Interference Source Localization Research Articles

Remote Interference Source Localization: A Multi‐UAV‐Based Cooperative Framework

Remote Interference Source Localization: A Multi‐UAV‐Based Cooperative Framework

A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location.

With the increase in satellite communication interference, the tri-satellite time difference of arrival (TDOA) localization technique, which is an effective method to determine the location of the interference using sensors or antennas, has been developed rapidly. The location of the interference source is determined through the intersection of the TDOA lines of position (LOP). However, when the two TDOA LOP have two mirrored intersection points, it is theoretically difficult to determine the real location. Aiming at this problem, a method for eliminating mirrored location based on multiple moment TDOA is proposed in this paper. First, the TDOA results are measured at multiple moments using the cross-ambiguity function (CAF), and the localization equation set is established based on the World Geodetic System (WGS)-84 earth ellipsoid model. Then, the initial location result can be obtained by solving the equation set through the Newton iteration method. Finally, the high-precision location result after eliminating the mirrored location is obtained after the single moment localization based on the initial location. Simulation experiments and real measured data processing results verify the effectiveness of the proposed method. It still has good robustness under the condition of large measurement errors and deviations from the prior initial values.

Data driven interference source localization based on train real-time onboard interference monitoring

Interference is an important factor affecting railway mobile communications, which leads to deterioration of communication quality. In serious cases, the link connection interruption will lead to degraded operation of train control system, then affect the operation efficiency of the railway system. In order to reduce the impact of interference on railway communications, the interference source should be localized and eliminated timely. However, the traditional interference localization methods require auxiliary equipment to be arranged along the track. In this paper, a data driven interference source localization method based on train real-time onboard interference monitoring is proposed, which can reduce the interference localization cost. Based on the collected Received Signal Strength (RSS) of interference signal, the particle filter principle is used to locate the target interference source combined with the data driven path-loss models. Besides, the modified whale optimization algorithm (MWOA) is introduced to further improve the positioning accuracy. The simulation and experiment results show that the proposed data driven interference source localization method outperforms the traditional localization method, and the positioning accuracy is improved from tens of meters degree to meters degree, which can meet the requirements of engineering practice to find the interference source.

UAV-Based Interference Source Localization: A Multimodal Q-Learning Approach

Localization problem is a significant component of the Internet of Things (IoT) and interference source localization is of great importance in the context of spectrum monitoring and management. However, it remains challenging to quickly but accurately locate an interference source from the distance, especially when little is known about the interference source. To handle this problem, a single learning algorithm can be exploited to search and locate the interference source. However, it is varying dynamics in varying environments that can make the design of such a learning algorithm intractable. In our study, we employ an unmanned aerial vehicle (UAV) to realize the localization. Moreover, a novel multimodal Q-learning framework along with its algorithm is proposed, and the framework combines pattern recognition with Q-learning. The proposed learning architecture can adjust the parameters of Q-learning algorithm dynamically based on the changing environments so as to achieve better detection precision, longer localization distance and shorter searching time. The simulation verifies multimodal Q-learning algorithm’s performance on interference source localization along with its capability of adapting to environmental change. The simulation results confirm the proposed concept of multimodal Q-learning. It is shown that the multimodal Q-learning based localization algorithms can outperform various baselines in terms of both accuracy and detection distance. The searching time consumed by the UAV is also largely reduced. This observation indicates that the capability of environmental adaption introduced by the proposed multimodal framework can benefit the Q-learning algorithms.

TDOA estimation of dual-satellites interference localization based on blind separation

The time difference of arrival (TDOA) estimation plays a crucial role in the accurate localization of the satellite interference source. In the dual-satellites interference source localization system, the target signal from the adjacent satellite is likely to be interfered by the normal communication signal with the same frequency. Therefore, the signal to noise ratio (SNR) of the target signal would become too low, and the TDOA estimation through cross-correlation processing would be unreliable or even unattainable. This paper proposes a technique based on blind separation to solve the co-channel interference problem, where separation of the mixed signal can be carried out by the particle filter (PF) algorithm. The experimental results show that the proposed method could achieve more accurate TDOA estimation. The measured data obtained by using the software radio platform at 915 MHz and 2 GHz respectively verify the effectiveness of the proposed method.

A Method of Experimental Design for GNSS-Dot Net under Jamming Attack

The feature of deception jamming for GNSS-dot networks is researched and analyzed, and it is difficult to accurately locate and correct the interference position by using the interference detection methods of the traditional WSN, a new attack detection algorithm that based on an improved angle of arrival (AOA) positioning mechanism to determine the point of disruption and interference correction is proposed. Nextly the algorithm of a single interference source localization based on the hyperbolic method by using anti-jamming principle of the GNSS is researched and given, and can locate both single and multiple interference sources. Then the indicators and methods of performance evaluation for the GNSS-dot networks are proposed. Finally, Experiment based on the algorithm is realized, and the attack detection and correction is very efficient, and interference location under ideal conditions is higher efficiency, and the strategies of anti deception jamming are also identified.