signalsambiguity

Signalsambiguity is a term used in signal processing and related disciplines to describe the uncertainty that arises when a detected signal cannot be uniquely identified or reconstructed from observations. It occurs when different underlying signals or parameter sets produce indistinguishable measurements given the available sensing model, noise, and processing methods.

Common causes include undersampling and aliasing, measurement noise, nonlinear distortions, and model mismatch. Ambiguity can also arise from phase wrapping, Doppler or frequency ambiguity in moving targets, and time-delay or direction-of-arrival ambiguities in sensor arrays. In spatial domains, symmetric or co-located sensor configurations can yield multiple plausible source locations. In the temporal and spectral domains, repeated patterns or harmonics can obscure the true signal, leading to multiple mathematically valid interpretations.

Types of signalsambiguity typically fall into temporal, frequency, spatial, or parameter categories. Temporal ambiguity concerns uncertainty in timing or order. Frequency ambiguity involves uncertain spectral content or carrier frequency when sampling constraints are insufficient. Spatial ambiguity relates to multiple possible source positions or directions. Parameter ambiguity covers uncertainties in estimated amplitudes, phases, or other model parameters.

Mitigation relies on design choices and processing techniques that break degeneracies. Strategies include increasing signal-to-noise ratio, improving sampling rates or resolution, using a priori information, employing multi-sensor fusion, and applying advanced estimation methods such as Bayesian inference, regularization, or ambiguity-function analysis. Understanding and mitigating signalsambiguity is essential for reliable detection, localization, and decoding across communications, radar, sonar, and related fields.

See also: aliasing, identifiability, ambiguity function, phase unwrapping, time-delay estimation.