Enhanced GMM-Based Filtering with Measurement Update Ordering and Innovation-Based Pruning

This paper considers the problem of tracking a moving target using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained at two unmanned aerial vehicles (UAVs) with varying baseline. Accumulation time is necessary because the emitter position cannot be estimated at each emission when there are only two UAVs as sensors. And the target position and velocity estimation accuracy often suffer from low convergence in conventional formation flight mode no matter what nonlinear filtering algorithm is used. Based on the analysis of the influence of different flight modes on the positioning performance, a moving target tracking system from two UAVs with varying baseline is proposed. The performance of unscented Kalman filter (UKF) under proposed system is analysed and compared with the Cramer-Rao lower bound (CRLB). Simulation results show that the proposed system can speed up the convergence.

Passive geolocation of communication emitters provides great benefits to military and civilian surveillance and security operations. Measurement combination for stationary emitters based on Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) may be obtained by sensors mounted on mobile platforms, for example, on a pair of Unmanned Aerial Vehicles (UAVs). This paper relies on joint TDOA/FDOA estimation algorithm. Then we put forward a kind of method by adjusting the receiver direction to improve the precision of localization. The results of the simulation show that the method can improve precision of the positioning 2–10 times.KeywordsPassive localizationDouble-star positioning systemTime differences of arrival (TDOA)Frequency differences of arrival (FDOA)Geometric dilution of precision (GDOP)

In double‐satellite interference localization system, the position of the interference source is computed by estimating the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) between the signals retransmitted by two satellites. The traditional cross ambiguity function (CAF)‐based TDOA/FDOA estimation method requires the constant TDOA/FDOA between the two received signals, which can be satisfied only when the signal length is short. Therefore, the CAF estimation method cannot be applied in long‐time accumulation (LTA). To solve this problem, a novel LTA method is proposed based on signal segmentation. In the proposed method, the two received signals are first divided into several short signal segments. Subsequently, the TDOA, the initial FDOA, and the FDOA change rate of each signal segment are estimated by a modified CAF method. After compensating the FDOA difference between the signal segments, the results are spliced again to form a complete signal without FDOA variation. Consequently, the signal energy can be concentrated and the estimation accuracy of FDOA can be improved. Experimental results of real data demonstrate that greater signal‐to‐noise ratio (SNR) gain is achieved by the proposed method, compared to the traditional CAF‐based method.

To weaken the effect of receiver location error on localization accuracy and make the localization model closer to the practical scenario, this paper considers the receiver location errors, usually neglected in prior studies into the measurement model, and proposes an algebraic method for locating a moving source using time difference of arrival (TDOA), frequency difference of arrival (FDOA), and differential Doppler rate measurements. The proposed method is based on the pseudo-linear set of equations and two-step weighted least square estimator. Only noise values of receiver locations and three types of positioning measurements are available for processing. In addition, a new Cramér-Rao lower bound (CRLB) combining TDOA, FDOA, and differential Doppler rate in the presence of receiver location errors is also derived in this paper. Theoretical analysis and simulation results both indicate that the proposed method can attain CRLB at a moderate noise level, avoid the rank deficiency problem efficiently, and achieve a significant improvement over the existing methods.

This paper considers the problem of moving source localization by introducing an additional Doppler frequency shift (DFS) measurement into the previous location system using time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs). Because of the separation of associated data channels and the difference in the estimation techniques, the DFS is independent of TDOA and FDOA measurements. The Cramer-Rao lower bound (CRLB) analysis shows that introducing DFS can improve source localization accuracy comparing with that of the TDOA and FDOA localization system, especially for the velocity location accuracy. An improved two-step weighted least-squares (I-TSWLS) source localization method utilizing DFS as well as TDOA and FDOA is developed. The solution is shown analytically to achieve the CRLB over small noise region, and have a better performance than that of the TSWLS method, especially when the sensor station error noises become large. Simulation results demonstrate the good performance of the proposed method.

Robust TDOA/FDOA estimation from emitter signals for hybrid localization using UAVs

This paper develops the model of the joint phantom track deception and the joint techniques of time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) localization to deceive air defense radar networks under the condition that an unmanned aerial vehicle (UAV) swarm has no prior knowledge of the radars’ precise locations, and related performance experiment and analysis are presented to demonstrate the effectiveness of the proposed method and to clarify the influence factors of phantom track deception. The main contributions of this paper are as follows. Firstly, the model of phantom track deception against a radar network by UAV swarm without prior knowledge of the radars’ positions are established. Secondly, TDOA/FDOA are adapted to locate networked enemy radars using UAV swarm, where the Fisher information matrix (FIM) is derived to evaluate the estimation accuracy. Thirdly, the uncertainty analysis consisting of radar location error and UAV position error is deduced. With these efforts, the integrated capability of sensing and jamming is realized. Moreover, the same source testing using space resolution cell (SRC) from the perspective of a radar network is executed to provide guidance for phantom track design. Finally, performance experiment and analysis are given to verify the theoretical analysis with simulation results.

This paper presents a comprehensive study of methods for detecting and localizing electronic warfare (EW) sources under complex conditions, which include the influence of intensive intentional jamming, multipath signal propagation, time-varying emission parameters, and dynamic movement of the sources. The relevance of the research is driven by the growing role of EW means in modern armed conflicts and the need to ensure timely detection and neutralization of such threats. The classification of EW sources based on spectral, energy, and spatial characteristics is considered, along with an analysis of their impact on communication, radar, and navigation systems. Traditional methods for determining the coordinates of sources—such as amplitude, phase, TDOA (Time Difference of Arrival), and FDOA (Frequency Difference of Arrival)—are examined in detail, as well as modern approaches that rely on adaptive antenna arrays, spectral analysis, and artificial intelligence algorithms for signal identification. Special attention is given to algorithms for improving localization accuracy in noisy and dynamic environments, including adaptive filtering methods, multiposition signal processing (data fusion), optimization of sensor network configurations, and trajectory prediction of moving objects. Results of computer simulations are presented, with error assessments for various application scenarios and different interference levels. The proposed approaches can be applied in advanced electronic intelligence systems and automated EW countermeasure complexes to enhance the efficiency of real-time detection, identification, and localization of emission sources. Keywords: electronic warfare, source localization, TDOA, FDOA, adaptive antennas, artificial intelligence, multisensor processing, intensive jamming, simulation.

The position and velocity estimation of underwater moving targets is an important part of the application of underwater sensor networks. The commonly used fusion of time difference of arrival (TDOA) and angle of arrival (AOA) can only roughly estimate the target position, but also has some limitations for nonlinear problems. So in order to overcome the nonlinear problem and improve the accuracy of underwater target location.Based on TDOA and AOA, this paper adopts a three-step location method which combines frequency difference of arrival (FDOA).At the same time, the whale algorithm is used to optimize the measurement error based on the weighted least square method (Tswls). Finally, the experimental results show that compared with TDOA location method and tdoa/aoa hybrid location algorithm, this method is superior to the above methods in the accuracy and bias of position and velocity estimation.

In this paper, we propose a cooperative localization method for moving source using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. Research on cooperative localization of moving source is an important topic to solve the passive source localization problem in different fields, such as radar, sonar, navigation, wireless communications and so forth. We investigate the improvement in source estimation accuracy. In the cooperative localization structure, a cooperative localization method based on quasi-Newton (QN) approach and two-step weighted least-squares (TSWLS) method is designed. The research results prove that the source localization accuracy has been improved effectively by using cooperative localization method based on QN-TSWLS method.

We consider the problem of geolocating two unknown co-channel emitters by a cluster of formation-flying satellites using both time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. As the association between the TDOA/FDOA measurements obtained by each pair of satellites and the corresponding emitters is typically not known, the emitter-measurement association and the emitters' locations need to be jointly estimated. In this paper, we first formulate the joint estimation problem as a mixed integer nonlinear optimization problem. Then, we propose a semidefinite relaxation-based approach to tackle the problem and demonstrate its efficacy via simulations.

In this paper, geolocation problem using noisy differences in time of arrival (TDOA) and frequency difference of arrival (FDOA) measurements is considered, and a sub-BLUE solution is proposed, a new TDOA/FDOA based recursive geolocation algorithm is also developed. The efficiency of the new algorithms is illustrated with simulation results.

This paper focuses on localizing a moving source with time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements in a wireless sensor network. An improved two-stage weighted least squares closed-form solution is proposed. The weighted spherical-interpolation method and deviation refining method are applied in the first and second stage respectively. It is analytically verified that the performance of proposed solution can attain the Cramer-Rao lower bound under mild Gaussian noise assumption. The proposed solution is shown to be effective and significantly decreases the bias of the estimate in terms of numerical simulations.

Direct solution for fixed source location using well-posed TDOA and FDOA measurements

Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement data fusion which further uses the maximum likelihood of the measurements received at each sensor of the surveillance region. The measurement grids of the sensors are used to perform data association. The simulation results show that the proposed algorithm outperforms the multipass grid search and further effectively eliminated the ghost targets created due to the fusion of measurements received at each sensor. Moreover, the proposed algorithm reduces the computational complexity compared to other existing algorithms as it does not use repeated steps for convergence or any biological evolutions. Furthermore, the experimental testing of the proposed technique was executed successfully for tracking multiple targets in different scenarios passively.