Signal processing + parameter estimation

In summary, parameter estimation in DSP involves estimating parameters in a math model to adjust the control law and improve control over time. This is not limited to communications, and can be covered in graduate-level digital controls courses. Parameters such as bit error rates and maximum likelihood detection can be tweaked using various methods, with some estimators being more effective than others in detecting signals. Zero padding can also have a significant impact on the results.
  • #1
JamesGoh
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Is parameter estimation in DSP concerned with things such as bit error rates, maximum likelihood detection etc ... ?

If not, can someone point me in the right direction so I can find some learning material.

thanks
 
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  • #2
when i used parameter estimation (many many years ago), it was part of an adaptive controller model. that is, estimate parameters to adjust the control law, and over time get tighter control and a tighter confidence interval on the error of your control law estimates. our modelling was not related to communications, but the theory for the parameter estimation should be covered in standard graduate-level digital controls courses, i think.
 
  • #3
JamesGoh said:
Is parameter estimation in DSP concerned with things such as bit error rates, maximum likelihood detection etc ... ?

Yes those are parameters that some may like to play with; i.e. tweak. If you have a math model with any parameters that you want to tweak then these are parameter estimations. In http://www.digitalCalculus.com/misc/calculus-programming.html" [Broken] most math models have parameters that are tweated by a 'find' statement.

For DSP, there are a dozen http://www.digitalCalculus.com/demo/rainbow.html" [Broken] in one program to compare results. Maximum likelihood is one of the methods available. Results differ dramatically between estimators. See effects of zero padding. Some estimators can detect signals 50 to 100 dB from main signal.
 
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What is signal processing?

Signal processing is the manipulation of signals to extract useful information or to enhance their quality. It involves techniques such as filtering, amplification, and noise reduction to improve the signal for further analysis or transmission.

What is parameter estimation?

Parameter estimation is the process of determining the values of unknown parameters in a mathematical model based on observed data. It is used to estimate the characteristics of a signal, such as its amplitude, frequency, or phase.

What are the different types of signal processing?

There are three main types of signal processing: analog, digital, and statistical. Analog signal processing involves manipulating signals in their original form, while digital signal processing converts signals into digital form for processing. Statistical signal processing uses mathematical models and algorithms to analyze signals and extract information.

How is signal processing used in real-world applications?

Signal processing has numerous applications in various fields, including telecommunications, audio and video processing, medical imaging, and radar and sonar systems. It is also used in data analysis and machine learning for pattern recognition and prediction.

What are the challenges in signal processing and parameter estimation?

Some of the challenges in signal processing and parameter estimation include dealing with noise and interference, determining the appropriate mathematical models and algorithms for a specific signal, and finding the optimal balance between accuracy and computational complexity.

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