Random sampling analog circuit compressed sensing measurement and signal reconstruction method

Abstract

The invention relates to a random sampling analog circuit compressed sensing measurement and signal reconstruction method, which belongs to the field of electronic system test and fault diagnosis. Aiming at a fault signal having a sparsity distribution characteristic per se or in an orthogonal space in an output response of an analog circuit, a test node is selected according to a circuit topology structure, circuit output responses are randomly sampled under a distributed sensor test network, response signals are expressed in a sparse way on a transform domain by utilizing discrete orthonormal basis, compressed sensing measurement of the sparse signals is completed under observability matrix projection, and when the recovery rate of signal reconstruction by randomly compressed sampling points reaches more than 80 percent, the compressed measurement values of the circuit output responses are effective, can form a characteristic set and can be used for analog circuit fault diagnosis. The method solves the problems that the traditional analog signal sampling occupies a large number of hardware resources, large signal reconstruction calculated amount and the like; and the random sampling compressed sensing measurement method is utilized to improve the efficiency of electronic system testing.

Description

technical field [0001] The invention relates to the fields of electronic system testing and fault diagnosis, in particular to a method for analog circuit compression sensing measurement and response signal reconstruction, which can be applied to electronic system design verification in the fields of military, communication, electronics and aerospace. , Integrated circuit testing, manufacturing and packaging, automated test production lines and measurement and control equipment research and development. Background technique [0002] Conventional integrated circuit test methods have the problems of poor universality, low test efficiency, and high redundancy of test vector sets. Many factors lead to the high test cost of complex large-scale integrated circuits. Efficient analog circuit test methods are conducive to improving the cost performance of electronic products. . However, component tolerances, continuous output response and nonlinear factors lead to complex and diverse...

AI Technical Summary

Problems solved by technology

[0002] Conventional integrated circuit testing methods have problems such as poor universality, low testing efficiency, and high redundancy of test vector sets. Many factors lead to high testing costs for complex large-scale integrated circuits. Efficient analog circuit testing methods are conducive to improving the cost performance of electronic products.

However, component tolerance, output response continuity and nonlinear factors lead to complex and diverse analog circuit fault situations, and the circuit response state often needs to be described by high-dimensional test data

Due to the large amount of characteristic information contained in analog circuits, the modeling of fault diagnosis equations is extremely difficult, and the traditional fault dictionary method is difficult to effectively extract fault features; starzyk proposed a fault dictionary method, but this method is only suitable for diagnosis after fault component modeling, diagnosis Large-scale circuits are computationally expensive

If the circuit input and output response analysis method is used to extract the time domain or frequency domain response function series core as the fault feature, it is difficult to realize automation and intelligence in online test diagnosis

The latest research results of computational intelligence technologies such as neural network, wavelet analysis and genetic algorithm are used to solve the fault diagnosis equation, avoiding the application of fault diagnosis due to computational complexity and time-consuming, artificial neural network directly from the observation data (training samples) Learning is widely used as a simple and effective identification method and heuristic technology, but it lacks practical value due to a large number of test sample data

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