sdr rangel

C知道：SDR（Software Defined Radio）是一种通过软件定义的方式来实现无线通信系统的技术。它将传统硬件电路中的一部分功能移至软件中，使得无线通信系统的灵活性和可配置性大大增强。

SDR范围是指SDR技术在通信领域的应用范围。由于SDR的灵活性，它可以应用于各种不同的通信系统，包括无线电广播、移动通信、卫星通信、无线局域网等。SDR还能够适应不同频段和不同标准的通信系统，因此在未来的通信发展中具有重要的作用。

SDR的出现使得无线通信系统更易于开发和部署，同时也提供了更好的互操作性和适应性。它可以通过软件更新来支持新的通信标准和功能，避免了传统硬件设备需要替换的问题。SDR技术的发展将进一步推动通信领域的创新和进步。

相关问题

rtl-sdr*ads-b*matlab

RTL-SDR refers to a popular software-defined radio (SDR) device that allows you to receive and decode a wide range of radio signals. ADS-B is a type of radio signal used by aircraft to broadcast their position, speed, and other information. MATLAB is a programming language and development environment that is often used for signal processing and data analysis.

Together, RTL-SDR, ADS-B, and MATLAB can be used to receive and process ADS-B signals from aircraft. This can be useful for tracking aircraft in real-time or analyzing flight patterns over time. To get started, you would need an RTL-SDR device, software to receive and decode ADS-B signals (such as dump1090), and MATLAB to process the data. There are many tutorials and resources available online that can guide you through the process.

在下面代码中加入接收端的收到的复信号的时域图，再加一个隔直流的代码，用python import time import adi import matplotlib.pyplot as plt import numpy as np from scipy import signal # Create radio sdr = adi.Pluto() # Configure properties sdr.rx_rf_bandwidth = 4000000 sdr.rx_lo = 1900000000 sdr.tx_lo = 2000000000 sdr.tx_cyclic_buffer = True sdr.tx_hardwaregain_chan0 = -30 sdr.gain_control_mode_chan0 = "slow_attack" # Read properties print("RX LO %s" % (sdr.rx_lo)) print(sdr.sample_rate) # Create a sinewave waveform fs = int(sdr.sample_rate) N = 1024 fc = int(3000000 / (fs / N)) * (fs / N) ts = 1 / float(fs) t = np.arange(0, N * ts, ts) i = np.cos(2 * np.pi * t * fc) * 2 ** 14 q = np.sin(2 * np.pi * t * fc) * 2 ** 14 i=i+2**15 q=q+2**15 iq = i + 1j * q # Send data sdr.tx(iq) # Collect data for r in range(20): x = sdr.rx() print(x) y=abs(x) print(y) print('------------------------') plt.figure(0) plt.plot(y) f, Pxx_den = signal.periodogram(x, fs) #plt.clf() # plt.figure(1) plt.semilogy(f, Pxx_den) plt.ylim([1e-7, 1e4]) plt.xlabel("frequency [Hz]") plt.ylabel("PSD [V**2/Hz]") plt.draw() plt.pause(0.05) time.sleep(0.1) plt.show()

以下是加入接收端的收到的复信号的时域图和隔直流的代码的修改后的完整代码：

import time
import adi
import matplotlib.pyplot as plt
import numpy as np
from scipy import signal

# Create radio
sdr = adi.Pluto()

# Configure properties
sdr.rx_rf_bandwidth = 4000000
sdr.rx_lo = 1900000000
sdr.tx_lo = 2000000000
sdr.tx_cyclic_buffer = True
sdr.tx_hardwaregain_chan0 = -30
sdr.gain_control_mode_chan0 = "slow_attack"

# Read properties
print("RX LO %s" % (sdr.rx_lo))
print(sdr.sample_rate)

# Create a sinewave waveform
fs = int(sdr.sample_rate)
N = 1024
fc = int(3000000 / (fs / N)) * (fs / N)
ts = 1 / float(fs)
t = np.arange(0, N * ts, ts)
i = np.cos(2 * np.pi * t * fc) * 2 ** 14
q = np.sin(2 * np.pi * t * fc) * 2 ** 14
i = i + 2 ** 15
q = q + 2 ** 15
iq = i + 1j * q

# Send data
sdr.tx(iq)

# Collect data
for r in range(20):
    x = sdr.rx()
    print(x)
    y = abs(x)
    y_dc = y - np.mean(y)  # remove DC component
    print(y_dc)
    print('------------------------')
    plt.figure(0)
    plt.subplot(211)
    plt.plot(y_dc)
    plt.xlabel("Time [samples]")
    plt.ylabel("Amplitude")
    plt.subplot(212)
    f, Pxx_den = signal.periodogram(x, fs)
    plt.semilogy(f, Pxx_den)
    plt.ylim([1e-7, 1e4])
    plt.xlabel("Frequency [Hz]")
    plt.ylabel("PSD [V**2/Hz]")
    plt.draw()
    plt.pause(0.05)
    time.sleep(0.1)

plt.show()

在这个代码中，我们加入了一个隔直流的代码，用于移除接收到的信号的DC分量。这个代码为：

y_dc = y - np.mean(y)  # remove DC component

我们在时域图上画出了处理后的信号，并在频域图上画出了信号的功率谱密度（PSD）。

注意：这个代码只能在连接了PlutoSDR设备并安装了必要的包后才能运行。