Kalman | A KalmanFilter to be discussed

I have the parameters of an eclipse: semi-major axis; semi-minor axis; rotation angle; x-position of the centre of the ellipse; y-position of the centre of the ellipse.

I wish to produce an array whereby all points inside the ellipse are set to one and all points outside are zero.

I found this answer, which gave a solution to this without rotation, and using height and width, rather than semi-major and semi-minor:

I have the following problem. I would like to remove the noise from an IMU sensor. My clue would be a Kalman filter. In the Arduino IDE you could easily implement one via library. Now I would like to solve this by C# directly on the computer but I can't find a library on .NET 4 that works. I tried it with NugetPackages : MathNet. and Emgu.CV. Do you have alternatives that work on .NET 4.0 or do they even work, and if they do, does anyone have a good example? Have a nice day :)

EDIT: Adding Arduino IDE code

Building up the model from a previous post, and the helpful answer, I've subclassed the MLEModel to encapsulate the model. I'd like to allow for two parameters q1 and q2 so that the state noise covariance matrix is generalized as in Sarkka (2013)'s example 4.3 (terms re-arranged for my convention):

I thought I would accomplish this with the update method below, but I'm running into problems with the fit method, as it returns a UFuncTypeError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind'. What am I missing here?

Is it possible to implement a model like the one presented in Bayesian Filtering and Smoothing, example 3.6 in statsmodels?

I can follow along with the Matlab code provided, but I'm not sure if and how this kind of model can be implemented in statsmodels.

The example involves tracking the position of an object in 2D space. The state is four-dimensional x=(x_1, x_2, x_3, x_4), but I've re-arranged the vector so that (x_1, x_3) represent position and (x_2, x_4) represent velocity in the two directions. Simulated data of the process consist of 100 position observations, arranged in a 2x100 matrix Y.

I have a question. I have a twin-engine boat and I want to implement an autopilot in it. Using GPS alone my boat goes to the destination like a sine wave, unfortunately for 300m. Just like the picture below.

I Use RadioLink M8N GPS SE100 (http://radiolink.com.cn/doce/UploadFile/ProductFile/SE100Manual.pdf) and STM32. It has a built-in geomagnetic sensor HMC5983. Is it possible to use this sensor and GPS to let my boat go straight?

I don't know much about all those Kalman filters, Fusion, etc. My question is what should I use, apart from the GPS itself, what kind of sensors and filters to make my boat sail in a straight line.

Thanks in advance for the tips and hints.

I am trying to get filtered RSSI values of each BLE beacon using Kalman filtering. I cannot use a single instance of kalman filtering on all BLE devices, how to map or assign a instance of kalman filter for each BLE device. I know it has something to do with maps but cannot figure out how to implement it. Any help would be much appreciated.

Im checking the hector_localization stack, that provide the full 6DOF pose of a robot or platform. It uses various sensor sources, which are fused using an Extended Kalman filter. Acceleration and angular rates from an inertial measurement unit (IMU) serve as primary measurements and also support barometric pressure sensors. I check the launch which is this one

I am using statsmodels to fit a Local Linear Trend state space model which inherits from the sm.tsa.statespace.MLEModel class using the code from the example in the documentation:

https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_local_linear_trend.html

The state space model and Kalman filter should handle missing values naturally but when I add some null values the state space model outputs nulls. In another example in the docs, implementing SARIMAX it appears that missing data appears to be handled automatically:

https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_sarimax_internet.html

Is there a way to handle missing values in the same way for a Local Linear Trend model?

I am trying to identify a state space model from discrete time series data in Python using statsmodels library: statsmodel.tsa.statespace.sarimax.SARIMAX.

I need the matrices of the state space general form (here the statsmodel reference): from the statsmodel page these matrices are explained but it is not clear how to extrapolate them.

For example if I want to apply a kalman filter to the identified model (by means of a sarimax) I need the matrices described in this picture state space matrices needed

Is it possible to obtain the matrices coefficients with statsmodel?