e.g., the set of FFT output samples  612  in  FIG.  6   ), and across antenna 2D-FFT output samples (e.g., the set of FFT output samples  631  in  FIG.  6   ). At block  1108 , a plurality of object parameters are determined based on at least some of the stored FFT output samples. At block  1110 , a confidence metric is provided for at least one of the object parameters based on at least some of the stored FFT output samples and a neural network classifier. In one example, providing the confidence metric at block  1110  comprises using an ANN trained to provide a confidence metric for direction of arrival data obtained using the stored FFT output samples, and wherein the direction of arrival data is a function of azimuth and elevation. In another example, providing the confidence metric at block  1110  comprises only analyzing FFT output samples and providing a confidence metric for an object parameter associated with a previously detected object. In different examples, the method  1100  uses the confidence metric provided at block  1110  for object detection, object tracking (e.g., tracking object position, velocity, and/or angle over time), and/or updating a CFAR detection threshold. 
     Certain terms have been used throughout this description and claims to refer to particular system components. As one skilled in the art will appreciate, different parties may refer to a component by different names. This document does not intend to distinguish between components that differ in name but not function. In this disclosure and claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . .” Also, the term “couple” or “couples” is intended to mean either an indirect or direct wired or wireless connection. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices and connections. The recitation “based on” is intended to mean “based at least in part on.” Therefore, if X is based on Y, X may be a function of Y and any number of other factors. 
     The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. For example, while the disclosed radar data processing options are described as being related to an FMCW radar system, use of neural network classifiers and confidence metrics with in other radar data processing systems is possible. It is intended that the following claims be interpreted to embrace all such variations and modifications.