GPS+LoRa运动轨迹遥测

 

GPS通常使用串口通信,而LoRa模块是一个远程无线串口,二者简单连接,就可以将火箭等飞行器参数包括飞行轨迹传回地面电脑,不仅可以用于事后数据分析,也能定位搜索飞行器降落地点,系统成本仅几十元,通讯距离4km(更远距离可选更大功率)。本文详细描述了系统构成、硬件选件、软件和关键参数设置,使普通爱好者可以采购模块通过简单组装,马上就能使用。

GPS可以选用深圳飞空科技的M10RC12,实测可以超过18Hz刷新,搜星能力也是不错的,通讯协议默认UBX,数据内容优于NMEA协议。LoRa我选用了大夏龙雀科技的DX-LR01-A(T0),这个套件带USB转TTL模块,套件的小弹簧天线增益低,实际使用的时候要改接大一点的天线,尤其是接收端。

1,测试GPS,先把GPS模块接TTL模块,需要4根线,VCC、GND、RX和TX,GPS模块的RX、TX与TTL模块的TX、RX接(交叉了一下),电源线认准了不要搞反,GPS模块的小线排肯定是直接插不上的,要改线。接好线后,TTL模块通过USB线插电脑。下载安装一个U-Center 8.20,打开软件,在通讯口port菜单选刚才插USB那个口,搞不清楚可以等选择口的时候插拔一下,然后波特率选115200。这时把GPS模块放到窗户外边就应该能收到信号,可以看到当地经纬度等信息。

2,调试LoRa,需要一对LoRa,各自插到一个TTL模块,然后USB线插到电脑上,安装公司提供的串口调试软件,测试两个LoRa之间互发信息,也就是在窗口里互相发些字符串。

3,设置LoRa,这一步很关键,因为LoRa默认波特率是9600,要改成与GPS的115200一致,另外LEVEL也要改到7,才能满足GPS发送的信息量。还是在步骤2的连线系统上设置,安装LoRa的说明,发送+++进入AT界面,有时候要+++换行再点发送,进入之后按说明分别在各自的串口调试窗口设置好两个LoRa模块的波特率和LEVEL。

4,接人GPS至其中一个LoRa,也是需要把VCC、GND、RX、TX这4根线接到LoRa上,这个时候LoRa是从TTL模块上拔下来了的,因此没有电源,可以外接一个3.7V锂电池,也可以只插TTL模块的电源两根线到LoRa模块上用于调试(注意不能插其他线)。另一个LoRa还照样插到TTL模块接到电脑上,串口显示收到一堆奇怪的字符。不管它,关闭串口,然后在U-Center 8.20中去打开这个串口,这时候GPS信号就通过LoRa A隔空传给了LoRa B并传回电脑U-Center软件,就能遥测显示GPS地理位置、高度、速度等信息了。U-Center软件有记录功能,通过记录的轨迹还可以定位搜索飞行器最终落地位置。

 

 

 

 

 

 

 

 

 

 

 

### RFT Algorithm and LoRA Technique Overview #### What is the RFT Algorithm? The Random Fourier Transform (RFT) algorithm refers to a method that leverages random projections of input data into higher-dimensional spaces using sinusoidal functions, enabling efficient computation for kernel methods without explicitly computing pairwise similarities between all points[^1]. This approach significantly reduces computational complexity while maintaining high accuracy in tasks such as classification or regression. For instance, when applying RFT within machine learning models: ```python import numpy as np def rft_transform(X, D=1000, gamma=1.0): """ Apply Random Fourier Transform on dataset X. Parameters: X : ndarray of shape (n_samples, n_features) Input data matrix. D : int Dimensionality after transformation. gamma : float Scaling parameter for the Gaussian kernel approximation. Returns: Z : ndarray of shape (n_samples, D) Transformed feature space. """ omega = np.random.normal(0, np.sqrt(gamma), size=(X.shape[1], D)) b = np.random.uniform(0, 2 * np.pi, size=D) Z = np.cos(np.dot(X, omega) + b[:, None]) return np.sqrt(2 / D) * Z.T ``` This code snippet demonstrates how one might implement an approximate version of the Gaussian kernel via RFT transformations by projecting inputs onto randomly sampled frequencies from a predefined distribution[^2]. #### Understanding Low-Rank Adaptation (LoRA) Low-Rank Adaptation (LoRA) represents a fine-tuning strategy designed specifically for large pre-trained language models where only low-rank matrices are updated during training instead of adjusting every weight value directly[^3]. By doing so, it drastically cuts down memory usage along with speeding up convergence rates compared to traditional full-parameter optimization techniques like Stochastic Gradient Descent (SGD). An example implementation could look something similar below showing insertion points marked clearly inside transformer layers' attention blocks: ```python class LoraLinear(nn.Module): def __init__(self, linear_layer: nn.Linear, rank=4): super().__init__() self.linear = linear_layer # Initialize trainable parameters A & B corresponding to U*A*B*V^T decomposition form self.A = nn.Parameter(torch.randn((linear_layer.out_features, rank))) self.B = nn.Parameter(torch.zeros((rank, linear_layer.in_features))) def forward(self, x): original_output = self.linear(x) lora_output = torch.matmul(original_output @ self.A, self.B) combined_outputs = original_output + lora_output return combined_outputs ``` Here we define `LoraLinear`, which wraps around existing PyTorch Linear Layers adding two new learnable tensors representing scaled versions of our desired updates constrained under certain ranks defined earlier through hyperparameter tuning processes[^4].
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