• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.88-96
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• 2014

In modern electronic warfare systems, the necessity of a more accurate estimation method based on non-AOA (arrival of angle) measurement, such as TDOA and FDOA, have been increased. The previous researches using single TDOA have been carried out in terms of not only the development of emitter location algorithms but also the enhancement of measurement accuracy. Recently, however, the combined TDOA/FDOA method is of considerable interest because it is able to estimate the velocity vector of a moving emitter and acquire a pair of TDOA and FDOA measurements from a single sensor pair. In this circumstance, it is needed to derive the required FDOA measurement accuracy in order that the TDOA/FDOA combined localization system outperforms the previous single TDOA localization systems. Therefore, we analyze the contribution of FDOA measurement accuracy to emitter location, then propose the criterion based on CRLB (Cramer-Rao lower bound). Simulations are included to examine the validity of the proposed criterion by using the Gauss-Newton algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.9
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• pp.746-756
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• 2013

The emitter geolocation method using the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) has more accurate performance comparing to the single TDOA or FDOA based method. The estimation performance varies with the sensor paring strategies, the deployment and velocities of the sensors. Therefore, to establish effective strategy on the electronic warfare system, it is required to analyze the relation between the estimation accuracy and the operational condition of sensors. However, in the conventional non-iterative method, the restriction of the deployment of sensors and the reference sensor exists. Therefore, we derive the emitter geolocation method based on a Gauss-Newton method which is available to apply to any various sensor pairs and the deployment and velocities of the sensors. In addition, simulation results are included to compare the performance of geolocation method according to the used measurements: the combined TDOA/FDOA, TDOA, and FDOA. Also, we present that the combined TDOA/FDOA method outperforms over single TDOA or FDOA on the estimation accuracy with the CEP plane.

• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.234-240
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• 2011

In this paper, the performances of Cram$\acute{e}$r-Rao Lower Bound (CRLB) with Frequency Difference of Arrival (FDOA) measurements for Unmanned Aerial Vehicle (UAV) tracking are investigated. We focus on two cases: the influence on CRLB with FDOA measurements collected by time, and random distribution of Ground Stations (GSs). We derived the performance by gauging the size of CRLB through Complementary Cumulative Distribution Function (CCDF). From the simulation results, broader distribution of GSs and FDOA measurements by longer time bring about better performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.60-68
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• 2014

In modern electronic warfare systems, a demand on the more accurate estimation method based on TDOA and FDOA has been increased. TDOA/FDOA localization consists of two-stage procedures; the extraction of information from signals, and the estimation of emitter location. CAF(complex ambiguity function) is known as a basic method in the extraction stage. However, when we extract TDOA and FDOA information from VHF(very high frequency) communication signals, conventional CAF algorithms may not work within a permitted time because of much computation. Therefore, in this paper, an improved sequential estimation algorithm based on CAF is proposed for effective calculation of extracting TDOA and FDOA estimates in terms of computational complexity. The proposed method is compared with the conventional CAF-based algorithms through simulation. In addition, we derive the optimal performance based on the CRLB(Cramer-Lao lower bound) to check the extraction performance of the proposed method.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.37-45
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• 2015

In modern electronic warfare systems, a demand on the more accurate estimation method based on TDOA and FDOA has been increased. TDOA/FDOA localization consists of two-stage procedures: the extraction of information from signals and the estimation of emitter location. Various algorithms based on CAF(complex ambiguity function), which is known as a basic method, has been presented in the area of extractions. When we extract TDOA and FDOA information using a conventional method based on the CAF algorithm from communication signals, considerably long integration time is required for the accurate position estimation of an unknown emitter far from sensors more than 300 km. Such long integration time yields huge amount of transmission data from sensors to a central processing unit, resulting in heavy computiational complexity. Therefore, we theoretically analyze the integration time for TDOA/FDOA information using CRLB and propose a two-stage global optimization algorithm which can minimize the transmission time and a computational complexity. The proposed method is compared with the conventional CAF-based algorithms in terms of a computational complexity and the CRLB to verify the estimation performance.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.72-85
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• 1998

In this paper, we propose a new method that sequentially estimates TDOA(Time Delay Of Arrival) and FDOA(Frequency Delay Of Arrival) for extracting the information about the bearing and relative velocity of a target in passive radar or sonar arrays. The objective is to efficiently estimate the TDOA and FDOA between two sensor signal measurements, corrupted by correlated Gaussian noise sources in an unknown way. The proposed method utilizes the one dimensional slice function of the third order cumulants between the two sensor measurements, by which the effect of correlated Gaussian measurement noises can be significantly suppressed for the estimation of TDOA. Because the proposed sequential algoritjhm uses the one dimensional complex ambiguity function based on the TDOA estimate from the first step, the amount of computations needed for accurate estimationof FDOA can be dramatically reduced, especially for the cases where high frequency resolution is required. It is demonstrated that the proposed algorithm outperforms existing TDOA/FDOA estimation algorithms based on the ML(maximum likelihood) criterionandthe complex ambiguity function of the third order cumulant as well, in the MSE(mean squared error) sense and computational burden. Various numerical resutls on the detection probability, MSE and the floatingpoint computational burden are presented via Monte-Carlo simulations for different types of noises, different lengths of data, and different signal-to-noise ratios.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.589-594
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• 2015

In this paper, we described the Cramer-Rao Lower Bound (CLRB) performances of Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) methods when there are multiple emitters. The TDOA and FDOA values between two receivers can be simultaneously estimated by using the so-called Complex Ambiguity Function (CAF). In the case of multiple emitters, there exist Inter Symbol Interferences (ISIs) in the measurement data. Therefore, it is required to reduce the effect of ISI and provide a performance evaluation method of TDOA and FDOA estimations. In order to eliminate the ISIs, using of a filter bank before calculating CAF is proposed when the carrier frequencies of the emitters are different to one another. Angle of Arrival (AOA) or Received Signal Strength (RSS) methods before calculating CAF were proposed to reduce the ISIs when the carrier frequencies are the same. In order to evaluate the CRLB of TDOA and FDOA estimations, we employed the conditional probability distribution method and described the numerical comparison results.

• ETRI Journal
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• v.34 no.2
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• pp.260-263
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• 2012

In this letter, we propose a moving-target tracking algorithm based on a particle filter that uses the time difference of arrival (TDOA)/frequency difference of arrival (FDOA) measurements acquired by distributed sensors. It is shown that the performance of the proposed algorithm, based on the particle filter, outperforms the one based on the extended Kalman filter. The use of both the TDOA and FDOA measurements is shown to be effective in the moving-target tracking. It is proven that the particle filter deals with the nonlinear nature of the movingtarget tracking problem successfully.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.15-21
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• 2020

This paper presents the outlier detection algorithm in the estimation method of a source location and velocity based on two-step weighted least-squares method using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) data. Since the accuracy of the estimated location and velocity of a moving source can be reduced by the outliers of TDOA and FDOA data, it is important to detect and remove the outliers. In this paper, the method to find the minimum inlier data and the method to determine whether TDOA and FDOA data are included in inliers or outliers are presented. The results of numerical simulations show that the accuracy of the estimated location and velocity is improved by removing the outliers of TDOA and FDOA data.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.76-84
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• 2013

In the passive emitter localization using instantaneous TDOA (time difference of arrival) and FDOA (frequency difference of arrival) measurements, the estimation accuracy can be improved by collecting additional measurements. To achieve this goal, it is required to increase the number of the sensors. However, in electronic warfare environment, a large number of sensors cause the loss of military strength due to high probability of intercept. Also, the additional processes should be considered such as the data link and the clock synchronization between the sensors. Hence, in this paper, the passive localization of a stationary emitter is presented by using the successive TDOA and FDOA measurements from two moving sensors. In this case, since an independent pair of sensors is added in the data set at every instant of measurement, each pair of sensors does not share the common reference sensor. Therefore, the QCLS (quadratic correction least squares) methods cannot be applied, in which all pairs of sensor should include the common reference sensor. For this reason, a Gauss-Newton algorithm is adopted to solve the non-linear least square problem. In addition, to show the performance of the proposed method, we compare the RMSE (root mean square error) of the estimates with CRLB (Cramer-Rao lower bound) and derived the CEP (circular error probable) planes to analyze the expected estimation performance on the 2-dimensional space.