Detection of signals by information theoretic criteria

1985/04/01 by M. Wax, T. Kailath · 3,401 citations
Computer Science · Engineering · Mathematics · #Akaike information criterion #Algorithm #Artificial intelligence #Bayesian information criterion #Blind Source Separation Techniques #Computer science #Control Systems and Identification #Data mining #Detection theory #Detector #Information Criteria #Information theory #Machine learning #Mathematics #Minimum description length #Model selection #Pattern recognition (psychology) #Selection (genetic algorithm) #Statistics #Target Tracking and Data Fusion in Sensor Networks

paper · doi:10.1109/tassp.1985.1164557

published in IEEE Transactions on Acoustics Speech and Signal Processing 33(2), 387-392 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1985/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

A new approach is presented to the problem of detecting the number of signals in a multichannel time-series, based on the application of the information theoretic criteria for model selection introduced by Akaike (AIC) and by Schwartz and Rissanen (MDL). Unlike the conventional hypothesis testing based approach, the new approach does not requite any subjective threshold settings; the number of signals is obtained merely by minimizing the AIC or the MDL criteria. Simulation results that illustrate the performance of the new method for the detection of the number of signals received by a sensor array are presented.

Citations

• Multiple emitter location and signal parameter estimation

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