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

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 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)

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.

This paper describes an implementation of the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). We show how the computation of the DFT and IDFT may be performed in Java and show why such operations are typically considered slow. This is a multi-part paper, in part 2, we discuss a speed up of the DFT and IDFT using a class of algorithms known as the FFT (Fast Fourier Transform) and the IFFT (Inverse Fast Fourier Transform). Part 3 demonstrates the computation of the PSD (Power Spectral Density) and applications of the DFT and IDFT. The applications include filtering, windowing, pitch shifting and the spectral analysis of re-sampling.

The use of unmanned aerial vehicles (UAVs) is becoming increasingly widespread in many areas of human life every year. Not only military, but also civilian use of UAVs in various fields of science, industry, forestry, agronomy, cartography, ecology, and space research is hard to overestimate. For this reason, the use and development of UAVs in various fields remains extremely promising in the near future. Compared to manned vehicles, UAVs have the following advantages: no crew and no need for life support systems, large airfields; lower cost and low expenses for their development, production and operation; better weight and dimensions; high reliability and maneuverability; a wide range of specialized equipment for placement on board. UAVs are being introduced into all spheres of human life, even those where UAVs have no manned alternative. Some models of multicopters are already being used to deliver goods and extinguish fires. UAVs of new designs continue to be developed.In all areas of UAV application, the task of increasing autonomy remains relevant and important. The duration of con-tinuous operation and flight range directly affect the capabilities of the UAV, its ability to perform tasks and efficiency.UAV power consumption depends on the weight of the payload, weather conditions and drone speed. Energy consumption is a critical criterion for a drone to perform its missions and needs to be further studied. Many studies have been conducted to increase flight time by improving energy efficiency or reducing the energy consumed by the drone.Study the influence of battery parameters (capacity and weight) and propeller group (number of propellers and their diameter) on the maximum possible flight duration of UAVs of different masses.The main factors affecting the maximum flight duration of a UAV are considered. A model for calculating the power consumed by the device in the hover mode is presented. The maximum flight duration of a UAV is calculated depending on the battery capacity and weight, the number of propellers, and the diameter of the propellers for vehicles of different masses.It is shown that there is no point in increasing the battery capacity indefinitely, and there is an optimum point that depends on the ratio of the battery mass to the mass of the vehicle. It is also shown that increasing the diameter of the propellers significantly increases the flight time, while increasing their number is not as effective.

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.

The Gaussian mixture model (GMM) has been extensively investigated in nonlinear/non-Gaussian filtering problems. This paper presents two enhancements for GMM-based nonlinear filtering techniques, namely, the adaptive ordering of the measurement update and normalized innovation square (NIS)-based mixture component management. The first technique selects the order of measurement update by maximizing the marginal measurement likelihood to improve performance. The second approach takes the filtering history of a mixture component into account and prunes those components with NIS larger than a threshold to eliminate their impact on the filtering posterior. The advantage of the proposed enhancements is illustrated via simulations that consider source tracking using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements received at two unmanned aerial vehicles (UAVs). A GMM-cubature quadrature Kalman filter (CQKF) is implemented and its performances with different measurement update and mixture component management strategies are compared. The superior performance obtained via the use of the two proposed techniques is demonstrated.

A fast raw signal simulation method based on the equivalent scatterer and subaperture processing for the one-stationary bistatic synthetic aperture radar (OS-BSAR) is presented. It considers the radar's motion error to simulate the more accuracy raw signal. First, the OS-BSAR geometry is built in elliptical polar coordinate, and then the scene is divided into several equidistant elliptical rings. Based on the equivalent scatterer, the approximate SAR system transfer function is derived, thus each pulse's raw signal is calculated by the convolution of the transmitted signal and transfer function, using the fast Fourier transform (FFT). To further improve the simulation speed, the subaperture and subscene processing are used. The transfer function for the same subaperture's pulses is calculated by the weighted sum of all the subscenes' equivalent scattering coefficient in the same equidistant elliptical ring, using the nonuniform FFT (NUFFT). This method involves NUFFT, FFT and complex multiplication, which means the easier operation and higher speed. Implementation of the proposed method is discussed. Simulation is given to prove the validity of the proposed method.

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.

Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.

Abstract. The rapid development of unmanned aerial vehicles is a challenge for applied research. Many technologies are developed and then researcher are looking up for their application in different sectors. Therefore, we decided to verify the use of the unmanned aerial vehicle for wood chips pile monitoring. We compared the use of GNSS device and unmanned aerial vehicle for volume estimation of four wood chips piles. We used DJI Phantom 3 Professional with the built-in camera and GNSS device (geoexplorer 6000). We used Agisoft photoscan for processing photos and ArcGIS for processing points. Volumes calculated from pictures were not statistically significantly different from amounts calculated from GNSS data and high correlation between them was found (p = 0.9993). We conclude that the use of unmanned aerial vehicle instead of the GNSS device does not lead to significantly different results. Tthe data collection consumed from almost 12 to 20 times less time with the use of UAV. Additionally, UAV provides documentation trough orthomosaic.

Regular fast Fourier transform (FFT) algorithms require uniformly sampled data. In many practical situations, however, the input data is nonuniform, and hence the regular FFT does not apply. To overcome this difficulty the authors have proposed an accurate algorithm for the nonuniform forward FFT (NUFFT) based on a new class of matrices, the regular Fourier matrices. For the nonuniform inverse FFT (NU-IFFT) algorithm, the conjugate-gradient method and the regular FFT algorithm are combined to speed up a matrix inversion. Numerical results show that these algorithms are more than one order of magnitude more accurate than existing algorithms.