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Signal detection method based on error decision multi-model hypothesis test

A technology of signal detection and hypothesis testing, applied in the field of signal detection

Active Publication Date: 2017-09-15
XIAN UNIV OF SCI & TECH
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Problems solved by technology

[0013] The technical problem to be solved by the present invention is to provide a signal detection method based on false-fixed multi-model hypothesis testing in view of the deficiencies in the above-mentioned prior art. The problem of unbounded time can obtain detection results with appropriate average sample size and error probability, and can provide new ideas for the future development of detection theory. It has strong practicability and is easy to promote and use.

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Embodiment Construction

[0060] Such as figure 1 As shown, the signal detection method based on wrongly determined multi-model hypothesis testing of the present invention comprises the following steps:

[0061] Step 1. According to the signal characteristics, select the non-signal model set and the signal model set (the non-signal model set is generally a Gaussian distribution with a mean value of 0, and the signal model set contains various signal distribution forms), and the original model-based signal detection The problem description is H 0 and H 1 The binary composite hypothesis test form for :

[0062]

[0063]

[0064] where x k is the signal state vector at time k and is n x dimension vector, z k is the signal observation vector at time k and is n z dimensional vector, k is the discrete time variable, a is the signal observation model and b is the signal model and The subscript j of the element in a and the subscript j of the element in b both indicate that the element with sub...

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Abstract

The invention discloses a signal detection method based on error decision multi-model hypothesis test. The method comprises the following steps: 1, selecting a non-signal model set and a signal model set for signal features, and describing an original signal detection problem based on model as a binary composite hypothesis verification mode; 2, calculating the likelihood of the non-signal model set; 3, calculating the likelihood of the signal model set according to a multi-model algorithm; 4, calculating the likelihood of an intermediate signal model of the non-signal model set and the signal model set; and 5, calculating the test statistics of a 2-SPRT decision rule, and comparing the test statistics with a threshold t obtain an asymptotically optimal detection results. The method disclosed by the invention is simple in steps, is convenient to implement, solves the problems of wrong judgment and no upper limit of stop time in the existing method, can obtain a detection result with a proper average sample amount and an error probability, and can provide a new idea for the development of the detection theory in the future, is high in practicability and is convenient to promote and use.

Description

technical field [0001] The invention belongs to the technical field of signal detection, and in particular relates to a signal detection method based on false-fixed multi-model hypothesis testing. Background technique [0002] The rapid development of communication theory, information theory, computer science and technology, and microelectronics technology has led to the development of signal detection theory and technology in the direction of more complex interference environments, diversified signal forms, and wider application ranges. The understanding has been greatly improved, and the problem of model-based signal detection has been widely used in many fields such as electronic information systems, biomedical engineering, aerospace engineering, pattern recognition and automatic control, especially in the field of cognitive radio spectrum sensing. prospect. In view of the particularity of this problem, the difficulty lies in the difference in the assumed distribution st...

Claims

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Application Information

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IPC IPC(8): H04B17/382H04B17/391H04W16/14
CPCH04B17/382H04B17/3913H04W16/14
Inventor 刘宝侯媛彬连峰黄梦涛王静婷
Owner XIAN UNIV OF SCI & TECH
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