Non-cooperative target location is accomplished by means of multiple passive radar receivers deployed in the region of interest and that detect the delayed replicas of the signal emitted by the target and estimate the time difference of arrival. However, in realistic scenarios, some of the involved sensors could not correctly work if the sensor is victim of intentional/unintentional interference and/or physical damage of the device or its communication link. Thus, procedures for failure detection become of primary interest to discard the measurements related to the out-of-order sensors. The approach proposed in this paper identifies sensors under failure from the analysis of the errors in the equation system implemented to estimate the delays. More precisely, we first compute the second and fourth order correlations (namely, cross-correlation and cross-cross-correlation, i.e. the cross-correlation between signals' cross-correlations) of the incoming signals to build up the system of equation. Then, we perform a sequential cancellation of the equations that experience the highest errors. A statistical test based on the number of canceled equations related to a specific sensor is used to state whether or not the specific sensor is under failure. Finally, the performance of the entire failure detection architecture is assessed by numerical simulations also in comparison with a heuristic method based on the percentages of canceled equations and its standard counterparts not performing any outlier screening.

Giunta, G., Pallotta, L., Orlando, D. (2022). Detecting Sensor Failures in TDOA-based Passive Radars: A Statistical Approach based on Outlier Distribution. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 1-12 [10.1109/TAES.2022.3210893].

Detecting Sensor Failures in TDOA-based Passive Radars: A Statistical Approach based on Outlier Distribution

Giunta G.;
2022-01-01

Abstract

Non-cooperative target location is accomplished by means of multiple passive radar receivers deployed in the region of interest and that detect the delayed replicas of the signal emitted by the target and estimate the time difference of arrival. However, in realistic scenarios, some of the involved sensors could not correctly work if the sensor is victim of intentional/unintentional interference and/or physical damage of the device or its communication link. Thus, procedures for failure detection become of primary interest to discard the measurements related to the out-of-order sensors. The approach proposed in this paper identifies sensors under failure from the analysis of the errors in the equation system implemented to estimate the delays. More precisely, we first compute the second and fourth order correlations (namely, cross-correlation and cross-cross-correlation, i.e. the cross-correlation between signals' cross-correlations) of the incoming signals to build up the system of equation. Then, we perform a sequential cancellation of the equations that experience the highest errors. A statistical test based on the number of canceled equations related to a specific sensor is used to state whether or not the specific sensor is under failure. Finally, the performance of the entire failure detection architecture is assessed by numerical simulations also in comparison with a heuristic method based on the percentages of canceled equations and its standard counterparts not performing any outlier screening.
2022
Giunta, G., Pallotta, L., Orlando, D. (2022). Detecting Sensor Failures in TDOA-based Passive Radars: A Statistical Approach based on Outlier Distribution. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 1-12 [10.1109/TAES.2022.3210893].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/420027
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 2
social impact