Method and system for object detection in a radar system

Abstract

The invention relates to a method and system for object detection in a radar system. According to an embodiment, a method of operating a radar system includes estimating a total noise level of a received radar signal; determining a first noise threshold based on the estimated total noise level; generating a radar map having a plurality of intervals from the radar signal; determining a first subset of the plurality of intervals having amplitudes exceeding a first noise threshold; determining a constant false alarm rate (CFAR) threshold for only each interval of the first subset of the plurality of intervals; and determining that the interval of the first subset having the amplitude exceeding the CFAR threshold corresponds to the one or more detected objects.

Description

Technical Field

[0001] The present invention relates generally to systems and methods for electronic systems and, in particular embodiments, to systems and methods for determining a constant false alarm rate (CFAR) in a radar system. Background Art

[0002] Radar systems are widely used to detect and track objects by transmitting radio waves and analyzing the reflected signals. Due to the rapid development of low-cost semiconductor technologies (such as silicon germanium (SiGe) and fine-geometry complementary metal oxide semiconductor (CMOS) processes), applications in millimeter wave frequency systems have gained great attention in the past few years. The availability of high-speed bipolar and metal oxide semiconductor (MOS) transistors has led to a growing demand for integrated circuits for millimeter wave applications at, for example, 24 GHz, 60 GHz, 77 GHz, and 80 GHz, as well as exceeding 100 GHz. Such applications include, for example, automotive radar systems, gesture recognition systems, surveillance systems, and multi-gigabit communication systems.

[0003] The demand for low-power radar systems is also increasing. This is driven by a variety of factors, including the proliferation of battery-powered devices, a growing emphasis on energy efficiency, and the rise of remote sensing applications with limited power resources. Low-power radar systems offer several advantages, such as longer battery life, reduced heat generation, and the ability to operate in energy-constrained environments. However, achieving lower power consumption without compromising detection performance is a challenging task, requiring innovative solutions and advanced radar signal processing techniques. Summary of the Invention

[0004] According to an embodiment, a method of operating a radar system includes: estimating a total noise level of a received radar signal; determining a first noise threshold based on the estimated total noise level; generating a radar map having a plurality of bins from the radar signal; determining a first subset of the plurality of bins whose magnitude exceeds the first noise threshold; determining a constant false alarm rate (CFAR) threshold only for each bin of the first subset of the plurality of bins; and determining that the first subset of bins whose magnitude exceeds the CFAR threshold correspond to one or more detected objects.

[0005] According to another embodiment, a method of operating a millimeter-wave radar system includes: during a reference setting mode: when no moving target is present within a detection range of the millimeter-wave radar system, receiving a first radar signal from a millimeter-wave radar sensor, estimating a total noise level of the received first radar signal, and determining a first noise threshold based on the estimated total noise level; and during a detection mode different from the reference setting mode: receiving a second radar signal from the millimeter-wave radar sensor, generating a range-Doppler radar map from the received second radar signal, comparing amplitudes of intervals of the range-Doppler radar map with a first noise threshold to produce a first subset of intervals whose amplitudes exceed the first noise threshold, wherein a second subset of the intervals, different from the first subset, has an amplitude that does not exceed the first noise threshold, applying a constant false alarm rate (CFAR) detection algorithm to the first subset of intervals but not to the second subset of intervals, and detecting a target based on the applied CFAR detection algorithm.

[0006] According to another embodiment, a system includes a millimeter-wave radar sensor and a processor coupled to the millimeter-wave radar sensor. The processor is configured to: during a reference setting mode, estimate a total noise level of a first radar signal received from the millimeter-wave radar sensor and determine a first noise threshold based on the estimated total noise level; and during a detection mode different from the reference setting mode, generate a range-Doppler radar map based on a second radar signal received from the millimeter-wave radar sensor, determine a first subset of intervals whose amplitudes exceed the first noise threshold, wherein a second subset of intervals different from the first subset has an amplitude that does not exceed the first noise threshold, apply a constant false alarm rate (CFAR) detection algorithm to the first subset of intervals but not to the second subset of intervals, and detect a target based on the applied CFAR detection algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] For a more complete understanding of the present invention and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which:

[0008] Figure 1 illustrates a radar system according to an embodiment;

[0009] Figure 2 It is a detailed description Figure 1 A block diagram of an example radar processing block of an embodiment of the present invention;

[0010] Figure 3 The diagram illustrates a radar application scenario according to an embodiment;

[0011] Figure 4A and Figure 4B shows a graph illustrating the technical performance of the CFAR algorithm of an exemplary embodiment;

[0012] Figure 5Aillustrates an example implementation of an embodiment radar system; and Figure 5B illustrates a chirp sequence according to an embodiment;

[0013] Figure 6 a block diagram illustrating an embodiment method; and

[0014] Figure 7 A processing system that can be used to implement the embodiment algorithms and processing steps is illustrated.

[0015] Unless otherwise indicated, corresponding numerals and symbols in the different figures generally refer to corresponding parts. The figures are drawn to clearly illustrate the relevant aspects of the preferred embodiments and are not necessarily drawn to scale. To more clearly illustrate certain embodiments, letters indicating variations of the same structure, material, or process step may follow the figure number. DETAILED DESCRIPTION

[0016] The following describes in detail the making and using of the presently preferred embodiments. However, it should be understood that the present invention provides many applicable inventive concepts that can be implemented in a wide variety of specific circumstances. The specific embodiments discussed are merely illustrative of specific ways to make and use the invention and do not limit the scope of the invention.

[0017] One signal detection technique used in radar systems is the Constant False Alarm Rate (CFAR) algorithm. CFAR is an adaptive threshold-based signal detection technique used to detect targets whose signal strength is sufficient to exceed a defined noise threshold. CFAR technology is particularly useful in environments with varying noise levels because it adjusts the detection threshold based on the surrounding noise level to maintain a constant false alarm rate.

[0018] However, CFAR techniques can be computationally intensive, especially as the size of the input signal increases, because they typically involve processing a large number of cells or bins in the radar map, each of which represents a potential target. This can result in increased computation time and power consumption, which can be a problem in applications where power efficiency and fast processing are critical, such as battery-powered devices.

[0019] In embodiments of the present invention, CFAR detection is performed by comparing radar map bins to a noise threshold based on the overall noise level of the received radar signal, and then determining a CFAR threshold only for bins that exceed the noise threshold. This advantageously reduces the number of radar map bins on which CFAR is performed, which can reduce the computational load and power consumption of the radar system. In various embodiments, the noise threshold is estimated based on frame data collected from the radar sensor when no target is present.

[0020] Figure 1A radar system 100 is shown, comprising a radar sensor 102 coupled to a processing system 104. In various embodiments, the radar sensor 102 is configured to transmit a radar signal and receive a signal reflected from a detected object. The time it takes for the signal to return and the change in frequency of the returning signal provide information about the object's range and velocity. This data is used to generate raw radar data, which is then processed to produce a radar map (such as a range-Doppler or range-angle intensity map). This map is then used to determine the presence of a target.

[0021] The radar sensor can be implemented using a variety of radar technologies and techniques. For example, the radar sensor 102 can be implemented using a frequency modulated continuous wave (FMCW) millimeter wave radar sensor, described in more detail below. Alternatively, the radar sensor 102 can be implemented using pulse radar technology. Pulse radar operates by sending short, intense pulses of radio waves and then listening for the echoes of these pulses. The time delay between the transmission of the pulse and the reception of the echo provides information about the distance to the object, while the frequency shift of the echo provides information about the object's velocity.

[0022] Processing system 104 is responsible for processing the raw radar signals received from radar sensor 102 and is configured to perform a series of operations to convert the raw radar data into a form that can be used for target detection. As shown, processing system 104 includes a pre-processing block 106, which performs various RF signal processing tasks to generate a radar map (such as a range-Doppler or range-angle intensity map). In some embodiments, the radar map can be a range-Doppler radar map or a portion of a range-angle map that represents the range angles within the range-Doppler map. There are various methods for generating a radar map from radar signals. Other types of radar maps (such as range-angle or Doppler-angle maps) can be used instead of generating a range-Doppler map. Processing system 104 can be located in radar sensor 102 and / or integrated with radar sensor 102, can be implemented external to radar sensor 102, or can be implemented both internally and externally. In some embodiments, the processing system can be a distributed system external to radar sensor 102. Such a distributed system can include one or more processors and one or more memories.

[0023] According to some embodiments, the object presence detector 120 includes a map intensity comparison block 112 , a multiplier 114 , a CFAR threshold estimation block 116 , and a threshold comparison block 118 .

[0024] The map strength comparison block 112 compares the strength of each bin in the radar map with a noise threshold established by a multiplier 114. For example, the radar map may be a range-Doppler map having bins representing radar signal strength according to range and Doppler value, or a range-angle map having bins representing radar signal strength according to range and angle. The threshold C established by the multiplier 114 is T The threshold value α, which can be considered as a noise threshold or a rough threshold, is used by the map strength comparison block 112 to determine whether the signal strength of a particular interval is above a predetermined level α above a reference noise level N. Thus, the map strength comparison block 112 is used to predefine (i.e., select) a particular interval in the radar map having sufficient signal strength before the CFAR threshold estimation is performed by the CFAR threshold estimation block 116 .

[0025] As shown in the figure, the noise threshold C is determined by multiplying the estimated noise level N of the radar sensor by a constant or offset α T In some embodiments, the predetermined offset α may represent a value between 5 dB and 10 dB. Alternatively, values outside this range may be used, depending on the specific embodiment and its implementation.

[0026] The CFAR threshold estimation block 116 is configured to generate a threshold for the radar image to exceed the noise threshold C T The CFAR threshold is determined based on the interval of the radar data. The CFAR threshold can be an adaptive threshold estimated based on the statistics of the surrounding interval. This is achieved using an adaptive algorithm that can be implemented by the processing system 104. Taking into account the variability of noise in the radar data, the CFAR threshold estimation block 116 provides a more accurate threshold for target detection.

[0027] In one embodiment, the CFAR threshold estimation block 116 is configured to: TThe CFAR threshold T is established by taking the average amplitude of a defined set of adjacent intervals. The CFAR threshold estimation block 116 may also implement CFAR threshold determination techniques known in the art, such as maximum CFAR (GO-CFAR), minimum CFAR (SO-CFAR), or ordered statistical CFAR (OS-CFAR), depending on the specific requirements of the radar system 100. These variations of the CFAR algorithm may provide different trade-offs in detection performance, computational complexity, and robustness to different noise and clutter conditions. For example, the GO-CFAR algorithm may provide robust performance in the presence of multiple targets or cluttered edges, while the SO-CFAR algorithm may be more suitable for environments with uniform background noise. On the other hand, the OS-CFAR algorithm may provide a balance between performance and complexity. In many embodiments, the determination of the CFAR threshold T for each interval includes multiple calculations of adjacent cells. Therefore, it can be seen that limiting the determination of the CFAR threshold to those exceeding the noise threshold C T The interval has the potential to advantageously reduce the total number of computations performed when performing target detection, thereby saving power.

[0028] The threshold comparison block 118 is configured to compare the intervals of the range-Doppler map or the range-angle map with the CFAR threshold T to determine whether a particular interval represents the presence of a target. In some embodiments, the intervals of the range-Doppler map or the range-angle map that have been determined by the map intensity comparison block to exceed the noise threshold C T The radar chart values of perform this comparison.

[0029] Reference noise estimation block 110 determines a reference noise level N of radar sensor 102, for example, by averaging noise values generated by radar sensor 102. In some embodiments, as further explained herein, the reference noise level is determined when no target is present. In some embodiments, the determination of whether a target is present can be made by motion sensor 122, which can be an optical sensor, an acoustic sensor, and / or an inertial sensor.

[0030] In some embodiments, additional processing 108 may be selectively performed on one or more outputs of pre-processing block 106 and the per-interval target presence indication generated by threshold comparison block 118 to produce a radar detector output. Additional processing 108 may include, but is not limited to, processing to perform target tracking and / or identification. In some embodiments, additional processing block 108 may be configured to identify targets using methods and algorithms known in the art. These algorithms may include, but are not limited to, machine learning algorithms for gesture recognition, facial recognition, presence detection, and life sensing.

[0031] In various embodiments, the processing system 104 may be implemented using hardware-based and / or software-based components. The hardware components may include one or more application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and / or digital signal processors (DSPs) configured to efficiently perform high-speed signal processing tasks. In some embodiments, one or more portions of the processing system 104 may be implemented using a general-purpose processor. In some embodiments, one or more memories may reside within the processing system 104 that store program code configured to be executed by the general-purpose processor or other programmable components (such as a DSP) to perform the operations described herein.

[0032] Figure 2 The figure shows a detailed example of the pre-processing block 106 and the reference noise estimation block 110. The pre-processing block 106 performs various RF signal processing tasks to prepare the radar signal for further analysis. These tasks include removing the average value from the radar sensor signal, performing a fast Fourier transform (FFT) to convert the signal to the frequency domain, and applying a windowing function to minimize spectral leakage. The specific methods and algorithms used in the pre-processing block 106 can vary based on the desired performance characteristics, computing resources, and the specific application of the radar system 100.

[0033] Pre-processing block 106 includes a mean removal block 132 that removes the mean from the radar sensor signal generated by radar sensor 102. This block can be used to center the signal around zero, which can improve subsequent processing steps. The mean removal block can be configured, for example, to determine the mean of the radar signal and subtract the determined mean value of the signal over a period of time from each signal sample. In some cases, other types of signal conditioning methods (such as normalization, filtering, or decimation) can be used instead of or in addition to mean removal.

[0034] The pre-processing block 106 also includes a range FFT and windowing block 134 that performs a range FFT and windowing on the output of the mean removal block 132. This block converts the time domain signal to the frequency domain to produce an array indicating the detected objects relative to their range (e.g., the distance from the radar sensor). A windowing function may be applied before the FFT to minimize spectral leakage. Implementations may include using digital signal processing (DSP) techniques and algorithms to perform the FFT calculation and applying a windowing function (such as a Hamming, Hanning, or Blackman window) to the signal.

[0035] A one-dimensional (1D) moving target indicator (MTI) block 136 in the pre-processing block 106 performs the MTI of the output range FFT and windowing block 134. Performing MTI filtering is used to distinguish targets from clutter (e.g., only retaining targets with, for example, high motion because their energy varies on the Doppler image). Therefore, after MTI filtering, the target can be identifiable, while information about the background can be partially or completely removed. This module filters out static or slowly moving objects from the signal, which helps to emphasize moving targets. The 1-D MTI block 136 can implement, for example, MTI techniques and algorithms known in the art. In an alternative embodiment, the 1-D MTI block 136 can be implemented in two dimensions.

[0036] The Doppler windowing block 138 performs Doppler windowing on the output of the ID MTI block 136 to reduce spectral leakage before performing a Doppler FFT. The Doppler FFT block 140 generates a range-Doppler map based on the output of the Doppler windowing block 138. The range-Doppler map is a two-dimensional representation of the target's range and velocity.

[0037] The reference noise estimation block 110 determines the reference noise level N of the radar sensor 102 for setting the noise threshold C T . This module estimates the noise level in the radar signal by performing statistical analysis on the signal when no target is present to determine a baseline noise level. The reference noise estimation block 110 includes a range-angle map and SNR estimation block 142, which generates a range-angle map and an SNR estimate for each Doppler slice. In some embodiments, the angle is estimated using digital beamforming techniques based on the range-Doppler map known in the art. The output of the range-angle map and SNR estimation block 142 is a three-dimensional array with dimensions Nrng×Ndop×Nang, where Nrng represents the number of range bins in the first dimension, Ndop represents the number of Doppler bins in the second dimension, and Nang represents the number of angle bins in the third dimension. In some embodiments, a corresponding SNR is estimated for each Doppler slice in the second dimension. For example, the signal power can be estimated by finding the maximum value of each two-dimensional (Nrng×Nang) range-angle slice corresponding to a particular Doppler bin, and the noise value can be estimated by calculating the variance of the interval of the range-angle slice. From here, the SNR is calculated by dividing the estimated signal power by the estimated noise value. It should be understood that this method of estimating the SNR is just one example of many possible ways to perform this determination. Alternatively, other known SNR estimates can be used.

[0038] The SNR Doppler filter block 144 in the reference noise estimation block 110 selects the top Doppler index with the maximum power to generate a distance-angle graph. An angle in the angle interval is selected by the angle interval selection block 146, and data is collected over multiple frames Nframes. In one embodiment, the zero-degree range angle interval is selected for noise determination. However, in alternative embodiments, other angle intervals can be used. The output of the angle interval selection block 146 is two-dimensionally (2D) averaged to generate an estimate of the reference noise level N. In some cases, instead of determining the average power of all intervals, other statistical measurements (such as median, mode or other statistical measurements) can be used to estimate the noise level.

[0039] It should be understood that the implementation of the reference noise estimation block 110 is only one of many possible example implementations. In alternative embodiments, different and / or modified processing steps may be used to determine the reference noise N. For example, in some embodiments, step 2D averaging step 148 may be implemented by averaging different types of radar data in one or three or more dimensions. For example, a three-dimensional mean of all angle bins over multiple frames may be determined. In some embodiments, the mean squared power of the radar sensor signal, the mean of the output of the range FFT and windowing blocks, the mean of the output of the Doppler FFT block 140, or other mean calculations may be used to determine the reference noise (N).

[0040] Figure 3 A block diagram of an application scenario 300 of radar system 100 is illustrated in the context of monitoring target area 204. As shown, radar system 100 includes radar sensor 102, processing system 104, and motion sensor 122, as discussed above. In an embodiment, motion sensor 122 monitors target area 204 to determine when the target area does not include moving targets. Target area 204 can represent an area or space in which the radar system is designed to detect objects. This can be a specific volume of air, a section of ground, or any other defined area of space into which signals transmitted by the radar sensor are directed and from which reflected signals are expected to be received. In various embodiments, monitoring target area 204 can involve appropriately positioning the radar sensor, calibrating its range and sensitivity, and using the processing system to analyze received signals to identify and locate targets within the area.

[0041] In some embodiments, as described above for Figure 1 and Figure 2As described above, the reference noise reference estimation block 110 within the processing block 104 is triggered to perform a reference noise measurement in response to the motion sensor 122 detecting the absence of a target in the target area 204. In some embodiments, the motion sensor 122 can be configured to sense motion periodically and / or at predetermined time periods. The output of the motion sensor 122 can also be used to determine when the radar system 102 is operating in a low-power mode. For example, when the motion sensor 122 does not detect motion, the radar sensor 102 can operate at a lower frame rate, and can operate at a higher frame rate in response to the motion sensor 122 sensing motion. In some cases, the motion sensor 122 can be an optical sensor, an acoustic sensor, and / or an inertial sensor. The specific type of motion sensor 122 used can vary based on the desired performance characteristics, computing resources, and the specific application of the radar system 100.

[0042] Figure 4A and Figure 4B Detection maps are shown, illustrating the performance of the enhanced CFAR technique according to an embodiment compared to a conventional CFAR technique. Each detection map represents the detection output for a scene in which a person stands 4 meters in front of a wall within the radar sensor's target range. For the first few frames, no one else is in the scene. Then, a second person walks into the field of view and subsequently leaves. This scene further assumes a 41×64 2D radar map, which has a total of 2624 input cells.

[0043] Figure 4A and Figure 4B The horizontal axis of each detection plot represents the frame number, indicating the order of radar frames over time. The vertical axis represents distance in meters, showing the distance at which an object was detected from the radar sensor. Lighter shades in the detection plot represent higher detection intensity levels, while darker shades represent lower intensity levels.

[0044] Use a noise threshold of -121dB C T generate Figure 4A Assume that the background noise is -21dB and α = -100dB. Therefore, the noise threshold C T This is well below the noise floor of the radar sensor 102, which effectively allows all radar map values to be evaluated by the CFAR threshold estimation block 116. Thus, the detection map represents the output of conventional CFAR processing.

[0045] On the other hand, using a noise threshold of -15dB C T generate Figure 4B Assuming the noise floor is -21 dB, α = 6 dB, which is 6 dB higher than the noise floor of the radar sensor 102. This only allows the CFAR algorithm to process radar map values exceeding C TThe threshold is -15dB. Figure 4A Fewer calculations are performed to produce Figure 4B However, Figure 4A and Figure 4B The detection graphs for appear qualitatively identical, indicating that in some embodiments, embodiment CFAR techniques may provide similar performance using less computation.

[0046] Figure 4A and Figure 4B The numerical comparison of the two scenarios is shown in Table 1. The detection rate is calculated as the percentage of true positive frames to the total number of frames. T =-15dB, the detection rate of the embodiment CFAR detection technique is 70.97%, which is very close to the detection rate of 71.9% using conventional CFAR technology. On the other hand, in this example, compared with processing all 2624 input units in a total processing time of 140ms, the embodiment CFAR technology only involves processing a total of 251 selected units in a processing time of 41.5ms. The reduced processing time also produces a corresponding power reduction. In the example provided, when there is a detected signal, the detection rate and calculation examples are determined over frames 216 to 533. Therefore, it can be seen that the embodiment CFAR technology potentially provides similar accuracy to conventional CFAR, but with less calculation and lower processing power.

[0047]

[0048] Table 1

[0049] It should be understood that Figure 4A and Figure 4B The example is only one example of many possible examples under a specific set of conditions and assumptions. The results of other performance comparisons may vary depending on the specific system implementation and test conditions.

[0050] Figure 5A A schematic diagram of a millimeter wave radar system 500 is shown, illustrating an example implementation of a millimeter wave frequency modulated continuous wave (FMCW) radar system 502 that may be used to implement radar sensor 102, in accordance with an embodiment of the present invention. Millimeter wave radar sensor 502 is shown coupled to an embodiment processing system 104.

[0051] Millimeter wave radar sensor 502 can operate as a frequency modulated continuous wave (FMCW) radar sensor that uses one or more transmitter (TX) antennas 514 to transmit multiple TX radar signals 506 (such as chirps) to scene 520. Radar signal 506 is generated using RF and analog circuitry 530. Radar signal 506 can be in the range of 20 GHz to 522 GHz, for example. Other frequencies can also be used. Objects in scene 520 can include one or more static or moving objects, such as surfaces (e.g., tables, countertops, etc.), keyboards, walls, and parts of people (such as hands, heads, or fingers). Other objects can also be present in scene 520.

[0052] Radar signal 506 is reflected by an object in scene 520. Reflected radar signal 508, also known as an echo signal, is received by a plurality of receive (RX) antennas. RF and analog circuitry 530 processes received reflected radar signal 508 in a manner known in the art using, for example, a bandpass filter (BPF), a lowpass filter (LPF), a mixer, a low noise amplifier (LNA), and / or an intermediate frequency (IF) amplifier to generate an analog signal x. outa (t) and x outb (t).

[0053] The analog signal x is converted into outa (t) and x outb (t) is converted into raw digital data x out_dig (n). The processing system 104 processes the raw digital data x out_dig (n) to detect one or more objects and their locations. In some embodiments, processing system 104 can also be used to track one or more objects in scene 520.

[0054] although Figure 5A A radar system with two receiver antennas 516 (antennas 516a and 516b) is shown, but it should be understood that more than two receiver antennas 516 may be used, such as three or more receiver antennas 516. In some embodiments, using more receiver antennas 516 may result in higher spatial resolution. Figure 5A A radar system having a single transmit antenna 514 is illustrated, but it should be understood that more than one transmit antenna 514, such as two or more transmit antennas 514, may be used. Figure 5A ADC 512 is shown as being partitioned within radar sensor 502 , but in some embodiments, ADC 512 may be implemented within processing system 104 .

[0055] Controller 510 controls one or more circuits of millimeter wave radar sensor 502, such as RF and analog circuits 530 and / or ADC 512. Controller 510 can be implemented, for example, as a custom digital or mixed-signal circuit. For example, controller 510 can also be implemented in other ways, such as using a general-purpose processor or controller. In some embodiments, processing system 104 implements part or all of controller 510.

[0056] The processing system 104 can be implemented by a general-purpose processor, a controller, or a digital signal processor (DSP), including, for example, a combination circuit coupled to a memory. In some embodiments, the processing system 104 can be implemented as an application-specific integrated circuit (ASIC). In some embodiments, the processing system 104 can be implemented using, for example, an ARM, RISC, or x86 architecture. In some embodiments, the processing system 104 can include an artificial intelligence (AI) accelerator. Some embodiments can use a combination of a hardware accelerator and software running on a DSP or a general-purpose microcontroller. Other implementations are also possible.

[0057] In some embodiments, part or all of the millimeter wave radar sensor 502 and the processing system 104 can be implemented inside the same integrated circuit (IC). For example, in some embodiments, part or all of the millimeter wave radar sensor 502 and the processing system 104 can be implemented in corresponding semiconductor substrates integrated in the same package. In other embodiments, part or all of the millimeter wave radar sensor 502 and the processing system 104 can be implemented in the same monolithic semiconductor substrate. In some embodiments, the millimeter wave radar sensor 502 and the processing system 104 are implemented in corresponding integrated circuits. In some embodiments, the millimeter wave radar sensor 502 is implemented using multiple integrated circuits. In some embodiments, the processing system 104 is implemented using multiple integrated circuits. Other implementations are also possible.

[0058] As a non-limiting example, the RF and analog circuits 530 may be implemented, for example, as Figure 5A As shown. During normal operation, voltage-controlled oscillator (VCO) 536 generates a radar signal, such as a linear frequency chirp (e.g., from 57 GHz to 64 GHz, or from 76 GHz to 77 GHz), which is transmitted by transmit antenna 514. Alternatively, VCO 536 can be configured to generate RF pulses according to pulsed radar technology. VCO 536 is controlled by PLL 534, which receives a reference clock signal (e.g., 80 MHz) from reference oscillator 532. PLL 534 is controlled by a loop including divider 538 and amplifier 540. Amplifier 537 can be used to drive transmit antenna 514.

[0059] TX radar signal 506 transmitted by transmit antenna 514 is reflected by objects in scene 520 and received by receive antennas 516a and 516b. The echoes received by receive antennas 516a and 516b are mixed with replicas of the signal transmitted by transmit antenna 514 using mixers 546a and 546b, respectively, to produce corresponding intermediate frequency (IF) signals x IFa (t)x IFb (t) (also called a beat signal). In some embodiments, the beat signal x IFa (t) and x IFb (t) has a bandwidth between 50 kHz and 5 MHz. Beat frequency signals with bandwidths below 50 kHz or above 5 MHz are also possible. Amplifiers 545a and 545b can be used to receive reflected radar signals from antennas 516a and 516b, respectively.

[0060] Beat frequency signal x IFa (t) and x IFb (t) is filtered by respective low pass filters (LPFs) 548a and 548b and then sampled by ADC 512. ADC 512 is advantageously capable of sampling the filtered beat signal x at a sampling frequency that is much lower than the frequency of the signals received by receive antennas 516a and 516b. outa (t) and x outb (t) Samples are taken. Thus, in some embodiments, the use of FMCW radar advantageously allows for a compact and low-cost implementation of ADC 512.

[0061] Original digital data x out_dig (n) In some embodiments, the filtered beat signal x outa (t) and x outb A digitized version of (t) is stored (eg, temporarily) in, for example, N of each receive antenna 516. c ×N s In the matrix, N c is the number of chirps considered in the frame, and N s is the number of transmitted samples per chirp for further processing by the processing system 104 .

[0062] Figure 5B 5 shows a sequence of chirps 556 transmitted by TX antenna 514 according to an embodiment of the present invention. Figure 5B As shown, chirp 556 is organized into multiple frames (also referred to as physical frames) and can be implemented as an up-chirp. Some embodiments may use a down-chirp or a combination of up-chirp and down-chirp, such as up-down chirp and down-up chirp. Other waveforms may also be used.

[0063] like Figure 5BAs shown, each frame may include a plurality of chirps 556 (also commonly referred to as pulses). For example, in some embodiments, the number of chirps in a frame is 64. Some embodiments may include more than 64 chirps per frame (such as 96 chirps, 528 chirps, 256 chirps, or more), or fewer than 64 chirps per frame (such as 32 chirps or less).

[0064] In some embodiments, the frame is repeated every FT time. In some embodiments, the FT time is 50 ms. Different FT times may be used, such as greater than 50 ms (such as 60 ms, 500 ms, 200 ms, or more), or less than 50 ms (such as 45 ms, 40 ms, or less). In some embodiments, the FT time is selected so that the time between the start of the last chirp of frame n and the start of the first chirp of frame n+5 is equal to the PRT. Other embodiments may use or result in different timings.

[0065] The time between frame chirps is generally referred to as the pulse repetition time (PRT). In some embodiments, the PRT is 5 ms. Different PRTs may also be used, such as less than 5 ms (such as 4 ms, 2 ms, 0.5 ms or less), or greater than 5 ms (such as 6 ms or more).

[0066] The duration of a chirp (from start to end) is generally referred to as the chirp time (CT). In some embodiments, the chirp time can be, for example, 64 μs. Higher chirp times, such as 528 μs or higher, can also be used. Lower chirp times can also be used.

[0067] In some embodiments, the chirp bandwidth may be, for example, 4 GHz. Higher bandwidths, such as 6 GHz or higher, or lower bandwidths, such as 2 GHz, 5 GHz or lower, are also possible.

[0068] In some embodiments, the sampling frequency of the millimeter wave radar sensor 502 may be, for example, 5 MHz. A higher sampling frequency (such as 2 MHz or higher) or a lower sampling frequency (such as 500 kHz or lower) is also possible.

[0069] In some embodiments, the number of samples used to generate the chirp may be, for example, 64 samples. A larger number of samples (such as 528 samples or more) or a smaller number of samples (such as 32 samples or less) may also be used.

[0070] It should be understood that Figure 5A and 5B The embodiment of the millimeter wave radar system 502 described in is only one example of many possible radar systems that may be used in embodiments. For example, in an alternative embodiment, a pulse radar system may be used.

[0071] Figure 6 A flow chart of an example method 600 is illustrated. In some implementations, one or more steps of method 600 may be performed by processing system 104.

[0072] As shown, the method 600 includes a reference setting mode 620 and a detection mode 630. In various embodiments, the reference setting mode is used to determine a reference noise level (N), which is used to determine a noise threshold C T In some embodiments, the reference setting mode may be triggered in response to the motion sensor 122 determining that there is no motion in the target area 204 .

[0073] Reference setting mode 620 may include: when there is no moving target within the detection range of the millimeter wave radar system, receiving a first radar signal from the millimeter wave radar sensor (step 602), estimating a total noise level of the received first radar signal (step 604), and determining a first noise threshold based on the estimated total noise level (block 606). For example, as described above, when there is no moving target within the detection range of the millimeter wave radar system, processing system 104 may receive the first radar signal from the millimeter wave radar sensor, estimate the total noise level of the received first radar signal using reference noise estimation block 110, and determine the first noise threshold based on the estimated total noise level.

[0074] Detection mode 630 may be a normal operating mode in which radar system 100 performs target detection, and may include receiving a second radar signal from a millimeter-wave radar sensor (step 608), generating a range-Doppler radar map from the received second radar signal, comparing amplitudes of intervals of the range-Doppler radar map with a first noise threshold to produce a first subset of intervals having amplitudes exceeding the first noise threshold, wherein a second subset of intervals, different from the first subset, has amplitudes that do not exceed the first noise threshold (step 610), applying a constant false alarm rate (CFAR) detection algorithm to the first subset of intervals but not to the second subset of intervals (step 612), and detecting the target based on the applied CFAR detection algorithm (block 614). For example, the processing system 104 may receive a second radar signal from the millimeter-wave radar sensor 102, generate a range-Doppler radar map from the received second radar signal via the pre-processing block 106, compare the amplitudes of intervals of the range-Doppler radar map with a first noise threshold via block 112 to produce a first subset of intervals whose amplitudes exceed the first noise threshold, wherein a second subset of intervals different from the first subset have amplitudes that do not exceed the first noise threshold, and apply a constant false alarm rate (CFAR) detection algorithm to the first subset of intervals but not to the second subset of intervals via the CFAR threshold estimation block 116, and detect targets based on the applied CFAR detection algorithm, as described above.

[0075] although Figure 6 Example blocks of method 600 are shown, but in some implementations, method 600 may include Figure 6 More blocks, fewer blocks, different blocks, or differently arranged blocks are depicted. Additionally or alternatively, two or more blocks of method 600 may be executed in parallel.

[0076] Reference Figure 7 , a block diagram of a processing system 700 is provided in accordance with an embodiment of the present invention. Processing system 700 depicts a general platform and general components and functionality that may be used to implement portions of the embodiments described herein, such as processing system 104.

[0077] The processing system 700 may include, for example, a central processing unit (CPU) 702 and a memory 704 connected to a bus 708, and may be configured to execute the above-described processes according to program instructions stored in the memory 704 or other non-transitory computer-readable medium. If desired or necessary, the processing system 700 may also include: a display adapter 710 for providing a connection to a local display 712; and an input-output (I / O) adapter 714 for providing an input / output interface for one or more input / output devices 716 (such as a mouse, keyboard, flash drive, etc.).

[0078] The processing system 700 may also include a network interface 718, which may be implemented using a network adapter configured to couple to a wired link (such as a network cable, a USB interface, etc.) and / or a wireless / cellular link for communicating with the network 720. The network interface 718 may also include a suitable receiver and transmitter for wireless communication. It should be noted that the processing system 700 may include other components. For example, if implemented externally, the processing system 700 may include hardware components, a power supply, cables, a motherboard, removable storage media, a housing, etc. Although not shown, these other components are considered part of the processing system 700. In some embodiments, the processing system 700 may be implemented on a single monolithic semiconductor integrated circuit and / or on the same monolithic semiconductor integrated circuit as other disclosed system components.

[0079] Embodiments of the present invention are summarized below. Other embodiments can also be understood from the overall description and claims submitted herein.

[0080] Example 1. A method of operating a radar system may include: estimating a total noise level of a received radar signal; determining a first noise threshold based on the estimated total noise level; generating a radar map having a plurality of intervals from the radar signal; determining a first subset of the plurality of intervals whose magnitude exceeds the first noise threshold; determining a constant false alarm rate (CFAR) threshold for only each interval of the first subset of the plurality of intervals; and determining that the intervals of the first subset whose magnitude exceeds the CFAR threshold correspond to one or more detected objects.

[0081] Example 2. The method of Example 1, wherein estimating the total noise level of the received radar signal may include: generating a reference radar map when no moving targets are present within the detection range of the radar system; and determining an average power across all bins of the reference radar.

[0082] Example 3. The method of example 1 or example 2, wherein the reference radar map is a range-Doppler radar map.

[0083] Example 4. The method of any one of Examples 1 to 3, wherein the reference radar map is part of a range-angle map of a range-Doppler map, the range-angle map of the range-Doppler map representing the range angles within the range-Doppler map.

[0084] Example 5. The method of example 4, wherein the distance angle is zero degrees.

[0085] Example 6. The method of any one of Examples 1 to 5, wherein determining the first noise threshold based on the total noise level may include adding a predetermined offset to the estimated total noise level.

[0086] Example 7. The method of Example 6, wherein the predetermined offset is between 5 dB and 10 dB.

[0087] Example 8. The method of any of Examples 1 to 7, wherein determining a first subset of the plurality of intervals whose amplitude exceeds a first noise threshold may include comparing the amplitude of each of the plurality of intervals of the radar plot to the first noise threshold.

[0088] Example 9. The method of any of Examples 1 to 8, wherein determining the CFAR threshold for each interval of the first subset of intervals may include determining an average amplitude of a defined set of neighboring intervals surrounding each interval of the first subset of intervals.

[0089] Example 10. The method according to any one of Examples 1 to 9 may further include: receiving a radar signal from a millimeter wave radar sensor.

[0090] Example 11. A method of operating a millimeter-wave radar system may include: during a reference setting mode: when there is no moving target within a detection range of the millimeter-wave radar system, receiving a first radar signal from a millimeter-wave radar sensor, estimating a total noise level of the received first radar signal, and determining a first noise threshold based on the estimated total noise level; and during a detection mode different from the reference setting mode: receiving a second radar signal from the millimeter-wave radar sensor, generating a range-Doppler radar map from the received second radar signal, comparing amplitudes of intervals of the range-Doppler radar map with a first noise threshold to produce a first subset of intervals whose amplitudes exceed the first noise threshold, wherein a second subset of the intervals different from the first subset has an amplitude that does not exceed the first noise threshold, applying a constant false alarm rate (CFAR) detection algorithm to the first subset of intervals but not to the second subset of intervals, and detecting a target based on the applied CFAR detection algorithm.

[0091] Example 12. The method according to Example 11 may further include: determining whether there is a moving target within the detection range of the radar system; and activating a reference setting mode in response to determining that there is no moving target within the detection range of the radar system.

[0092] Example 13. The method of Example 12, wherein determining whether the mobile target is present may include: monitoring a detection range of the radar system using a first sensor different from the millimeter wave radar sensor.

[0093] Example 14. The method of Example 13, wherein the first sensor may include an optical sensor, an acoustic sensor, or an inertial sensor.

[0094] Example 15. A system may include: a millimeter wave radar sensor; and a processor coupled to the millimeter wave radar sensor, the processor configured to: during a reference setting mode, estimate a total noise level of a first radar signal received from the millimeter wave radar sensor and determine a first noise threshold based on the estimated total noise level, and during a detection mode different from the reference setting mode, generate a range-Doppler radar map based on a second radar signal received from the millimeter wave radar sensor, determine a first subset of intervals whose amplitudes exceed the first noise threshold, wherein a second subset of intervals different from the first subset has an amplitude that does not exceed the first noise threshold, apply a constant false alarm rate (CFAR) detection algorithm to the first subset of intervals but not to the second subset of intervals, and detect a target based on the applied CFAR detection algorithm.

[0095] Example 16. The system according to Example 15 may also include: one or more transmitting antennas, the one or more transmitting antennas being coupled to the millimeter wave radar sensor; and one or more receiving antennas, the one or more receiving antennas being coupled to the millimeter wave radar sensor.

[0096] Example 17. The system according to Example 15 or Example 16 may also include an optical sensor coupled to the processor and configured to monitor the detection distance of the millimeter wave radar sensor, wherein the processor is configured to enter a reference setting mode in response to the optical sensor indicating that there is no motion within the detection distance of the millimeter wave radar sensor.

[0097] Example 18. A system according to any one of Examples 15 to 17, wherein: the processor is configured to estimate the total noise level of the first radar signal by generating a reference radar map when no moving target is present within the detection range of the millimeter wave radar sensor, and determining the average power of all intervals of the reference radar map; and the processor is configured to determine the first noise threshold by adding a predetermined offset to the estimated total noise level.

[0098] Example 19. A system according to any of Examples 15 to 18, wherein the processor is configured to apply the CFAR detection algorithm to the first subset of intervals but not the second subset of intervals by: determining a CFAR threshold only for each interval in the first subset of intervals but not for each interval in the second subset of intervals; and determining that the intervals of the first subset whose magnitude exceeds the CFAR threshold correspond to the detected object.

[0099] Example 20. The system of any of Examples 15 to 19, wherein the millimeter wave radar sensor is a frequency modulated continuous wave (FMCW) radar sensor.

[0100] Although the present invention has been described with reference to illustrative embodiments, this description is not intended to be limiting. Various modifications and combinations of the illustrative embodiments, as well as other embodiments, will be apparent to those skilled in the art upon reference to the specification. Accordingly, the appended claims are intended to encompass any such modifications or embodiments.

Claims

1. A method of operating a radar system, the method comprising: Estimating the total noise level of the received radar signal; determining a first noise threshold based on the estimated total noise level; generating a radar map from the radar signal, the radar map comprising a plurality of intervals; determining a first subset of the plurality of intervals whose magnitudes exceed the first noise threshold; determining a constant false alarm rate (CFAR) threshold for each interval of only the first subset of the plurality of intervals; as well as The first subset of intervals having magnitudes exceeding the CFAR threshold are determined to correspond to one or more detected objects.

2. The method of claim 1 , wherein estimating the total noise level of the received radar signal comprises: generating a reference radar map when no moving target exists within the detection range of the radar system; as well as The average power across all bins of the reference radar is determined. The method according to claim 2 , wherein the reference radar map is a range-Doppler radar map. 4 . The method according to claim 2 , wherein the reference radar map is a portion of a range-angle map of a range-Doppler map, the range-angle map of the range-Doppler map representing range angles within the range-Doppler map. The method of claim 4 , wherein the distance angle is zero degrees.

6. The method of claim 1 , wherein determining the first noise threshold based on the total noise level comprises: A predetermined offset is added to the estimated total noise level. The method of claim 6 , wherein the predetermined offset is between 5 dB and 10 dB.

8. The method of claim 1 , wherein determining the first subset of the plurality of intervals whose magnitude exceeds the first noise threshold comprises: The magnitude of each of the plurality of bins of the radar plot is compared to the first noise threshold.

9. The method of claim 1 , wherein determining the CFAR threshold for each interval of the first subset of intervals comprises: An average magnitude of a defined set of neighboring intervals surrounding each interval of said first subset of intervals is determined.

10. The method according to claim 1, further comprising: The radar signal is received from a millimeter-wave radar sensor.

11. A method of operating a millimeter wave radar system, the method comprising: During reference setting mode: When there is no moving target within the detection range of the millimeter wave radar system, receiving a first radar signal from the millimeter wave radar sensor, estimating a total noise level of the received first radar signal, and determining a first noise threshold based on the estimated total noise level; and during a detection mode different from said reference setting mode: receiving a second radar signal from the millimeter-wave radar sensor, generating a range-Doppler radar map from the received second radar signal, comparing the amplitudes of the intervals of the range-Doppler radar plot to the first noise threshold to produce a first subset of intervals having amplitudes exceeding the first noise threshold, wherein a second subset of intervals, different from the first subset, has amplitudes that do not exceed the first noise threshold, applying a constant false alarm rate (CFAR) detection algorithm to said first subset of intervals but not to said second subset of intervals, and The target is detected based on the applied CFAR detection algorithm.

12. The method according to claim 11, further comprising: determining whether a moving target exists within the detection range of the radar system; as well as In response to determining that no moving target exists within the detection range of the radar system, the reference setting mode is activated.

13. The method according to claim 12, wherein determining whether the mobile target exists comprises: The detection distance of the radar system is monitored using a first sensor different from the millimeter wave radar sensor. The method of claim 13 , wherein the first sensor comprises an optical sensor, an acoustic sensor, or an inertial sensor.

15. A system comprising: millimeter-wave radar sensors; as well as a processor coupled to the millimeter-wave radar sensor, the processor being configured to: During a reference setting mode, estimating a total noise level of a first radar signal received from the millimeter-wave radar sensor, and determining a first noise threshold based on the estimated total noise level, and During a detection mode different from the reference setting mode, a range-Doppler radar map is generated based on a second radar signal received from the millimeter-wave radar sensor, a first subset of intervals having amplitudes exceeding the first noise threshold is determined, wherein a second subset of intervals different from the first subset has amplitudes that do not exceed the first noise threshold, a constant false alarm rate (CFAR) detection algorithm is applied to the first subset of intervals but not to the second subset of intervals, and a target is detected based on the applied CFAR detection algorithm.

16. The system of claim 15, further comprising: one or more transmit antennas coupled to the millimeter wave radar sensor; as well as One or more receiving antennas, the one or more receiving antennas being coupled to the millimeter wave radar sensor.

17. The system of claim 15, further comprising an optical sensor coupled to the processor and configured to monitor the detection range of the millimeter-wave radar sensor, wherein the processor is configured to enter the reference setting mode in response to the optical sensor indicating that there is no motion within the detection range of the millimeter-wave radar sensor.

18. The system of claim 15, wherein: The processor is configured to estimate the total noise level of the first radar signal by generating a reference radar map when no moving target exists within the detection range of the millimeter wave radar sensor and determining an average power of all intervals of the reference radar map; and The processor is configured to determine the first noise threshold by adding a predetermined offset to the estimated total noise level.

19. The system of claim 15, wherein the processor is configured to apply the CFAR detection algorithm to the first subset of intervals but not to the second subset of intervals by: determining a CFAR threshold only for each interval in said first subset of intervals and not for each interval in said second subset of intervals; and It is determined that the first subset of intervals having a magnitude exceeding the CFAR threshold corresponds to a detected object.

20. The system of claim 15, wherein the millimeter wave radar sensor is a frequency modulated continuous wave (FMCW) radar sensor.

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