Talk:Estimation of signal parameters via rotational invariance techniques

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Unitary ESPRIT

Hello, colleagues! What are you thinking about covering of the stuff about Unitary ESPRIT (a.k.a. TLS-ESPRIT) in this article? In one hand, this is widespread method in the communications, which even included in the MATLAB ULA toolbox. In other hand, I'm afraid that the mathematical description can be ambiguous for the Wikipedia article. There are several open-access materials on the Internet about Unitary ESPRIT (scientific articles ^[1] ^[2], Master Thesis), and I guess, MATLAB examples can also be found... Maybe, this method should be at least mentioned, I don't know, frankly speaking. — Preceding unsigned comment added by Kirlf (talk • contribs) 08:20, 28 January 2022 (UTC)[reply]

References

^ Haardt, M., Zoltowski, M.D., Mathews, C.P. and Ramos, J., 1998. ESPRIT and closed-form 2D angle estimation with planar arrays. In Digital Signal Processing Handbook. Boca Raton, FL: CRC Press.
^ Ren, S., Ma, X., Yan, S. and Hao, C., 2013. 2-D unitary ESPRIT-like direction-of-arrival (DOA) estimation for coherent signals with a uniform rectangular array. Sensors, 13(4), pp.4272-4288.

Current status

What do you think about the current status of the article? How can it be improved?

I started the rework of this article. However, I am not an expert in English language nor an expert on how to write mathematical things on Wikipedia. Some articles give proofs of their claims and some give general ideas. So, it is not clear to me what is judged as textbook style (even after reading about it). Tituswes (talk) 09:02, 10 December 2024 (UTC)[reply]

Hello! Let's take a look...

Currently, the article reads like a textbook-style math derivation. It seems instructional. It can be improved by adding non-math explanations. For instance, in plain English terms, briefly explain things like:

What is the structure of this technique, in a nutshell? How does it operate? ("It takes input signals, and ...")
Why is it useful?
What problems does it solve?
What are its applications?
What are its limitations?
Who invented it?

Answering these in the introduction, or in an "Overview" section, would help. Right now, the short introduction is insufficient to prepare a non-expert reader. Even for many math-literate readers, the immediate jump into "One-dimensional ESPRIT" is too technical and lacks motivation.

Symbolic derivations instruct rather than inform (hence the textbook tone). The point of including a derivation in an article is to illustrate or reveal an insight, not rigorously prove a statement. Try to simplify or remove intermediate steps in a derivation--the end result is what most readers want to know about. (This can be tricky, because a derivation that skips too much can leave the reader confused.) Also, before a derivation, it helps if the reader understands (1) what is about to be shown, and (2) why this is relevant.

Hope this helps! Thank you for any contributions you can make to this article!

(Also: I'm removing the copy edit tag, because the article is currently not ready for copy editing at this time. First it needs more writing.)

Cheers, Jayowyn (talk) 01:42, 23 May 2025 (UTC)[reply]

[1] Haardt, M., Zoltowski, M.D., Mathews, C.P. and Ramos, J., 1998. ESPRIT and closed-form 2D angle estimation with planar arrays. In Digital Signal Processing Handbook. Boca Raton, FL: CRC Press.

[2] Ren, S., Ma, X., Yan, S. and Hao, C., 2013. 2-D unitary ESPRIT-like direction-of-arrival (DOA) estimation for coherent signals with a uniform rectangular array. Sensors, 13(4), pp.4272-4288.

[1]

[2]