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I am kind of overwhelmed with the amount of models there are so finding the one that best suits my dataset is proving kind of difficult. The Dataset I have is as follows enter image description here

, its produced by a Radar, which outputs a row of values for a signal that it detects for a target.

enter image description here

Plotting across the row gives me the following wave, and as we go below the rows, we get the translation in the x-axis suggesting movement of the target, my dataset that I want to feed the model will have the following features: The average value of the two peaks for signal strength, the x-axis average value (multiplied by .77 meters), this will be the case for all of the waves for each target as each # of target will have these waves associated with it, tracking the movement of these waves shows the distance change, the change in amplitude and so on

enter image description here

Shows the translation of x-axis showing movement

I am currently working on a script that will try and get all the times when the radar detects something and get the amplitude column values and fill in the dataset, if I can't get the script to work I will just do it manually:

Link to what the Radar outputs

https://drive.google.com/file/d/1IJOebiXuScjLPytemulcXph7ZB1X65wU/view

The model that I will use will be different than what the radar outputs This is how I want to set the dataset for the model

enter image description here

Reference images to what I referred

https://i.stack.imgur.com/hZ1gp.jpg

I might also add another column that gets the average velocity of those points since we have time, distance given by the Radar. I will have a similar dataset for pedestrians as well, the first dataset was for vehicles, but there will be one for pedestrians as well, I want a model that can predict, once the training is done if the target was either a pedestrian or a vehicle given the features. What model would best fit this sort of data, a top 5 list would be super appreciated!

Thank You

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1 Answer 1

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From the data I suppose this is a timeseries problem and assuming you have continuous data, "since its a radar"

Generally the most used "best" approach is using stacked Long-Short Term Memory "LSTM" or some of its variants like GRU etc.. if you want to predict some specific cell in the future "e.g. row-column".. This might help get you started: https://keras.io/examples/timeseries/

If you are trying to predict the whole row, I would suggest using LSTM/variants AutoEncoders that would "supposedly" give you a better result in that case Thats also an introduction to that approach: https://towardsdatascience.com/step-by-step-understanding-lstm-autoencoder-layers-ffab055b6352

If your data is not continuous, I would suggest to use Convolutional Neural Networks "CNN" -AutoEncoders- to extract the features then feeding that to some fully connected layers to either outputs some categorization "e.g. distance from 1-5 or 5-10" or even the actual distance, but an important remark, this approach needs a lot of data points. it can be something like this: https://keras.io/examples/audio/

There are some research papers suggesting to convert your data points into 2D using fft or continuous wavelets then feeding that to a CNN to extract features from, but I haven't got any good results from that in different sets of problems.

P.S. Scaling the data beforehand "e.g. between the range of [0,1]" is essential in this case because apparently you have different units for each column.

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  • $\begingroup$ Thank you very much for responding, I forgot to add a main thing, I will have a similar dataset for pedestrians as well, the first dataset was for vehicles, but there will be one for pedestrians as well, I want a model that can predict, once the training is done if the target was either a pedestrian or a vehicle given the features. will this work for that as well? $\endgroup$ Apr 11, 2022 at 2:37
  • $\begingroup$ so what you are describing is a binary classification problem, what you should do is combine both datasets into 1 and add another column for the labels thats either 0 for pedestrians and 1 for vehicles or vice versa.. and using a cnn would be more appropriate and faster in training "supposedly". $\endgroup$ Apr 12, 2022 at 3:12

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