{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "3cf2f4d2",
      "metadata": {
        "id": "3cf2f4d2"
      },
      "source": [
        "# FMCW-Radar-107_Micro-Doppler\n",
        "\n",
        "You can open this workbook in Google Colab to experiment with mmWrt\n",
        "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/matt-chv/mmWrt/blob/main/docs/micro_doppler.ipynb)\n",
        "\n",
        "Below is an intro to mmWrt for simple micro doppler estimation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4c4a7905",
      "metadata": {
        "id": "4c4a7905",
        "outputId": "582ed468-5a65-4516-b5a8-582a6e92f9dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "running from git folder, using local path (latest) mmWrt code c:\\git\\mmWrt\n",
            "version: 0.0.8-pre.1\n",
            "last run on  2024-10-27 16:10:25.819591\n"
          ]
        }
      ],
      "source": [
        "# Install a pip package in the current Jupyter kernel\n",
        "import sys\n",
        "from os.path import abspath, basename, join, pardir\n",
        "import datetime\n",
        "\n",
        "# hack to handle if running from git cloned folder or stand alone (like Google Colab)\n",
        "cw = basename(abspath(join(\".\")))\n",
        "dp = abspath(join(\".\",pardir))\n",
        "if cw==\"docs\" and basename(dp) == \"mmWrt\":\n",
        "    # running from cloned folder\n",
        "    print(\"running from git folder, using local path (latest) mmWrt code\", dp)\n",
        "    sys.path.insert(0, dp)\n",
        "else:\n",
        "    print(\"running standalone, need to ensure mmWrt is installed\")\n",
        "    !{sys.executable} -m pip install mmWrt\n",
        "\n",
        "from os.path import abspath, join, pardir\n",
        "import sys\n",
        "from numpy import arange, array, expand_dims, pi, sin, where, zeros\n",
        "from numpy import complex_ as complex\n",
        "from numpy.fft import fft, fftshift, fft2, fftshift, fftfreq\n",
        "from scipy.signal import find_peaks, stft\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy import arange, cos, sin, pi, zeros\n",
        "\n",
        "from mmWrt.Scene import Antenna, Medium\n",
        "from mmWrt.Raytracing import rt_points  # noqa: E402\n",
        "from mmWrt.Scene import Radar, Transmitter, Receiver, Target  # noqa: E402\n",
        "from mmWrt import RadarSignalProcessing as rsp  # noqa: E402\n",
        "\n",
        "from mmWrt.Raytracing import rt_points\n",
        "from mmWrt import __version__\n",
        "print(\"version:\", __version__)\n",
        "\n",
        "print(\"last run on \", datetime.datetime.now())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c727bbdb",
      "metadata": {
        "id": "c727bbdb"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.cm as cm\n",
        "from matplotlib import colors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1cb956b7-d8d6-4c0a-82a9-0baf79772f79",
      "metadata": {
        "id": "1cb956b7-d8d6-4c0a-82a9-0baf79772f79",
        "outputId": "8d6e06c0-1c77-45b8-ae4e-cb296ba8df0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "opt = {\"compute\":True, \"Dres_min\":50, \"NC\":32, \"NA\":64, \"BW\":0.01e9, \"k\":10e12, \"fs\":100e6, \"f0_min\":60e9, \"NF\":256,\n",
        "        \"d0\": 300, \"v0\": 160,\n",
        "        \"TIC\":1.2e-6, \"TIF\": 1.2e-3,\n",
        "        \"logger\": \"TBD\",\n",
        "        \"xt1\": \"lambda t: d0 + t*v0\", \"xt\": \"lambda t: 4*A0*sin(2*pi*f1*t)+d0\"}\n",
        "\n",
        "d0, v0 = opt[\"d0\"], opt[\"v0\"]\n",
        "NC = opt[\"NC\"]\n",
        "NA = opt[\"NA\"]\n",
        "NF = opt[\"NF\"]\n",
        "BW = opt[\"BW\"]\n",
        "k = opt[\"k\"]\n",
        "fs = opt[\"fs\"]\n",
        "f0_min=opt[\"f0_min\"]\n",
        "TIF = opt[\"TIF\"]\n",
        "TIC = opt[\"TIC\"]\n",
        "F1 = 6\n",
        "f1 = F1/(NF*TIF)\n",
        "A0 = v0/(2*pi*f1)\n",
        "\n",
        "xt = eval(opt[\"xt\"])\n",
        "\n",
        "radar = Radar(transmitter=Transmitter(bw=BW, slope=k,\n",
        "                                      f0_min=f0_min,\n",
        "                                      t_inter_chirp=TIC,\n",
        "                                      t_inter_frame=TIF,\n",
        "                                      frames_count=NF,\n",
        "                                      chirps_count=NC),\n",
        "              receiver=Receiver(fs=fs, max_adc_buffer_size=1024,\n",
        "                                max_fs=110e6,\n",
        "                                n_adc = NA),\n",
        "             debug=False)\n",
        "\n",
        "\n",
        "target = Target(xt=lambda t: xt(t))\n",
        "\n",
        "targets = [target]\n",
        "\n",
        "bb = rt_points(radar, targets, datatype=complex, raytracing_opt=opt,\n",
        "               debug=False)\n",
        "\n",
        "udops1 = zeros((NC, NF))\n",
        "tx_i, rx_i = 0, 0\n",
        "\n",
        "for frame_idx in range(NF):\n",
        "    # compute range doppler\n",
        "    cube = bb[\"adc_cube\"][frame_idx,:,tx_i,rx_i,:]\n",
        "    Z_fft2 = abs(fftshift(fft2(cube)))\n",
        "    # find peak in range\n",
        "    pk0 = find_peaks(abs(fft(cube[0,:])))[0][0]\n",
        "    pk1 = find_peaks(abs(fft(cube[-1,:])))[0][0]\n",
        "    # non regression hook\n",
        "    assert pk0 == pk1\n",
        "    # append doppler bin at peak range\n",
        "    udops1[:,frame_idx] = Z_fft2[:, pk0]\n",
        "dop_idx = find_peaks(abs(Z_fft2[:, pk0]))[0][0]\n",
        "# non regression hook\n",
        "assert dop_idx == 26\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "fig, [ax0, ax1] = plt.subplots(nrows=2)\n",
        "ax0.imshow(abs(Z_fft2))\n",
        "ax0.set_title(f\"Range-Doppler for frame: {frame_idx}\")\n",
        "ax0.set_xlabel(\"Range idx\")\n",
        "ax0.set_ylabel(\"Dop idx\")\n",
        "ax1.set_title(\"single target\")\n",
        "ax1.imshow(udops1, aspect='auto')\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f180a453-c387-4178-a422-1fb7ea0ddc76",
      "metadata": {
        "id": "f180a453-c387-4178-a422-1fb7ea0ddc76"
      },
      "source": [
        "## 2 Targets 180 degrees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7e87a4f-6be6-4752-9a32-cc9f3c2feee0",
      "metadata": {
        "id": "c7e87a4f-6be6-4752-9a32-cc9f3c2feee0",
        "outputId": "4143d718-b974-42fc-e578-d3e8b2eed0d6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x1cab0189f10>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "target_180 = Target(xt=lambda t: xt(t+1/2*1/f1))\n",
        "bb = rt_points(radar, [target, target_180], datatype=complex, raytracing_opt=opt)\n",
        "\n",
        "udops = zeros((NC, NF))\n",
        "pk0 = None\n",
        "tx_i, rx_i = 0, 0\n",
        "\n",
        "for frame_idx in range(NF):\n",
        "    # compute range doppler\n",
        "    cube = bb[\"adc_cube\"][frame_idx,:,tx_i,rx_i,:]\n",
        "    Z_fft2 = abs(fftshift(fft2(cube)))\n",
        "    # find peak in range\n",
        "    pk = find_peaks(abs(fft(cube[0,:])))[0][0]\n",
        "    # append doppler bin at peak range\n",
        "    udops[:,frame_idx] = Z_fft2[:, pk]\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.title(\"2 targets 180 degrees apart\")\n",
        "plt.imshow(udops, aspect='auto')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2e60b332-c3ef-4083-a3e1-6b057be0e941",
      "metadata": {
        "id": "2e60b332-c3ef-4083-a3e1-6b057be0e941"
      },
      "source": [
        "## 3 Targets each 120 degrees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f4b0e764-6964-4b21-9530-2cd4d3c390c6",
      "metadata": {
        "id": "f4b0e764-6964-4b21-9530-2cd4d3c390c6",
        "outputId": "871c0c68-eaf7-44fe-cb9d-ceddbaef1da2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x1cab2d32bd0>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "target_120 = Target(xt=lambda t: xt(t+1/3*1/f1))\n",
        "target_240 = Target(xt=lambda t: xt(t+2/3*1/f1))\n",
        "bb = rt_points(radar, [target, target_120, target_240], datatype=complex, raytracing_opt=opt)\n",
        "\n",
        "udops = zeros((NC, NF))\n",
        "pk0 = None\n",
        "tx_i, rx_i = 0, 0\n",
        "\n",
        "for frame_idx in range(NF):\n",
        "    # compute range doppler\n",
        "    cube = bb[\"adc_cube\"][frame_idx,:,tx_i,rx_i,:]\n",
        "    Z_fft2 = abs(fftshift(fft2(cube)))\n",
        "    # find peak in range\n",
        "    pk = find_peaks(abs(fft(cube[0,:])))[0][0]\n",
        "    # append doppler bin at peak range\n",
        "    udops[:,frame_idx] = Z_fft2[:, pk]\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.title(\"3 targets 120 degrees apart\")\n",
        "plt.imshow(udops, aspect='auto')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4d1a9bb-bb68-4cc0-8b70-c9bd46a5174b",
      "metadata": {
        "id": "b4d1a9bb-bb68-4cc0-8b70-c9bd46a5174b"
      },
      "source": [
        "## UDOP w/ STFT\n",
        "\n",
        "STFT can be used to compute $\\mu$-Doppler when there is only one frame available.\n",
        "\n",
        "Below also shows when one target has just a linear speed and second target is oscillating around the first one."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a2083ffb-9a7c-4fff-a6a7-164a1c4dff50",
      "metadata": {
        "id": "a2083ffb-9a7c-4fff-a6a7-164a1c4dff50",
        "outputId": "32262d1d-f4a1-41c3-9e0c-8b8f51cd7764"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x1cab4be4cd0>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from scipy.fft import fft, fft2\n",
        "from numpy import angle, cos, real\n",
        "from scipy.signal import find_peaks\n",
        "from scipy.signal import stft\n",
        "\n",
        "opt = {\"compute\":True, \"Dres_min\":50}\n",
        "debug_ON = False\n",
        "\n",
        "# chirp start freq\n",
        "f0m = 60e9\n",
        "# chirp bandwidth\n",
        "bw = 1e6\n",
        "# chirp slope\n",
        "k=800e6\n",
        "# t_inter_chirp\n",
        "t_ic = 2e-3 # 1/2e5\n",
        "# chirp count\n",
        "NC=4096\n",
        "# sampling frequency\n",
        "fs_if = 20e3\n",
        "x0, y0, z0 = 500, 0, 500\n",
        "\n",
        "radar = Radar(transmitter=Transmitter(bw=bw, slope=k,\n",
        "                                      f0_min=f0m,\n",
        "                                      t_inter_chirp=t_ic,\n",
        "                                      chirps_count=NC),\n",
        "              receiver=Receiver(fs=fs_if, max_adc_buffer_size=60000,\n",
        "                                debug=debug_ON),\n",
        "              debug=debug_ON)\n",
        "\n",
        "xt0 = lambda t: v0*t\n",
        "xt1 = lambda t: xt0(t) + 1*sin(2*pi*f1*t)\n",
        "\n",
        "# Targets position and speed\n",
        "xt0 = lambda t: x0+10*t\n",
        "# 3 rps\n",
        "f1 = 0.5\n",
        "xt1 = lambda t: xt0(t) + 0.1*sin(2*pi*f1*t)\n",
        "target0 = Target(x0, 0, z0, xt=lambda t: xt0(t))\n",
        "target1 = Target(x0, 0, z0, xt=lambda t: xt1(t))\n",
        "\n",
        "bb = rt_points(radar, targets=[target0, target1], debug=debug_ON, raytracing_opt=opt, datatype=complex)\n",
        "# take the first frame (since only one anyway)\n",
        "cube = bb[\"adc_cube\"][0,:,0,0,:]\n",
        "# find the range bin where targets are\n",
        "range_fft = fft(cube)\n",
        "peaks, _ = find_peaks(abs(range_fft[0]))  # , height=8)\n",
        "\n",
        "p0 = peaks[0]\n",
        "range_bin = range_fft[:,p0]\n",
        "seg_n = NC//64\n",
        "_,_,B = stft(range_bin, nperseg=seg_n, return_onesided=False)\n",
        "C = abs(fftshift(B, axes=0))\n",
        "C = C[:,]\n",
        "plt.title(\"STFT $\\mu$-Doppler spectrogram\")\n",
        "plt.imshow(C[:,:])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3347c0a5-ecc1-42e0-b74b-9eed0957bea5",
      "metadata": {
        "id": "3347c0a5-ecc1-42e0-b74b-9eed0957bea5"
      },
      "source": [
        "### Retrieve oscillation data from uDOP\n",
        "\n",
        "Simples logic: get the doppler peak location, get the FFT index of those and compare to the setting used to create micro-doppler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f1f99da8-9a53-4595-ae82-3f2d247c0c41",
      "metadata": {
        "id": "f1f99da8-9a53-4595-ae82-3f2d247c0c41"
      },
      "outputs": [],
      "source": [
        "NC1, NF1 = udops1.shape\n",
        "\n",
        "dops = []\n",
        "for frame_idx in range(NF1):\n",
        "    dop = find_peaks(udops1[:, frame_idx])[0][0]\n",
        "    dops.append(dop)\n",
        "Y = fft(dops)\n",
        "ym = find_peaks(Y)[0][0]\n",
        "# non regression hook:\n",
        "# verify that the frequency measured is the same\n",
        "# as setup at beginning of simulation\n",
        "assert ym == F1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a38218d8-3d2f-4be1-a51c-e106eff78537",
      "metadata": {
        "id": "a38218d8-3d2f-4be1-a51c-e106eff78537",
        "outputId": "dc8d41e0-f6d8-4b88-a8fd-6d06fbf55887"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "last successful run on \n",
            "2024-10-27 16:13:46.384196\n"
          ]
        }
      ],
      "source": [
        "print(\"last successful run on \")\n",
        "print(datetime.datetime.now())"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.1"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}