Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review
Abstract
:1. Introduction
2. Performance Metrics in mmWave-Radar-Based Sensing
- Range—The range is estimated by analyzing the frequency content in the IF signal. As shown in Figure 2a, transmitted and received chirps as a function of time for a single object detected. It can be observed that the received chirp is a time-delay () version of the transmitted chirp.The time delay can be measured as:The initial phase of the IF signal isThe range is computed as:The maximum range is decided via the sampling frequency of this IF signal. The larger the sampling frequency, the better the maximum range capacity of the radar will be. The other deciding factor for the maximum range is the transmission power. There is no hardware constraint on the accurate range estimation of multiple targets. One TX antenna and one RX antenna are sufficient.Another fundamental limitation for maximum range comes from the transmitting power:
- Velocity—mmWave radar estimates the velocity of multiple targets using the phase difference between IF signals, as illustrated in Figure 2b. There is no hardware constraint for the accurate velocity estimation of the multiple targets. One TX antenna and one RX antenna are sufficient.The phase difference is derived as = /, where is the chirp duration time. The velocity is computed as:The measurement is unambiguous only if || < . We can derive that vs. < /4.The maximum possible velocity estimation depends on how fast chirps can be transmitted.
- Angle of arrival—Estimating the angle of arrival of one object requires at least one TX antenna and two RX antennas as shown in Figure 3. The greater the number of RX antennas, the better the AoA estimation performance will be. By using MIMO, more transceiver elements can be made with a limited number of TX and RX antennas. However, accurate AoA estimation of multiple targets demands more hardware resources, such as more TX and RX antennas.The phase difference between the IF signals of the two receivers is derived as:Under the assumption of a planar wavefront basic geometry R = d), where d is the distance between the receiving antennas. The angle of arrival is computed fromThe unambiguous measurement of the angle of arrival requires that () < .This leads to:
- Range resolution—The shortest distance at which two objects can become close while still being detected as two distinct objects via radar. The smaller this distance, the better the resolving capability of the radar will be. A radar with greater RF bandwidth gives better range resolution.The smallest frequency differences of an IF signal are related to the chirp duration, .Range resolution depends only on the RF bandwidth swept by the chirp.
- Velocity resolution—The smallest velocity difference the two targets can have while still being detected via radar as two distinct targets with two distinct velocities. Velocity resolution can be improved by increasing the frame time. A frame consists of a number of series of chirps.One can mathematically derive the velocity resolution (Vres) if the frame period = N.V > Vres = /(2 )
- Angle of arrival resolution—The angle of arrival resolution is the smallest angle that can be formed between two targets and radar while still being detected via the radar as two distinct targets. The smaller the angle of arrival resolution, the better the radar’s resolving capability. Extending the number of antennas (both TX and RX) improves the AoA resolution.
- Sensor type—The RF bandwidth, IF bandwidth, ADC sampling rate, range resolution, velocity resolution, and AoA resolution are important key factors in deciding the performance. mmWave radar sensors are broadly categorized into three sensor types: long-range, medium-range, and short-range radars, as listed in Table 4. The appropriate sensor should be chosen depending on the application demands and needs.
- Frequency and bandwidth—It is advantageous to have a high-frequency sensor; it uses a low antenna size and gives a better angular resolution. High bandwidth offers a high-range resolution. A 77 GHz frequency radar with a 4 GHz bandwidth gives a range resolution of 3.75 cm. Popular mmWave radar models with specifications are listed in Table 5.
- Accessories—As the needs of mmWave radars change, upgrading the firmware and software becomes necessary. Hence, choosing a manufacturer that can offer stable software updates is important. mmWave radars have been integrated with Lidars in [35,36] to provide better results; additionally, radars fused with cameras or infrared sensors are studied in [37,38,39]. Texas Instruments (TI) introduced a commercial radar, TDA3x, board for radar camera fusion to provide effective tracking and detection applied in [40]. There is a discussion of fusion techniques in [41,42], such as low-level and feature-level fusion.
3. Measurements from mmWave Radar Sensor and Interpretation of the Data
3.1. Range Profile
3.2. Range–Azimuth Heatmap
3.3. Three-dimensional Scatter Plot
4. Applications of mmWave Radars and Machine Learning Techniques
4.1. Automotive Applications
4.2. Industrial Applications
4.3. Medical Applications
4.4. Robotics and Automation Applications
4.5. Security and Surveillance Applications
4.6. Civilian Applications
4.7. Other Applications
References | Year | Method | Application | Comments |
---|---|---|---|---|
[88] | 2016 | CNN, data transformation techniques | Fitness tracking |
|
[132] | 2016 | Random forest algorithm | Hand gesture recognition |
|
[84] | 2019 | Convolution neural network (CNN) | Human behavior detection |
|
[131] | 2019 | NN is compared with SVM, DT | Fall detection |
|
[153] | 2019 | CNN, ConvLSTM, RF | Received power prediction |
|
[112] | 2019 | LSTM | Channel tracking in vehicular system |
|
[45] | 2019 | PointNets | 2D car detection |
|
[46] | 2020 | CNN, RNN | Scene understanding via classification |
|
[47] | 2020 | DBSCAN, Faster R-CNN | Vehicle detection |
|
[110] | 2020 | Point clouds, GMM | Multimodal traffic monitoring |
|
[48] | 2020 | SAF-FOC framework | Obstacle detection |
|
[86] | 2020 | CNN | Detecting human skeletal pose |
|
[138] | 2021 | Graph neural network with LSTM | Human activity recognition and gesture recognition |
|
[154] | 2021 | SVM | Shape classification and object detection |
|
[98] | 2022 | CNN | Automatic monitoring of heart rate and breathing rate |
|
[70] | 2022 | CNN, K-nearest neighbor | Material identification |
|
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Working Band (GHz) | Bandwidth (GHz) | Resolution |
---|---|---|
24 | GHz | 60 cm |
77 | 4 GHz | cm |
60 | 7 GHz | cm |
94 | 2 GHz | cm |
100 | 9 GHz | cm |
300 | 40 GHz | cm |
300 | 20 GHz | cm |
Reference | Year | Focus Area | SP | MI | Appl | PM | ML/DL Tech. | Comments |
---|---|---|---|---|---|---|---|---|
[6] | 2011 | Different mmWave technology specifications | ✗ | ✗ | ✓ | ✗ | ✗ | Restricted to limited technologies and data processing |
[7] | 2017 | Focus on architecture, estimation techniques | ✓ | ✓ | ✓ | ✓ | ✗ | Restricted to advanced driver assistance system |
[4] | 2019 | Review of digital modulation and interference mitigation methods | ✓ | ✓ | ✓ | ✗ | ✗ | Restricted to automated driving technology |
[5] | 2020 | Working principles of vision sensors and performance parameters for autonomous systems | ✓ | ✗ | ✗ | ✓ | ✓ | Restricted to autonomous systems |
[8] | 2021 | Uses deep learning and fusion models | ✓ | ✗ | ✓ | ✓ | ✓ | Deep fusion operations and datasets need to be improved |
[3] | 2022 | To determine working principles, data representation methods, and challenges | ✓ | ✗ | ✓ | ✓ | ✗ | Restricted to detection applications |
[1] | 2022 | On various on-board sensors, hardware components, software environments, and machine learning algorithms | ✗ | ✓ | ✗ | ✓ | ✓ | Restricted to unmanned aerial vehicle applications |
Our Work | 2022 | Performance metrics of mmWave radar, state-of-the-art mmWave radar models available on the market, radar data interpretation, applications of mmWave radars, machine learning techniques for mmWave radar | ✓ | ✓ | ✓ | ✓ | ✓ | Considers sensing applications using mmWave radar sensor in broad areas of science and engineering |
Range | Velocity | Angle of arrival |
---|---|---|
R = ( C × )/ | V = / | = (( )/(2 d)) |
Range resolution | Velocity resolution | Angle of arrival resolution |
dres = | Vres = /(2 ) | res = 2/ |
Max range | Max velocity | Max angle of arrival |
dmax = C/ | Vmax = /4 | max = ((/2 d)) |
Type | Range (m) | Bandwidth (MHz) | Azimuth View Angle (deg.) | Elevation View Angle (deg.) | Applications |
---|---|---|---|---|---|
Long-range radar | 10–250 | 600 | ±15 | ±5 | Autonomous cruise control |
Medium-range radar | 1–100 | 600 | ±40 | ±5 | Lane change assistance system, collision mitigation |
Short-range radar | 1–30 | 4000 | ±80 | ±10 | Blind spot detection, parking assistance |
Reference | Model Name | Range (m) | Working Band (GHz) | Azimuth Field of View (deg) | Elevation Field of View (deg) | Chip Memory (MB) | User Interface and Connectivity |
---|---|---|---|---|---|---|---|
[13] | TI-IWR6843AOPEVM | 180 | 60 GHz–64GHz | ±120 | ±120 | 1.75 | TMMWAVEICBOOST, DCA1000, I2C, LVDS, QSPI, SPI, UART |
[14] | TI-IWR1843 | 180 | 76 GHz–81 GHz | 100 | ±15 | 2 | CAN, LVDS, QSPI, I2C, SPI, UART, CSI-2 |
[15] | Delphi ESR | 174 | 76 GHz | ±10 | ±45 | - | CAN |
[16] | NavTech CIR204-h | 200 | 76 GHz–77 GHz | 360 | - | - | 1 Gbps Ethernet |
[17] | TI-AWRL6432 | 250 | 57 GHz–64 GHz | ±18 | - | 1 | QSPI, PWM, I2C |
[18] | BoschLRR3 | 250 | 77 GHz | 30 | - | - | CAN |
[19] | Continental ARS408-21 | 250 | 77 GHz | ±9 | 14 | - | CAN 500 kbps |
[20] | Continental SRR600 | >180 | 76 GHz–81 GHz | ±90 | ±40 | - | Ethernet, CAN-FD |
[21] | TI-AWR1843AOPEVM | 150 | 76 GHz–81 GHz | 140 | 140 | 2 | DCA1000EVM, CAN, CAN-FD |
[22] | TI-AWR1642 | 100 | 76 GHz–81 GHz | ±60 | ±10 | CAN, CAN-FD, SPI, I2C, UART | |
[23] | TI-IWR1642 | 150 | 76 GHz–77 GHz | ±100 | ±15 | CAN, CAN-FD, QSPI | |
[24] | NXP-TEF810X | 250 | 76 GHz–81 GHz | ±18 | - | LVDS, CSI2 | |
[25] | NXP-SAF85xx | 250 | 76 GHz–81 GHz | ±18 | - | SGMII Ethernet, dual CAN FD | |
[26] | NXP-TEF82xx | 250 | 76 GHz–81 GHz | ±18 | - | CSI-2, LVDS | |
[27] | Continental-ARS540 | 300 | 76 GHz–81 GHz | ±60 | ±60 | 1 | CAN, Ethernet |
[23] | TI-IWR1443 | 60 | 77 GHz–79 GHz | ±65 | ±15 | LVDS, DCAN, QSPI, CSI2 | |
[28] | TI-AWR1443 | 60 | 76 GHz–81 GHz | ±65 | ±15 | LVDS, DCAN, QSPI | |
[29] | TI-IWRL6432 | 60 | 57 GHz–64 GHz | ±65 | ±15 | 1 | I2C, SPI, UART, QSPI |
[30] | Continental-ARS4-A | 250 | 77 GHz | ±75 | ±20 | 1 | CAN, Ethernet |
[31] | TI-AWR2243BOOST | 150 | 76 GHz–81 GHz | ±90 | ±90 | SPI, UART, I2C, CAN-FD | |
[32] | TI-AWR1243 | 250 | 76 GHz–81 GHz | ±60 | ±15 | 1 | SPI, MIPI-CSI2, UART |
[33] | NXP4D-S32R45 | 300 | 77 GHz | ±56 | ±56 | 1 | PCIe, Ethernet, CAN-FD |
[34] | RDK-S32R274 | 200 | 79–81 GHz | ±30 | ±30 | 1 | Ethernet, CAN-FD |
Reference | Year | Application | Radar Used |
---|---|---|---|
[58] | 1997 | Intelligent cruise control with collision warning | FMCW (76 GHz–77 GHz) |
[59] | 2017 | Blind spot detection and warning system | AWR1843 (76 GHz–77 GHz) |
[60] | 2017 | Automated emergency breaking | TI-AWR1243 (76 GHz–78 GHz) |
[60] | 2017 | In-car occupant detection | TI-AWR1642 (76 GHz–81 GHz) |
[61] | 2017 | Driver vital sign monitoring | TI-AWR1642 (77 GHz) |
[62] | 2018 | Automotive body and chassis sensing applications | TI-AWR1642 (77 GHz) |
[50] | 2018 | In-car controlling with gestures | FMCW-mmWave (60 GHz) |
[63] | 2019 | Automated parking system | TI-AWR1843 (77 GHz) |
[64] | 2020 | Lane change assistance with obstacle detection | TI-AWR1843AOPEVM (77 GHz) |
[65] | 2020 | Parking assistance with obstacle detection | TI-AWR1642BOOST (77 GHz–81 GHz) |
[66] | 2020 | Debris detection for automotive radar | mmWave (76 GHz–81 GHz) |
[67] | 2021 | Automotive vehicle detection in parking lot | TI AWR2243BOOST-MIMO (76 GHz–81 GHz) |
[65] | 2022 | Motor cycle safety and Blind spot detection | TI-AWR1843AOP (76 GHz–81 GHz) |
[55] | 2022 | Automotive corner radar for cross traffic alert | TI-AWR1843EVM (76 GHz–81 GHz) |
Reference | Year | Application | Radar Used |
---|---|---|---|
[75] | 2006 | Surface sensing | mmWave sensor (29.72 GHz–37.7 GHz) |
[76] | 2013 | Measuring the liquid level and interface sensing | mmWave Doppler sensor (77 GHz) |
[68] | 2015 | Crack detection in ceramic tiles | V-Band Imaging Radar (60 GHz) |
[69] | 2017 | Fluid level sensing | TI-IWR1443 (77 GHz) |
[77] | 2018 | Material classification | FMCW radr with Infineon’s DEMO-BGT60TR24 sensor (60 GHz) |
[78] | 2018 | Motion detection and intersection monitoring | IWR6843 60 GHz radar |
[79] | 2019 | Foam detection in chemical applications | IC with mmWave ssensor (80 GHz) |
[10] | 2020 | Obtaining the performance on detecting vibrational targets | FMCW 80 GHz sensor integrated on SiGe chip |
[80] | 2022 | Eavesdropping and spying on phone calls | TI-AWR1843BOOST (77 GHz) |
[70] | 2022 | Material identification | TI-IWR1443 FMCW (77 GHz–81 GHz) |
Reference | Year | Application | Radar Used |
---|---|---|---|
[81] | 2018 | Blood glucose level detection | FMCW-XENSIV (60 GHz) |
[85] | 2019 | Multiple patients behavior detection | TI-AWR1642BOOST (77 GHz) |
[90] | 2020 | Skin cancer detection | Designed sensor (77 GHz) |
[87] | 2021 | Contactless fitness tracking | TI-IWR1642 (77 GHz–81 GHz) |
[82] | 2022 | Contactless monitoring of patients and elderly people alone | IWR6843AOPEVM (60 GHz–64 GHz) |
[92] | 2022 | Measuring systolic blood pressure | TI-IWR6843AOP (60 GHz–64 GHz) |
[93] | 2022 | Vital sign measuring | TI-IWR1443 (77 GHz–81 GHz) |
[94] | 2022 | Health monitoring with posture estimation | TI-IWR6843 (60 GHz–64 GHz) |
[95] | 2022 | Blood pressure monitoring | TI-AWR1843 (77 GHz–81 GHz) |
[96] | 2022 | Cardiorespiratory rate monitoring | Commercial FMCW (122 GHz) |
[97] | 2022 | Galvanic skin test to assess mental acuity and stress levels | TI-AWR1843 (77 GHz) |
[98] | 2022 | Automated heart rate and breathing rate monitoring | TI-AWR1443BOOST (77 GHz) |
Reference | Year | Application | Radar Used |
---|---|---|---|
[102] | 2019 | Intelligent robot for transparent object sensing | IWR6843 (60 GHz) |
[103] | 2020 | Robot-mounted mmWave radar for tracking heart rate | IWR6843 (62 GHz) |
[54] | 2020 | Predicting autonomous robot navigation | FMCW (77 GHz) |
[101] | 2020 | Collision detection and avoidance | IWR6843 (60 GHz) |
[104] | 2020 | mmWave radars as safe guard robots | IWR6843 (60 GHz) |
[100] | 2021 | Automated indoor navigation and path tracking | AWR6843 (77 GHz) |
[99] | 2021 | Glass wall and partition detection | IWR1443BOOSTEVM (77 GHz) |
Reference | Year | Application | Radar Used |
---|---|---|---|
[114] | 2006 | Power line prediction in helicopter rescue | mmWave radar (94 GHz) |
[115] | 2008 | mmWave radars for safe helicopter landing | Radar module with 94 GHz |
[116] | 2010 | Providing indoor security of short range | mmWave SAR (77 GHz) |
[117] | 2010 | Debris detection on airport runways | mmWave radar (73 GHz–80 GHz) |
[118] | 2013 | Concealed threat detection | W-band (75 GHz–110 GHz) |
[108] | 2015 | Surveillance imaging applications | MIRANDA radar (35 GHz and 94 GHz) |
[113] | 2018 | Traffic monitoring | IWR1642EVM 77 GHz radar |
[119] | 2019 | Human target detection, classification, tracking | ISM band (24 GHz MIMIC) |
[120] | 2020 | Tracking of malicious and hidden drones | mmWave (77 GHz) |
[121] | 2021 | Ego-motion estimating in indoor environments | TI-AWR1843BOOST (76 GHz–81 GHz) |
[2] | 2021 | Unmanned aircraft system detection and localization | AWR1843 Boost (76 GHz–81 GHz) |
[106] | 2021 | Aerial vehicle locating and air traffic management | AWR1843 (76 GHz–79 GHz) |
[122] | 2021 | 3D human skeletal pose estimation | TI-AWR1843 (77 GHz) |
[123] | 2023 | Indoor positioning system | IWR6843ISK (60 GHz–64 GHz) |
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Soumya, A.; Krishna Mohan, C.; Cenkeramaddi, L.R. Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review. Sensors 2023, 23, 8901. https://doi.org/10.3390/s23218901
Soumya A, Krishna Mohan C, Cenkeramaddi LR. Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review. Sensors. 2023; 23(21):8901. https://doi.org/10.3390/s23218901
Chicago/Turabian StyleSoumya, A., C. Krishna Mohan, and Linga Reddy Cenkeramaddi. 2023. "Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review" Sensors 23, no. 21: 8901. https://doi.org/10.3390/s23218901
APA StyleSoumya, A., Krishna Mohan, C., & Cenkeramaddi, L. R. (2023). Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review. Sensors, 23(21), 8901. https://doi.org/10.3390/s23218901