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A Sparse TDOA Estimation Method for LPI Source Localization Using Distributed Sensors

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Abstract

In this paper, we propose a novel sparse time difference of arrival (S-TDOA) estimation method for the localization of the low probability of intercept (LPI) signals using distributed sensors. The proposed method uses the phase linearity of the wideband spatial covariances between the sensors obtained using sparse data. In the conventional distributed sensor systems, whole the data between the sensors is required to be transmitted to a central processor with a high computational load. In this work, the TDOA estimation is performed by using sparse sensor data in order to reduce computational load and high data sharing rate, which is more desirable for the practical implementations. It is shown in various simulations that the proposed method can effectively estimate the TDOA between the distributed sensors using the sparse multi-sensor data even in low signal-to-noise ratio. It is also shown that the TDOA estimation performance of the proposed method follows to Cramer Rao lower bound. Moreover, it is also shown that the data sharing rate and computation time are quite reduced without a significant performance loss with the sparsity process.

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References

  1. Akansu, A. N., Duhamel, P., Lin, X., & De Courville, M. (1998). Orthogonal transmultiplexers in communication: A review. IEEE Transactions on Signal Processing, 46(4), 979–995.

    Article  Google Scholar 

  2. Pickholtz, R., Schilling, D., & Milstein, L. (1982). Theory of spread-spectrum communications-a tutorial. IEEE Transactions on Communications, 30(5), 855–884.

    Article  Google Scholar 

  3. Chamola, V., Kotesh, P., Agarwal, A., Gupta, N., & Guizani, M. et al.(2020). A comprehensive review of unmanned aerial vehicle attacks and neutralization techniques. Ad Hoc Networks, p 102324.

  4. Wang, J., Liu, Y., & Song, H. (2021). Counter-unmanned aircraft system (s)(c-uas): State of the art, challenges, and future trends. IEEE Aerospace and Electronic Systems Magazine, 36(3), 4–29.

    Article  Google Scholar 

  5. Farrell, T. C., & Prescott, G.(1996) A low probability of intercept signal detection receiver using quadrature mirror filter bank trees. In 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, vol 3, pp 1558–1561. IEEE.

  6. Wiley, R. G.(2006) ELINT: The interception and analysis of radar signals. Artech House

  7. Zhang, H., Wei, P., & Mou, Q. (2013). A semidefinite relaxation approach to blind despreading of long-code DS-SS signal with carrier frequency offset. IEEE Signal Processing Letters, 20(7), 705–708.

    Article  Google Scholar 

  8. Vankayalapati, N., & Kay, S. (2012). Asymptotically optimal detection of low probability of intercept signals using distributed sensors. IEEE Transactions on Aerospace and Electronic Systems, 48(1), 737–748.

    Article  Google Scholar 

  9. Pace, P. E.(2009) Detecting and classifying low probability of intercept radar. Artech House.

  10. Wymeersch, H., Lien, J., & Win, M. Z. (2009). Cooperative localization in wireless networks. Proceedings of the IEEE, 97(2), 427–450.

    Article  Google Scholar 

  11. Rappaport, T. S., Reed, J. H., & Woerner, B. D. (1996). Position location using wireless communications on highways of the future. IEEE Communications Magazine, 34(10), 33–41.

    Article  Google Scholar 

  12. Li, W., & Jia, Y. (2015). Distributed target tracking by time of arrival and received signal strength with unknown path loss exponent. IET Signal Processing, 9(9), 681–686.

    Article  Google Scholar 

  13. Chenguang, S., Jianjiang, Z., & Fei, W. (2016). Lpi based resource management for target tracking in distributed radar network. In 2016 IEEE Radar Conference (RadarConf), pp 1–5. IEEE.

  14. Shen, J., Molisch, A. F., & Salmi, J. (2012). Accurate passive location estimation using toa measurements. IEEE Transactions on Wireless Communications, 11(6), 2182–2192.

    Article  Google Scholar 

  15. Wang, G., Cai, S., Li, Y., & Jin, M. (2013). Second-order cone relaxation for toa-based source localization with unknown start transmission time. IEEE Transactions on Vehicular Technology, 63(6), 2973–2977.

    Article  Google Scholar 

  16. Gezici, S. (2008). A survey on wireless position estimation. Wireless Personal Communications, 44(3), 263–282.

    Article  Google Scholar 

  17. Wang, G., & Chen, H. (2011). An importance sampling method for tdoa-based source localization. IEEE Transactions on Wireless Communications, 10(5), 1560–1568.

    Article  Google Scholar 

  18. Bin, X., Sun, G., Ran, Yu., & Yang, Z. (2013). High-accuracy tdoa-based localization without time synchronization. IEEE Transactions on Parallel and Distributed Systems, 24(8), 1567–1576.

    Article  Google Scholar 

  19. Huang, B., Xie, L., & Yang, Z. (2014). Tdoa-based source localization with distance-dependent noises. IEEE Transactions on Wireless Communications, 14(1), 468–480.

    Article  Google Scholar 

  20. Li, W., Tang, Q., Huang, C., Ren, C., & Li, Y. (2017). A new close form location algorithm with aoa and tdoa for mobile user. Wireless Personal Communications, 97(2), 3061–3080.

    Article  Google Scholar 

  21. Lee, K., Jungkeun, O., & You, K. (2017). Closed-form solution of tdoa-based geolocation and tracking: A recursive weighted least square approach. Wireless Personal Communications, 94(4), 3451–3464.

    Article  Google Scholar 

  22. Kim, S.-D., & Chong, J.-W. (2017). A novel tdoa-based localization algorithm using asynchronous base stations. Wireless Personal Communications, 96(2), 2341–2349.

    Article  Google Scholar 

  23. Wang, G., So, A.M.-C., & Li, Y. (2016). Robust convex approximation methods for tdoa-based localization under nlos conditions. IEEE Transactions on Signal processing, 64(13), 3281–3296.

    Article  MathSciNet  Google Scholar 

  24. Uysal, C., Filik, T. (2014). Presence detection of long-and-short-code DS-SS signals using the phase linearity of multichannel sensors. In 19th International Conference on Digital Signal Processing (DSP), 2014, pp. 305–309.

  25. Uysal, C., Filik, T. (2015). A joint detection and localization method for non-cooperative DS-SS signals. In Military Communications Conference, MILCOM 2015–2015 IEEE, pp. 523–528.

  26. Uysal,C., Filik, T. (2016). The localization of lpi signals using sparse tdoa estimates. In 2016 24th Signal Processing and Communication Application Conference (SIU), pp. 1669–1672.

  27. Naresh, V. & Steven, K. (2010). Asymptotically optimal detection/localization of LPI signals of emitters using distributed sensors. In SPIE Defense, Security, and Sensing, pp 77060U–77060U.

  28. Hamschin, B., Clancy, J., Grabbe, M., Fortier, M., & Novak, J. (2014). Passive detection, characterization, and localization of multiple LFMCW LPI signals. In 2014 IEEE Radar Conference, pp. 0537–0543.

  29. Denk, A. (2006). Detection and jamming low probability of intercept (LPI) radars. DTIC Document: Technical report.

  30. Jiang, L., Li, L., & Zhao, G. Q. (2016). Polyphase coded low probability of intercept signals detection and estimation using time-frequency rate distribution. IET Signal Processing, 10(1), 46–54.

    Article  Google Scholar 

  31. Kishore, T. R., & Deergha, R. K. (2017). Automatic intrapulse modulation classification of advanced lpi radar waveforms. IEEE Transactions on Aerospace and Electronic Systems, 53(2), 901–914.

    Article  Google Scholar 

  32. Kong, S.-H., Kim, M., Hoang, L. M., & Kim, E. (2018). Automatic lpi radar waveform recognition using cnn. IEEE Access, 6, 4207–4219.

    Article  Google Scholar 

  33. Shi, C., Wang, F., Salous, S., & Zhou, J. (2018). Low probability of intercept-based optimal ofdm waveform design strategy for an integrated radar and communications system. IEEE Access, 6, 57689–57699.

    Article  Google Scholar 

  34. Knapp, C., & Clifford Carter, G. (1976). The generalized correlation method for estimation of time delay. IEEE Transactions on Acoustics, Speech and Signal Processing, 24(4), 320–327.

    Article  Google Scholar 

  35. Clifford Carter, G., Nuttall, A. H., & Cable, P. G. (1973). The smoothed coherence transform. Proceedings of the IEEE, 61(10), 1497–1498.

    Article  Google Scholar 

  36. Hannan, E. J., & Thomson, P. J. (1973). Estimating group delay. Biometrika, 60(2), 241–253.

    Article  MathSciNet  Google Scholar 

  37. So, H. C., Chan, Y. T., & Chan, F. K. W. (2008). Closed-form formulae for time-difference-of-arrival estimation. IEEE Transactions on Signal Processing, 56(6), 2614–2620.

    Article  MathSciNet  Google Scholar 

  38. Chan, Y.-T., & Ho, K. C. (2005). Joint time-scale and tdoa estimation: Analysis and fast approximation. IEEE Transactions on Signal processing, 53(8), 2625–2634.

    Article  MathSciNet  Google Scholar 

  39. Guo, F., Zhang, Z., & Yang, L. (2016). TDOA/FDOA estimation method based on dechirp. IET Signal Processing, 10(5), 486–492.

    Article  Google Scholar 

  40. Piersol, A. (1981). Time delay estimation using phase data. IEEE Transactions on Acoustics, Speech and Signal Processing, 29(3), 471–477.

    Article  Google Scholar 

  41. So, H. C. (2001). Time-delay estimation for sinusoidal signals. IEE Proceedings-Radar, Sonar and Navigation, 148(6), 318–324.

    Article  Google Scholar 

  42. Huang, Y., Lin, J., & M, G. (2013). High accuracy time delay measurements for band-pass signals. IEEE Transactions on Instrumentation and Measurement, 62(11), 2998–3005.

    Article  Google Scholar 

  43. Chan, Y. T., Hattin, R., & Plant, J. B. (1978). The least squares estimation of time delay and its use in signal detection. In IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ’78.,3, 665–669.

  44. Ahnström, U., Falk, J., Händel, P., Wikström, M. (2003). Detection and direction-finding of spread spectrum signals using correlation and narrowband interference rejection. In Nordic Matlab Conference. COMSOL A/S.

  45. Falk, J., Händel, P., Jansson, M. (2002). Direction finding for electronic warfare systems using the phase of the cross spectral density. In RadioVetenskap och Kommunikation (RVK), Stockholm, Sweden, June 2002, pp. 264–268.

  46. Pages-Zamora, A., & Vidal, J. (2002). Closed-form solution for positioning based on angle of arrival measurements. In The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications,4(4), 1522–1526.

  47. Sathish, C. (2005). Advances in Direction-of-arrival Estimation. Artech House.

  48. FaIk, J., Handel, P., & Jansson, M. (2003). Effects of frequency and phase errors in electronic warfare TDOA direction-finding systems. In Military Communications Conference, 2003. MILCOM ’03. 2003 IEEE,1(1), 118–123.

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  1. Department of Electrical and Electronics Engineering, Eskisehir Technical University, Iki Eylul Campus, 26555, Eskisehir, Turkey

    Can Uysal & Tansu Filik

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  1. Can Uysal
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  2. Tansu Filik
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Uysal, C., Filik, T. A Sparse TDOA Estimation Method for LPI Source Localization Using Distributed Sensors. Wireless Pers Commun 123, 2171–2187 (2022). https://doi.org/10.1007/s11277-021-09233-1

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