当GPS信号下降时，比如说30秒。后期处理应使用同时记录的其他传感器数据猜测经纬度。所以我正在寻找一种解决方案来寻找使用加速度计和陀螺仪传感器覆盖的速度或距离。
客户端可以是任何Android设备或Windows ce设备。后期处理服务器只需解决以CSV或任何其他格式发送的数据。
我发现了关于惯性导航系统和其他一些文章和白皮书算法。并试图实施其中的一些。或者还有其他更好的公式，比如我在下面找到的公式。
vel_new = vel_old +（acc_old +（（acc_new - acc_old ）/ 2.0））* SAMPLING_TIME;编辑：除了所有的传感器数据，我也有我所感兴趣的特定城市地图的所有道路在我的数据库中存储为sqlgeography。所以用我可以得到的大致位置，我会试着找到他们本来可以走的最近的道路，或者转弯。
这是一个典型的问题：它不会工作。答案是关于职位，但即使是速度。使用GPS的最后测量速度和第一个测量速度并在它们之间进行插值，从而强化从地图中推导出的位置约束，这样做会更好。
We are planning to write a software that would track a Car's GPS position, GPS signal strength, Accelerometer (X, Y, Z) and Gyroscope(X, Y, Z) for every 5 seconds or so. And send the data up to a server for post processing. The server would determine what are roads that are covered car. So this is not a real time processing. It can happen in the backend (over the night as batch process)
When the GPS signal goes down, say for 30 seconds. The post processing should guess the lat and lon using the other sensor data recorded in the same time. So I am looking for a solution to find the speed or distance covered using Accelerometer and Gyroscope sensors.
The client could be anything an Android device or a Windows ce device. The post processing server just have to solve the data that is sent in a CSV or any some other format.
I found some articles and white papers about Inertial navigation system and other algorithms. And tried to implement some of them. Or is there any other better formula like the one I found below.
vel_new = vel_old + ( acc_old + ( (acc_new - acc_old ) / 2.0 ) ) * SAMPLING_TIME;EDIT: In addition to all the sensor data, I also have all the roads of the particular city map that I am interested in in my database stored as sqlgeography. So with the approximate position I could get, I would try to find the closest road they could have been, or turned.
解决方案This a classic question: It won't work. That answer is about the position but it won't work even for the speed.
You would be better off with a heuristic that uses the last and the first measured velocity from the GPS and interpolates between them, enforcing position constraints deducible from a map.
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