IWR6843ISK: AreaScanner Demo

Hello Sander,

With the way mmWave Radar technology works, the more reflective an object is the easier it is the sensor to see. Because cars are shiny/reflective with a lot of metal while also being very dense, cars are actually much easier to be detected even with the same demo/tuning. However we have a demo which we provide in the Radar Toolbox on TI Resource Explorer that specifically explores detecting cars which is the Traffic Monitoring Demo. Details are in the link below.

https://dev.ti.com/tirex/explore/node?a=1AslXXD__1.00.01.07&node=A__AEELPhag5kwL6DM0T2rvog__radar_toolbox__1AslXXD__1.00.01.07

Best Regards,

Pedrhom

Hello,

Thanks for the fast reply!

With the project im working on i want to scan an area of around 20-30 meters for any cars AND persons and track them.
Later I also want to differntiatie a person with a vehicle using some neural networks. I was gone use the pointcloud data from the AreaScanner demo for that.

I dont think the traffic monitoring has that point cloud feature.

And is there such a big difference between two demos, can i not just change the antenna configuartion to get some additional distance?

With kind regards,
Sander

Hey there,

Thanks for helping me previously.
I am a lot further in the development and wat to tune my radar paramaters with the tool u mentioned. (https://dev.ti.com/gallery/view/mmwave/mmWaveSensingEstimator/ver/2.2.1/)

Ive put in the wanted parameters and it generates some usefull profile and framecfg commands.
But when I insert the commands in my cfg file my radar doesnt give any information anymore.

I also tuned the tracking layer and detection layer paramaters following the tuning guides.
Here is my .cfg file and a picture of the tool u mentioned:

For context, i have chosen to use the 3D people Counting binaries.
It uses the GTrack algorythm and produces a really dense point cloud, ideal for object classification in the long run.

% SDK Parameters
% See the SDK user's guide for more information
% "C:\ti\mmwave_sdk_[VER]\docs\mmwave_sdk_user_guide.pdf"
% Front- End paramaters 
% Generate front end parameters here: https://dev.ti.com/gallery/view/mmwave/mmWaveSensingEstimator/ver/2.2.2/

sensorStop
flushCfg
dfeDataOutputMode 1
channelCfg 15 7 0
adcCfg 2 1
adcbufCfg -1 0 1 1 1
lowPower 0 0

profileCfg 0 60 2 3.8 20.84 0 0 46.64 1 187 11660 0 0 30
chirpCfg 0 0 0 0 0 0 0 1
chirpCfg 1 1 0 0 0 0 0 2
chirpCfg 2 2 0 0 0 0 0 4
frameCfg 0 2 44 0 41.67 1 0

% Detection Layer Parameters
% See the Detection Layer Tuning Guide for more information
% "C:\ti\mmwave_industrial_toolbox_[VER]\labs\people_counting\docs\3D_people_counting_detection_layer_tuning_guide.pdf"
% Recommend the default values stated in the Detection Layer Tuning Guide

dynamicRACfarCfg -1 4 4 2 2 8 12 4 8 5.00 8.00 0.40 1 1
staticRACfarCfg -1 6 2 2 2 8 8 6 4 8.00 15.00 0.30 0 0
dynamicRangeAngleCfg -1 0.75 0.0010 1 0
dynamic2DAngleCfg -1 3 0.0300 1 0 1 0.30 0.85 8.00
staticRangeAngleCfg -1 0 8 2

% Board parameters for IWR6843ISK
antGeometry0 0 -1 -2 -3 -2 -3 -4 -5 -4 -5 -6 -7
antGeometry1 -1 -1 -1 -1 0 0 0 0 -1 -1 -1 -1
antPhaseRot 1 1 1 1 1 1 1 1 1 1 1 1
fovCfg -1 70.0 20.0
compRangeBiasAndRxChanPhase 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0

% Tracker Layer Parameters
% See the Tracking Layer Tuning Guide for more information
% "C:\ti\mmwave_industrial_toolbox_[VER]\labs\people_counting\docs\3D_people_counting_tracker_layer_tuning_guide.pdf"

% Most important to tune !

staticBoundaryBox -10 10 2 30 0 3
boundaryBox -10 10 2 30 0 3
presenceBoundaryBox -10 10 2 30 0 3

sensorPosition 2 0 15

gatingParam 3 2 2 3 4

stateParam 3 3 6 500 5 6000
allocationParam 40 100 0.1 20 0.5 20

maxAcceleration 0.1 0.1 0.1

trackingCfg 1 4 800 20 10 13 41 
sensorStart