Performance Research on Multi-Target Detection in Different Noisy Environments
Abstract This paper studies five classic multi-target detection methods in different noisy environments, including Akaike information criterion, ration criterion, Rissanen's minimum description length, Gerschgorin disk estimator and Eigen-increment threshold methods. Theoretical and statistical analyses of these methods have been done through simulations and a real-world water tank experiment. It is known that these detection approaches suffer from array errors and environmental noises. A new diagonal correction algorithm has been proposed to address the issue of degraded detection performance in practical systems due to array errors and environmental noises. This algorithm not only improves the detection performance of these multi-target detection methods in low signal-to-noise ratios (SNR), but also enhances the robust property in high SNR scenarios.
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Wan, F., Wen, J., and Liang, L., 2020. A source number estimation method based on improved eigenvalue decomposition algorithm. In 2020 IEEE 20th International Conference on Communication Technology (ICCT), pages 1184-1189. IEEE, Nanning, China.
Wang, K. and Huang, J., 1996. On Estimating the Correct Number of Sinusoidal Signals in White Noise. JOURNAL-NORTHWESTERN POLYTECHNICAL UNIVERSITY, 14:308-312.
Wax, M. and Kailath, T., 1985. Detection of signals by information theoretic criteria. IEEE Transactions on acoustics, speech, and signal processing, 33(2):387-392.
Wax, M. and Ziskind, I., 1989. Detection of the number of coherent signals by the MDL principle. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(8):1190-1196.
Wei, C., Zhang, Z., and Zhengjia, H., 2015. Information criterion-based source number estimation methods with comparison. Hsi-AnChiao Tung Ta Hsueh/Journal of Xi’an Jiaotong University, 49(8):38-44.
Wong, K. M., Zhang, Q.-T., Reilly, J. P., and Yip, P. C., 1990. On information theoretic criteria for determining the number of signals in high resolution array processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(11):1959-1971.
Wu, H.-T., Yang, J.-F., and Chen, F.-K., 1995. Source number estimators using transformed Gerschgorin radii. IEEE transactions on signal processing, 43(6):1325-1333.
Yang, Q. and Han, R., 2013. Estimation of Number of Signal Sources in Far Separated Subarrays.
Zhao, L., Krishnaiah, P. R., and Bai, Z., 1986. On detection of the number of signals in presence of white noise. Journal of multivariate analysis, 20(1):1-25.
Zhao, L.-C., Krishnaiah, P., and Bai, Z.-D., 1987. Remarks on certain criteria for detection of number of signals. IEEE transactions on acoustics, speech, and signal processing, 35(2):129-132.
Casado-Vara, R., Novais, P., Gil, A. B., Prieto, J., and Corchado, J. M., 2019. Distributed Continuous-Time Fault Estimation Control for Multiple Devices in IoT Networks. IEEE Access, 7:11972-11984. doi: 10.1109/ACCESS.2019.2892905.
Chen, W., Wong, K. M., and Reilly, J. P., 1991. Detection of the number of signals: A predicted eigen-threshold approach. IEEE Transactions on Signal Processing, 39(5):1088-1098.
De Ridder, F., Pintelon, R., Schoukens, J., and Gillikin, D. P., 2005. Modified AIC and MDL model selection criteria for short data records. IEEE Transactions on Instrumentation and Measurement, 54(1):144-150.
Fishler, E. and Messer, H., 1999. Order statistics approach for determining the number of sources using an array of sensors. IEEE Signal Processing Letters, 6(7):179-182.
Fishler, E. and Messer, H., 2000. On the use of order statistics for improved detection of signals by the MDL criterion. IEEE Transactions on Signal Processing, 48(8):2242-2247.
Hu, O., Zheng, F., and Faulkner, M., 1999. Detecting the number of signals using antenna array: a single threshold solution. In ISSPA'99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No. 99EX359), volume 2, pages 905-908. IEEE.
Hussain, A., Ahmad, M., Hussain, T., and Ullah, I., 2022a. Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(2):207-235. doi: 10.14201/adcaij.27430.
Hussain, A., Hussain, T., and Ullah, I., 2022b. The Approach of Data Mining: A Performance-based Perspective of Segregated Data Estimation to Classify Distinction by Applying Diverse Data Mining Classifiers. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4):339-359. doi: 10.14201/ADCAIJ2021104339359.
Jiang, H. and Zhou, Z., 2023. A hybrid signal source signal statistics and localisation algorithm. In 2023 IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), volume 6, pages 974-981. IEEE, Chongqing, China.
Karhunen, J., Cichocki, A., Kasprzak, W., and Pajunen, P., 1997. On neural blind separation with noise suppression and redundancy reduction. International Journal of Neural Systems, 8(02):219-237.
Li, H., Yang, Q., Liu, A., and Lyu, Z., 2021. Estimation of sources number and DOA by pseudo-covariance matrix constructed from single snapshot.
Li, T., Corchado, J. M., and Sun, S., 2019a. Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion. IEEE Transactions on Aerospace and Electronic Systems, 55(5):2150-2163. doi: 10.1109/TAES.2018.2882960.
Li, T., Elvira, V., Fan, H., and Corchado, J. M., 2019b. Local-Diffusion-Based Distributed SMC-PHD Filtering Using Sensors With Limited Sensing Range. IEEE Sensors Journal, 19(4):1580-1589. doi: 10.1109/JSEN.2018.2882084.
Li, T., Fan, H., García, J., and Corchado, J. M., 2019c. Second-Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion: Application to Multi-Sensor Target Tracking. Inf. Fusion, 51(C):233–243. ISSN 1566-2535.
Li, T., Hu, Z., Liu, Z., and Wang, X., 2023a. Multisensor Suboptimal Fusion Student's t Filter. IEEE Transactions on Aerospace and Electronic Systems, 59(3):3378-3387. doi: 10.1109/TAES.2022.3210157.
Li, T., Liang, H., Xiao, B., Pan, Q., and He, Y., 2023b. Finite mixture modeling in time series: A survey of Bayesian filters and fusion approaches. Information Fusion, 98:101827. ISSN 1566-2535. doi: 10.1016/j.inffus.2023.101827.
Li, T., Song, Y., and Fan, H., 2023c. From target tracking to targeting track: A data-driven yet analytical approach to joint target detection and tracking. Signal Processing, 205:108883. ISSN 0165-1684. doi: 10.1016/j.sigpro.2022.108883.
Liu, J. and Liao, G., 2004. Research on source number detection in colored noise. Xi'an: Xidian University.
Meyer, F., Kropfreiter, T., Williams, J. L., Lau, R., Hlawatsch, F., Braca, P., and Win, M. Z., 2018. Message Passing Algorithms for Scalable Multitarget Tracking. Proceedings of the IEEE, 106(2):221-259. doi: 10.1109/JPROC.2018.2789427.
Muhamada, A. W. and Mohammed, A. A., 2022. Review on recent Computer Vision Methods for Human Action Recognition. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4):361-379. doi: 10.14201/ADCAIJ2021104361379.
Qader Kheder, M. and Aree Ali, M., 2023. IoT-Based Vision Techniques in Autonomous Driving: A Review. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(3):367-394.
Sayed, A. H., 2014. Adaptation, Learning, and Optimization over Networks. Found. Trends in Machine Learn., 7(4-5):311-801. ISSN 1935-8237. doi: 10.1561/2200000051.
Stoica, P. and Cedervall, M., 1997. Detection tests for array processing in unknown correlated noise fields. IEEE Transactions on Signal Processing, 45(9):2351-2362. doi: 10.1109/78.622957.
Strang, G., 2006. Linear algebra and its applications. Belmont, CA: Thomson, Brooks/Cole.
Van Trees, H. L., 2002. Optimum array processing: Part IV of detection, estimation, and modulation theory. John Wiley & Sons.
Wan, F., Wen, J., and Liang, L., 2020. A source number estimation method based on improved eigenvalue decomposition algorithm. In 2020 IEEE 20th International Conference on Communication Technology (ICCT), pages 1184-1189. IEEE, Nanning, China.
Wang, K. and Huang, J., 1996. On Estimating the Correct Number of Sinusoidal Signals in White Noise. JOURNAL-NORTHWESTERN POLYTECHNICAL UNIVERSITY, 14:308-312.
Wax, M. and Kailath, T., 1985. Detection of signals by information theoretic criteria. IEEE Transactions on acoustics, speech, and signal processing, 33(2):387-392.
Wax, M. and Ziskind, I., 1989. Detection of the number of coherent signals by the MDL principle. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(8):1190-1196.
Wei, C., Zhang, Z., and Zhengjia, H., 2015. Information criterion-based source number estimation methods with comparison. Hsi-AnChiao Tung Ta Hsueh/Journal of Xi’an Jiaotong University, 49(8):38-44.
Wong, K. M., Zhang, Q.-T., Reilly, J. P., and Yip, P. C., 1990. On information theoretic criteria for determining the number of signals in high resolution array processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(11):1959-1971.
Wu, H.-T., Yang, J.-F., and Chen, F.-K., 1995. Source number estimators using transformed Gerschgorin radii. IEEE transactions on signal processing, 43(6):1325-1333.
Yang, Q. and Han, R., 2013. Estimation of Number of Signal Sources in Far Separated Subarrays.
Zhao, L., Krishnaiah, P. R., and Bai, Z., 1986. On detection of the number of signals in presence of white noise. Journal of multivariate analysis, 20(1):1-25.
Zhao, L.-C., Krishnaiah, P., and Bai, Z.-D., 1987. Remarks on certain criteria for detection of number of signals. IEEE transactions on acoustics, speech, and signal processing, 35(2):129-132.
Yin, Y., Jia, Q., Xu, H., & Fu, G. (2024). Performance Research on Multi-Target Detection in Different Noisy Environments. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31710. https://doi.org/10.14201/adcaij.31710
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