{ "cells": [ { "cell_type": "markdown", "id": "33a79c4a", "metadata": {}, "source": [ "# Sub mm Precision\n", "\n", "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/matt-chv/mmWrt/blob/main/docs/Precision.ipynb)" ] }, { "cell_type": "markdown", "id": "916a046d", "metadata": {}, "source": [ "## The problem\n", "\n", "Multiple systems exhibit sub-mm precision (i.e. precision lower than 1 mm), yet the FMCW basics is that resolution is a function of the chirp bandwidth. \n", "\n", "Antenna designs often limits bandwidth to sub 10-GHz which effectively limits resolution to 3 cm.\n", "\n", "\n", "## The solution\n", "\n", "Instead of using the frequency of the FFT bin, use a frequency estimator with higher precision. While it will not be possible to resolve two targets with better than the FFT range resolution, it will be possible to increase precision of the estimated frequency.\n", "\n", "Small list of possible algorithms are:\n", "* simple FFT\n", "* FFT with zero padding\n", "* Quinn's second interpolation [1]\n", "* Phase based interpolation\n", "\n", "[1] Quinn, BG, \"Estimation of frequency, amplitude and phase from the DFT of a time series,\" IEEE Trans. Sig. Proc. Vol 45, No 3, Mar 1997, pp814-817." ] }, { "cell_type": "code", "execution_count": 1, "id": "0a0a3b6f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "running from git folder, using local path (latest) mmWrt code c:\\git\\mmWrt\n", "2024-05-23 14:08:44.193499\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", "print(datetime.datetime.now())" ] }, { "cell_type": "code", "execution_count": 2, "id": "5155877a", "metadata": {}, "outputs": [], "source": [ "from os.path import abspath, join, pardir\n", "import sys\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "from matplotlib import colors\n", "from numpy import arange, where, expand_dims\n", "\n", "# uncomment below if the notebook is launched from project's root folder\n", "# dp = abspath(join(\".\",pardir))\n", "# sys.path.insert(0, dp)\n", "\n", "\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" ] }, { "cell_type": "markdown", "id": "9c920e38", "metadata": {}, "source": [ "### Frequency Estimator for sub-mm precision\n", "\n", "Below is just an example with nominal values. For a more comprehensive analysis, noise sensitivity (phase noise in RX channel), channel noise and other aspects would need to be considered.\n", "\n", "For embedded systems, a zoom FFT, can be used to reduce the memory needed for the full FFT to be computed.\n", "\n", "Overview - Comparing \n", "\n", "> FFT estimator ($\\approx 9 mm$) \n", "\n", "vs \n", "\n", "> Quinn2 estimator ($\\approx 0.4 \\mu m$)\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "50afe306", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "radar Range resolution: 0.0375\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from numpy import linspace\n", "from numpy import complex_ as complex\n", "\n", "debug_ON = False\n", "c = 3e8\n", "test = 0\n", "fs = 10.24e3\n", "k = 50e8\n", "BW = 4e9\n", "radar = Radar(transmitter=Transmitter(bw=BW, slope=k, chirps_count=1),\n", " receiver=Receiver(fs=fs, max_adc_buffer_size=9000,\n", " debug=debug_ON), debug=debug_ON)\n", "delta_R = c/2/BW\n", "print(\"radar Range resolution:\", delta_R)\n", "range_bin_index = 100\n", "d_start = delta_R * (range_bin_index - 0.25)\n", "d_end = d_start + delta_R/2\n", "increments_count = 20\n", "ds = linspace(d_start, d_end, increments_count)\n", "\n", "\n", "# compare FFT to quinn2\n", "errors = []\n", "for est in [\"fft\", \"quinn2\"]:\n", " errors_in_mm = []\n", " deltas = []\n", " for idx, d1 in enumerate(ds):\n", " delta = idx/increments_count\n", " deltas.append(delta*100)\n", "\n", " targets = [Target(d1)]\n", " bb = rt_points(radar, targets, datatype=complex, debug=debug_ON)\n", "\n", " Distances, range_profile = rsp.range_fft(bb)\n", " ca_cfar = rsp.cfar_ca_1d(abs(range_profile))\n", "\n", " mag_r = abs(range_profile)\n", " mag_c = abs(ca_cfar)\n", " # little hack to remove small FFT ripples : mag_r> 10\n", " target_filter = ((mag_r > mag_c) & (mag_r > 20))\n", "\n", " index_peaks = where(target_filter)[0]\n", " # note: grouped_peaks only returns integer values\n", " # an improvement could be to return float values here with\n", " # simple interpolator\n", " grouped_peaks = rsp.peak_grouping_1d(index_peaks, mag_r)\n", " ipeaks = rsp.frequency_estimator(range_profile, grouped_peaks,\n", " estimator_name=est)\n", " f2d = rsp.if2d(radar)\n", " distances = f2d * (radar.fs* ipeaks/radar.n_adc)\n", " found_targets = [Target(d) for d in distances]\n", " error = rsp.error(targets, found_targets)\n", " if error>2:\n", " print(\"errr\",idx, error)\n", " else:\n", " pass\n", " errors_in_mm.append(error*1e3)\n", " max_error = max(errors_in_mm)\n", " errors.append(max_error)\n", " plt.plot(deltas, errors_in_mm)\n", " plt.title(f\"max error: {max_error:.2g}mm in one range bin for: {est}\")\n", " plt.xlabel('% of one range bin')\n", " plt.ylabel('rms error in mm')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "id": "19ec7fda-a558-4832-813f-250b6d98bc63", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9.375000000000355\n", "4.308655654483573e-07\n" ] } ], "source": [ "from numpy.testing import assert_almost_equal\n", "print(errors[0])\n", "print(errors[1])\n", "assert_almost_equal(errors[0]*1e-3, 9.375e-3)\n", "assert_almost_equal(errors[1]*1e-3, 4.309e-10)" ] }, { "cell_type": "code", "execution_count": null, "id": "d0ba2ff3-6d4f-4cea-a57f-53a7df6b00f8", "metadata": {}, "outputs": [], "source": [] } ], "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" } }, "nbformat": 4, "nbformat_minor": 5 }