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mmWave Vehicle Occupancy Detection EVM Kit (BM201-VOD) (Worldwide Shipping)
$ 184.27
- Description
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Description
Immediately Ship -Worldwide Shipping AvailablemmWave Sensor Evaluation Solution
Batman BM201-VOD mmWave EVM Kit
mmWave Vehicle Occupancy Detection (VOD)
●
Vehicle Occupancy Detection (VOD)
For plotting a Range-Azimuth-Heatmap with a 64 x 48 Grid Matrix covering: Range of 3 meter / 64 row (approx. 0.047 meter per row) x Azimuth of 108 degree / 48 column (approx. 2.3 degree /column). Subsequently a programmer may write code to group the Grid(s) into Zone(s) for detecting whether the particular Zone(s) is occupied by Target(s); suitable for vehicle occupancy detection or for occupancy detection for an area of around 3 meter x 3 meter.
Attention:
● Batman BM201 mmWave EVM Kit supports Raspberry Pi4 (not for versions below) and NVIDIA Jetson Nano
● Raspberry Pi4 and / or NVIDIA Jetson Nano not included within this EVM Kit (must be purchased separately).
Please check their respective websites for purchasing info
● Make sure you are using the correct power supply of 5 V, >2.0 A with a Micro USB connection
Python SDK
Available on GitHub
/bigheadG/mmWave
(Note: Please refer to README.md file first for proper configuration)
mmWave Solution bridges Hardware & Software World together with Simplicity
Joybien Batman BM201 mmWave EVM Kit is a Texas Instruments (TI) IWR6843 ASIC based millimeter-wave (mmWave) Kit with Frequency-Modulated Continuous Wave (FMCW) radar technology capable of operation in the 60GHz to 64GHz band with up to 4 GHz continuous chirp, using 3 Transmission Antennas and 4 Receiving Antennas, for sensing target object’s range, velocity, and angle parameters.
Batman BM201 mmWave EVM Kit is with a small and compact mmWave Module (with low-power, self-monitored, ultra-accurate, and lighting condition independent versatilities), along with a Pi-Hat Board for simple and direct connectivity to a Raspberry Pi or NVIDIA Jetson Nano computer, suitable for various applications including: Education, Engineering, Science, Industrial, Medical, and Business & Consumer.
Applications
● Education’s Practical Radar Introduction
● Engineering & Science’s Motion Detection, Displacement, etc.
● Industrial sensor for Displacement & Safe Guard, Factory Automation, Robotics, etc.
● Building Automation sensor for Occupancy Detection, Proximity & Position sensing, People Counting, Security and Surveillance
● Business’ Traffic Monitoring, and Proximity Advertisement
*** Specifications subject to change without notice
Features
Operating Frequency
60GHz ~ 64GHz coverage
with 4GHz continuous bandwidth
Antenna
3 Tx and 4 Rx Antennas on Module, with:
TX Power: 10 dBm
RX Noise Figure: 14 dB
Processors
ARM R4F based MCU and C674x DSP
for advanced signal processing
On-Chip Memory
1.75MB
•Internal Memories
ECC
•Input Power
3.3Vdc, 2.1A
Specifications mmWave Sensor Evaluation Module
mmWave ASIC
TI IWR6843 Single Chip mmWave Sensor
FMCW Transceiver
Integrated PLL, Transmitter, Receiver, Baseband, and A2D
60GHz to 64GHz Coverage With 4GHz Continuous Bandwidth
Four Receive Channels
Three Transmit Channels
Ultra-Accurate Chirp Engine Based on Fractional-N PLL
TX Power: 10 dBm
RX Noise Figure: 14 dB
Phase Noise at 1 MHz: –92 dBc/Hz
Antenna Type : ISK Antenna
Built-in Calibration and Self-Test (Monitoring)
ARM® Cortex® -R4F-Based Radio Control System
Built-in Firmware (ROM)
Self-calibrating System Across Frequency and Temperature
DSP
C674x DSP for Advanced Signal Processing
On-Chip Memory
1.75MB
MCU
ARM R4F Microcontroller for Object Detection, and Interface Control
Joybien mmWave Protocol (Per configuration)
I/O
Up to 6 ADC Channels (low sample rate monitoring)
Up to 2 SPI Ports
Up to 2 UARTs
I2C – GPIOs
Power Management
Built-in LDO Network for Enhanced PSRR
I/Os Support Dual Voltage 3.3 V/1.8 V
Clock Source
40MHz
Antenna Orientation
4 receive(RX) 3 transmit (TX) antenna with 108° azimuth field of view (FoV) and 44° elevation FoV
Input Power
3.3VDC, 2.1A source
Operating Temperature
& Humidity
0° to 40° degree Celsius
10 ~ 85% Non-Condensing
Dimensions & Weight
67mm x 46mm x 2mm ; 15 grams net
Raspberry Pi-Hat Board /Jetson Nano carrier board
Connector
Matching mmWave Module Female Connector
Matching Raspberry Pi GPIO Female Connector
Micro USB Power Connector
Jumpers for Bluetooth Tx/Rx or Raspberry Pi Tx/Rx Selection
Jumper for mmWave Raw Data or Key Data Selection
Bluetooth (optional)
Joybien JBT24M Bluetooth Low Energy Module
Micro USB Input Power
5VDC, 2Amp.
(Note: Power Adapter and Micro USB Cable NOT included)
Operating Temperature
Operating Humidity
0° to 40° degree Celsius
10 ~ 85% Non-Condensing
Dimensions & Weight
65.3mm x 56.3mm
30 grams with JBT24M Bluetooth
Python SDK
Python SDK
Available on GitHub
Note: Please refer to README.md file first for proper configuration
/bigheadG/mmWave
(BM201-VOD)
Vehicle Occupancy Detection
/bigheadG/mmWave/tree/master/VOD
(BM201-LPD)
Long-Range People Detection
/bigheadG/mmWave/tree/master/LPD
(BM201-PC3)
People Counting & Detection
/bigheadG/mmWave/tree/master/PC3
(BM201-TMD)
Traffic Monitoring Detection
/bigheadG/mmWave/tree/master/TMD
(BM201-VSD)
Vital Signs Detection
/bigheadG/mmWave/tree/master/VSD
(BM201-HAM)
High Accuracy Measurement
/bigheadG/mmWave/tree/master/HAM
(BM201-DRN)
Drone Radar Navigation
/bigheadG/mmWave/tree/master/DRN
(BM201-FDS)
Fall Detection Sensing
Python SDK upon purchasing BM201-FDS EVM Kit via email
.
Appendix: Joybien mmWave EVM Kit Application Solution Selection
(BM201-VOD)
Zone Occupancy Detection
For plotting a Range-Azimuth-Heatmap with a 64 x 48 Grid Matrix covering: Range of 3 meter / 64 row (approx. 0.047 meter per row) x Azimuth of 108 degree / 48 column (approx. 2.3 degree /column). Subsequently a programmer may write code to group the Grid(s) into Zone(s) for detecting whether the particular Zone(s) is occupied by Target(s); suitable for vehicle occupancy detection or for occupancy detection for an area of around 3 meter x 3 meter.
(BM201-LPD)
Long-Range People Detection
For a contactless and wearableless Long-Range People Detection (LPD) of 1 meter ~ 50 meters (about 3 ~ 164 feet), for various applications that require people sensing or counting without privacy invasion.
(BM201-PC3)
People Counting & Detection
For a wireless People Counting & Detection in 6 x 6 meter or 36 square meter area (or about 387.5 square feet), for various applications that require people sensing, people counting, or people occupancy density estimation without privacy invasion.
(BM201-TMD)
Traffic Monitoring Detection
For detecting moving objects (such as vehicles) in 5m ~ 50m with FOV of approx. +/- 54 degrees with Position X&Y, Velocity X&Y info. And based on the detected data, a programmer may write a program to define virtual Zones, for mapping objects (vehicles) moving in and out of certain Zones for traffic monitoring applications.
(BM201-VSD)
Vital Signs Detection
For a contactless and wearableless human Vital Signs Detection (VSD) with real-time Heartbeat Rate & Respiration Rate data, for range of 30cm ~ 90cm (about 1~3 feet); along with Status Indicator for sensing the presence of a person, as well as the measurement stability, and whether the person is present but without Vital Signs.
(BM201-HAM)
High Accuracy Measurement
For a wireless High Accuracy Measurement (HAM) of an object distance with range of 30cm ~ 3 meters (about 1~10 feet), having millimeter measurement resolution.
(BM201-FDS)
Fall Detection Sensing
For wireless sensing of people-fall-detection along with people movement & tracking in 3-Dimensional region covering 6m x 6m area without privacy invasion. The sensed people behavior data are with Position X/Y/Z, Velocity X/Y/Z, and Acceleration X/Y/Z parameters suitable for people movement analysis such as standing, sitting, lying down or falling down positions.
Note:
NVIDIA logo, and Jetson Nano are trademarks and/or registered trademarks of NVIDIA Corporation.ducation’s Practical Radar Introduction
Raspberry Pi logo and Raspberry Pi 4 are trademarks and/or registered trademarks of Raspberry Pi Foundation.
"Python" is a registered trademark of the PSF.
This EVM Kit does not include Raspberry Pi computer, nor NVIDIA Jetson Nano computer.