Robusni algoritam praćenja mjerenjem smjera pomoću strukturiranog potpunog Kalmanovog filtra zasnovanog na metodi najmanjih kvadrata

A nonlinear approach called the robust structured total least squares kalman filter (RSTLS-KF) algorithm is proposed for solving tracking inaccuracy caused by outliers in bearings-only multi-station passive tracking. In that regard, the robust extremal function is introduced to the weighted structured total least squares (WSTLS) location criterion, and then the improved Danish equivalent weight function is built on the basis, which can identify outliers automatically and reduce the weight of the polluted data. Finally, the observation equation is linearized according to the RSTLS location result with the structured total least norm (STLN) solution. Hence location and velocity of the target can be given by the Kalman filter. Simulation results show that tracking performance of the RSTLS-KF is comparable or better than that of conventional algorithms. Furthermore, when outliers appear, the RSTLS-KF is accurate and robust, whereas the conventional algorithms become distort seriously.U ovome radu predložen je nelinearni pristup za rješavanje netočnosti uzrokovanih netipčnim vrijednostima kod praćenja mjerenjem smjera pasivnim senzorima s više stanica. Pristup je zasnovan na robusnom strukturiranom potpunom Kalmanovom filtru zasnovanom na metodi najmanjih kvadrata. Pomoću predložene metode moguće je estimirati položaj i brzinu praćenog objekta. Simulacijski rezultati pokazuju da je učinkovitost predloženog algoritma jednaka ili bolja od konvencionalnih algoritama. Nadalje, u prisustvu netipčnih vrijednosti mjerenja, predloženi algoritam zadržava točnost i robusnost, dok konvencionalni algoritmi pokazuju pogreške u estimaciji

RDOA based Emitter Localization using Constrained Least Square Algorithm under NLOS Environment

The emitter localization is a significant problem in many fields such as target tracking, wireless communication, radar and many types of mobile application; In this paper, we apply Kalman filter and constrained least square (CLS) algorithm for compensating these noises. With the proposed two algorithms, we can confirm high accuracy for localization. A simulation demonstrates the performance of our proposed algorithm

Locating Unknown Interference Sources with Time Difference of Arrival Estimates

Adaptive spectrum sharing between different systems and operators is being deployed in order to make use of the wireless spectrum more efficiently. However, when the spectrum is shared, it can create situations in which an operator is unable to determine the identity of an interferer transmitting an unknown signal. This is the situation in which the POWDER testbed found itself in, starting in late 2021. This thesis provides general-purpose tools for operators to locate an unknown signal source in real-world outdoor environments. We used cross-correlation between the signals measured at multiple time-synchronized base stations to estimate the time difference of arrival (TDoA) between each pair. Then, we used a TDoA localization algorithm to locate each unknown transmitted signal source. In particular, for POWDER, we applied these methods to estimate the source locations of multiple unknown interference signals detected in the citizens broadband radio service (CBRS) band with multiple static base stations as the receivers. The localization results are displayed in grid maps that indicate the most likely signal source coordinates of the unknown signals. Our tools are open source and available for other researchers to locate interferers near their deployed network

A Moving Source Localization Method for Distributed Passive Sensor Using TDOA and FDOA Measurements

The conventional moving source localization methods are based on centralized sensors. This paper presents a moving source localization method for distributed passive sensors using TDOA and FDOA measurements. The novel method firstly uses the steepest descent algorithm to obtain a proper initial value of source position and velocity. Then, the coarse location estimation is obtained by maximum likelihood estimation (MLE). Finally, more accurate location estimation is achieved by subtracting theoretical bias, which is approximated by the actual bias using the estimated source location and noisy data measurement. Both theoretical analysis and simulations show that the theoretical bias always meets the actual bias when the noise level is small, and the proposed method can reduce the bias effectively while keeping the same root mean square error (RMSE) with the original MLE and Taylor-series method. Meanwhile, it is less sensitive to the initial guess and attains the CRLB under Gaussian TDOA and FDOA noise at a moderate noise level before the thresholding effect occurs

Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numerically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms; some general guidelines are drawn and conclusions are provided.Mr. Ivan A. Mantilla-Gaviria has been supported by a FPU scholarship (AP2008-03300) from the Spanish Ministry of Education. Moreover, the authors are grateful to ERA A.S. who supplied the recording of TDOA measurements.Mantilla Gaviria, IA.; Leonardi, M.; Galati, G.; Balbastre Tejedor, JV. (2015). Localization algorithms for multilateration (MLAT) systems in airport surface surveillance. Signal, Image and Video Processing. 9(7):1549-1558. https://doi.org/10.1007/s11760-013-0608-1S1549155897EUROCAE-WG-41: ED-117, MOPS for Mode S multilateration systems for use in advanced surface movement guidance and control systems (A-SMGCS). In: The European Organisation for Civil Aviation Equipment (EUROCAE) (November 2003)EUROCAE-WG-70: ED-142, technical specification for wide area multilateration (WAM) systems. In: The European Organisation for Civil Aviation Equipment (EUROCAE) (September 2010)Galati, G., Genderen, P.V.: Proceedings Book of the Tyrrhenian International Workshop on Digital Communications, Enhanced Surveillance of Aircraft and Vehicles (TIWDC/ESAV’11), Capri, Italy (2011)Galati, G., Genderen, P.V.: Proceedings Book of the Tyrrhenian International Workshop on Digital Communications, Enhanced Surveillance of Aircraft and Vehicles (TIWDC/ESAV’08), Capri, Italy (2008)Galati, G., Zellweger, A.: Proceedings Book of the ATM 2002 Advanced Workshop. Capri, Italy (2002)Hahn, W.R., Tretter, S.A.: Optimum processing for delay-vector estimation in passive signal arrays. IEEE Trans. Inf. Theory IT–19(5), 608–614 (1973)Torrieri, D.J.: Statistical theory of passive location systems. IEEE Trans. Aerosp. Electron. Syst. AES–20, 183–198 (1984)Levanon, N.: Lowest GDOP in 2-D scenarios. IEE Proc. Radar Sonar Navig. 147(3), 149–155 (2000)Galati, G., Leonardi, M., Mantilla-Gaviria, I.A., Tosti, M.: Lower bounds of accuracy for enhanced mode-s distributed sensor networks. IET Radar Sonar Navig. 6(3), 190–201 (2012). doi: 10.1049/iet-rsn.2011.0197Smith, J.O., Abel, J.S.: Closed-form least-squares source location estimation from range-difference measurements. IEEE Trans. Acoust. Speech Signal Process. ASSP–35(12), 1661–1669 (1987)Mantilla-Gaviria, I.A., Leonardi, M., Galati, G., Balbastre-Tejedor, J.V., Reyes, E.D.L.: Efficient location strategy for airport surveillance using Mode-S multilateration systems. Int. J. Microw. Wireless Technol. 4(2), 209–216 (2012). doi: 10.1017/S1759078712000104Romero, L., Mason, J.: Evaluation of direct and iterative methods for overdetermined systems of TOA geolocation equations. IEEE Trans. Aerosp. Electron. Syst. 47(2), 1213–1229 (2011)Yang, K., An, J., Bu, X., Sun, G.: Constrained total least-squares location algorithm using time-difference-of-arrival measurements. IEEE Trans. Veh. Technol. 59(3), 1558–1562 (2010)Weng, Y., Xiao, W., Xie, L.: Total least squares method for robust source localization in sensor networks using TDOA measurements. Int. J. Distrib. Sens. Netw. 2011 (Article ID 172902) (2011). doi: 10.1155/2011/172902Mantilla-Gaviria, I.A., Leonardi, M., Galati, G., Balbastre-T, J.V., Reyes, E.D.L.: Improvement of multilateration (MLAT) accuracy and convergence for airport surveillance. In: Tyrrhenian International Workshop on Digital Communications—Enhanced Surveillance of Aircraft and Vehicles (ESAV’11), Capri, Italy (September 12–14, 2011)Mantilla-Gaviria, I.A., Leonardi, M., Balbastre-Tejedor, J.V., Reyes, Edl: On the application of singular value decomposition and Tikhonov regularization to ill-posed problems in hyperbolic passive location. Math. Comput. Model. 57(7–8), 1999–2008 (2013). doi: 10.1016/j.mcm.2012.03.004El-Rabbany, A.: Introduction to GPS: The Global Positioning System, 2nd edn. Artech House, Boston, USA (2006)Trees, HLv: Detection, Estimation and Modulation Theory, Part I. Wiley, New York (2001)Foy, W.H.: Position-location solution by Taylor-series estimation. IEEE Trans. Aerosp. Electron. Syst. AES–12(2), 187–194 (1976)Smith, J.O., Abel, J.S.: The spherical interpolation method of source localization. IEEE J. Ocean. Eng. OE–12(1), 246–252 (1987)Friedlander, B.: A passive localization algorithm and its accuracy analysis. IEEE J. Ocean. Eng. OE–12(1), 234–245 (1987). doi: 10.1109/JOE.1987.1145216Schau, H.C., Robinson, A.Z.: Passive source localization employing intersecting spherical surfaces from time-of-arrival differences. IEEE Trans. Acoust. Speech Signal Process. ASSP–35(8), 1223–1225 (1987)Chan, Y.T., Ho, K.C.: A simple and efficient estimator for hyperbolic location. IEEE Trans. Signal Process. 42(8), 1905–1915 (1994)Schmidt, R.O.: A new approach to geometry of range difference location. IEEE Trans. Aerosp. Electron. Syst. AES–8(6), 821–835 (1972)Geyer, M., Daskalakis, A.: Solving passive multilateration equations using Bancroft’s algorithm. In: Digital Avionics Systems Conference, pp. F41/41–F41/48, Bellevue, WA, USA (1998)Wikipedia: http://en.wikipedia.org/wiki/MultilaterationBancroft, S.: An algebraic solution of the GPS equations. IEEE Trans. Aerosp. Electron. Syst. AES–21(7), 56–59 (1985)Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd edn. The Johns Hopkins University Press, Baltimore (1996)Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. Wiley, New York (2001