我这里使用的设备为 IWR6843ISK+DCA1000EVM+mmWave Studio

参考资料：采用不同型号雷达有不同格式的数据规定，以官方提供的文档为准

Mmwave Radar Device ADC Raw Data Capture

原始数据介绍

1. 原始数据格式介绍：
   对于iwr6843isk和dca1000EVM设备，原始数据通过两个 LVDS lanes被捕获，Lanes1 存储两个采样点实部，Lanes2 存储两个采样点虚部，以non-interleaved的形式，储存在二进制文件中

• non-interleaved的形式，我的理解是，对于每一个chirp，会按照先存储RX0对于该chirp的所有采样点数，再存储RX1的所有采样点数，直到 RX channel都存储完毕，再去存储下一个chirp
• 由于只能使用两个LVDS lanes，因此iwr6843无法使用 3个RXchannel进行数据捕获，只能使用1，2，4个RXchannel捕获数据

2. 数据容量：
   计算公式：Total Size in Bytes = Num ADC Samples × Num RX Channels × Num Frames × Num Chirps × Num Bytes Per Sample
   对于复数的输出形式， Num Bytes Per Sample的值为4，2bytes存储实部，2bytes存储虚部
   例如：
   我这里采集的原始数据大小为4915200bytes
   
   参数配置为：

这里我启用了3个TX，分别发射3种chirp构成TDM-MIMO，故对于每一个RX接收到的chirp数应该是3*128
关于TDM-MIMO的详细信息可参考我的另一篇文章

IWR6843ISK+MMWAVE STUDIO 配置TDM-MIMO

计算可得 100*4*8*128*3*4 = 4915200 bytes 与实际相符

参考代码

matlab读取数据参考代码

%%% This script is used to read the binary file produced by the DCA1000 and Mmwave Studio
%%% Command to run in Matlab GUI -
readDCA1000('<ADC capture bin file>') function [retVal] = readDCA1000(fileName)
%% global variables
% change based on sensor config
numADCSamples = 256; % number of ADC samples per chirp
numADCBits = 16; % number of ADC bits per sample
numRX = 4; % number of receivers
numLanes = 2; % do not change. number of lanes is always 2
isReal = 0; % set to 1 if real only data, 0 if complex data0
%% read file
% read .bin file
fid = fopen(fileName,'r');
adcData = fread(fid, 'int16');
% if 12 or 14 bits ADC per sample compensate for sign extension
if numADCBits ~= 16
	l_max = 2^(numADCBits-1)-1;
	adcData(adcData > l_max) = adcData(adcData > l_max) - 2^numADCBits;
end
fclose(fid);
fileSize = size(adcData, 1);
% real data reshape, filesize = numADCSamples*numChirps
if isReal
	numChirps = fileSize/numADCSamples/numRX;
	LVDS = zeros(1, fileSize);
	%create column for each chirp
	LVDS = reshape(adcData, numADCSamples*numRX, numChirps);
	%each row is data from one chirp
	LVDS = LVDS.';
else
	% for complex data
	% filesize = 2 * numADCSamples*numChirps
	numChirps = fileSize/2/numADCSamples/numRX;
	LVDS = zeros(1, fileSize/2);
	%combine real and imaginary part into complex data
	%read in file: 2I is followed by 2Q
	counter = 1;
	for i=1:4:fileSize-1
		LVDS(1,counter) = adcData(i) + sqrt(-1)*adcData(i+2); 
		LVDS(1,counter+1) = adcData(i+1)+sqrt(-1)*adcData(i+3);
		counter = counter + 2;
	end
	% create column for each chirp
	LVDS = reshape(LVDS, numADCSamples*numRX, numChirps);
	%each row is data from one chirp
	LVDS = LVDS.';
end
%organize data per RX
adcData = zeros(numRX,numChirps*numADCSamples);
for row = 1:numRX
	for i = 1: numChirps
		adcData(row, (i-1)*numADCSamples+1:i*numADCSamples) = LVDS(i, (row-1)*numADCSamples+1:row*numADCSamples);
	end
end
% return receiver data
retVal = adcData;

python读取数据参考代码

这里我采用了openrader的开源库实现，代码更为简洁

参考资料：OpenRadar

import numpy as np
from mmwave.dataloader import DCA1000

# 雷达参数配置
# numStartFreq = 60
# numRampEndTime = 48.57
# numIdleTime = 7
numFrames = 100
numADCSamples = 119
# numSampleRate = 3000
# numFreqSlope = 74.98
numTxAntennas = 3
numRxAntennas = 4
numLoopsPerFrame = 88
numChirpsPerFrame = numTxAntennas * numLoopsPerFrame
# numRangeBins = numADCSamples
# numDopplerBins = numLoopsPerFrame
# numAngleBins = 64


# 原始数据路径
bin_path = 'D:\\Workspace_lab\\adc_data.bin'

adc_data = np.fromfile(bin_path, dtype=np.uint16)
adc_data = adc_data.reshape(numFrames, -1)
adc_data = np.apply_along_axis(DCA1000.organize, 1, adc_data, num_chirps=numChirpsPerFrame,
                                num_rx=numRxAntennas, num_samples=numADCSamples)

# 转换结果：（Frame, Chirp, numRx, ADCsample） 四维numpy数组
print(adc_data.shape)