A Simplified Framework for Fault Prediction in Radar Transmitter based on Vector Autoregression Model



Published Sep 4, 2023
Sheriff Murtala Soojung Hur Yongwan Park


The prediction of faults in radar subsystems remains a challenge. It is common practice to use multiple sensors to monitor the performance of electronic components in radar. The complexity of processing the measurements increases with the number of monitored quantities. In this paper, we presented a simple method to predict the fault degradation of radar transmitter. Using historical data of monitored quantities leading to two different faults, the vector autoregression model is applied to predict future values of monitored quantities resulting in fault degradation in marine radar. The results showed that the proposed method can be useful for cases where failure in subsystem needs to be promptly detected and corrected to avoid overall system failure. We also demonstrated the performance of the proposed method on interpolated data generated from radar transmitter fault data.

Abstract 174 | PDF Downloads 181



radar transmitter, fault prediction, piecewise cubic hermite interpolating polynomial, vector autoregression model

Aminghafari, M., Cheze, N., Poggi, J-M. (2006), Multivariate de-noising using wavelets and principal component analysis. Computational Statistics & Data Analysis, vol. 50(9), pp. 2381–2398.

Fritsch, F. N., & Carlson, R. E. (1980). Monotone Piecewise Cubic Interpolation. SIAM Journal on Numerical Analysis, 17(2), 238-246.

Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Science & Business Media.

Mallat, S. and Hwang, W.L., (1992). Singularity detection and processing with wavelets. IEEE transactions on information theory, vol. 38, no 2, pp.617-643.

Mushtaq, R. (2011). Augmented dickey fuller test. Available at SSRN: https://ssrn.com/abstract=1911068

Stein C. M. (1981). Estimation of the mean of a multivariate normal distribution. The annals of Statistics. vol. 9, no. 6, pp. 1135-1151.

Zhai Y., & Fang S. (2020). A Degradation Fault Prognostic Method of Radar Transmitter Combining Multivariate Long Short-Term Memory Network and Multivariate Gaussian Distribution. IEEE Access, vol. 8, pp. 199781- 199791. doi: 10.1109/ACCESS.2020.3035622.

Zhai, Y., Shao, X., Li, J., & Fang, S. (2021). An unsupervised prediction method for radar transmitter degradation fault based on isolation forest. Journal of Physics: Conference Series, vol. 2010(1), p. 012125. IOP Publishing. Doi:10.1088/1742- 6596/2010/1/012125.

Zhai, Y., Liu, D., Cheng, Z., & Fang, S. (2022). A Novel Prognostic Model of the Degradation Malfunction Combining a Dynamic Updated-ARIMA and Multivariate Isolation Forest: Application to Radar Transmitter. Electronics, 11(12), 1921. https://doi.org/10.3390/electronics11121921.
Regular Session Papers