Emitter Geolocation using Received Signal Strength Difference with Randomly Distributed Sensors

2011 ◽  
Author(s):  
Brad R. Jackson
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Distributed Sensors ◽  
Strength Difference

scholarly journals Received signal strength difference–based tracking estimation method for arbitrarily moving target in wireless sensor networks

2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876487 ◽  
Author(s):  
Zixi Jia ◽  
Bo Guan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Negative Impact ◽  
Estimation Method ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Time Interval ◽  
Received Signal Strength Indicator ◽  
Strength Difference

The surveillance system, which is mainly used for detecting and tracking moving targets, is one of the most significant applications of wireless sensor networks. Up to present, received signal strength indicator is the most common measuring mean for estimating the distance in sensor networks. However, in the presence of noise, it is impossible to gain the accurate distance based on received signal strength indicator. In this article, we propose a new tracking scheme based on received signal strength difference, which is the difference value of received signal strength indicators between two neighboring sampling steps. Supposing the noise has a certain degree of correlation in a certain time interval, received signal strength difference can effectively reduce the negative impact from noise. The tracking algorithm based on received signal strength difference is built: The sensor nodes collectively estimate a possible zone of the target via the signs of received signal strength difference. Next, the possible zone is further immensely shrunk to the refined zone via the absolute values of received signal strength difference. Finally, we determine the target’s final location by choosing the reference dot with the minimum norm in the refined zone. The simulation results demonstrate that the proposed tracking method achieves higher localization accuracy than the typical received signal strength indicator–based scheme. The received signal strength difference–based method also has good generality and robustness with respect to the noises with different deviation values and the target following arbitrarily state model.

scholarly journals Indoor 3-D Localization Based on Received Signal Strength Difference and Factor Graph for Unknown Radio Transmitter

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 338 ◽  
Author(s):  
Liyang Zhang ◽  
Taihang Du ◽  
Chundong Jiang
Keyword(s):  
Measurement Errors ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Least Square ◽  
Practical Significance ◽  
Factor Graph ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Radio Transmitter ◽  
Strength Difference

Accurate localization of the radio transmitter is an important work in radio management. Previous research is more focused on two-dimensional (2-D) scenarios, but the localization of an unknown radio transmitter under three-dimensional (3-D) scenarios has more practical significance. In this paper, we propose a novel 3-D localization algorithm with received signal strength difference (RSSD) information and factor graph (FG), which is suitable for both line-of-sight (LOS) and non-line-of-sight (NLOS) condition. Considering the stochastic properties of measurement errors caused by the indoor environment, RSSD measurements are processed with mean and variance in the form of Gaussian distribution in the FG framework. A new 3-D RSSD-based FG model is constructed with the relationship between RSSD and location coordinates by local linearization technique. The soft-information computation and iterative process of the proposed model are derived by using the sum-product algorithm. In addition, the impacts of different grid distances and number of signal receivers on positioning accuracy are explored. Finally, the performance of our proposed approach is experimentally evaluated in a real scenario. The results show that the positioning performance of the proposed algorithm is not only superior to the k-nearest neighbors (kNN) algorithm and least square (LS) algorithm, but also it can achieve a mean localization error as low as 1.15 m. Our proposed scheme provides a good solution for the accurate detection of an unknown radio transmitter under indoor 3-D space and has a good application prospect.

scholarly journals Research on the Minimum Size of Received Signal Strength Difference Localization Network

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Fangli Ma ◽  
Yang Xu ◽  
Peng Xu
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Minimum Size ◽  
Measured Signal ◽  
Monitoring Networks ◽  
Angle Of Arrival ◽  
Localization Accuracy ◽  
Radio Monitoring ◽  
Strength Difference ◽  
Monitoring Stations

AbstractThe received signal strength difference (RSSD) localization is a kind of method to locate emission sources by measuring the differences of received signal strength level between the monitoring stations and is essentially the truth value ratios of measured signal strength. In the existing literatures, only the rule of RSSD localization circle of two monitoring stations and the geometric relation of RSSD localization circle of five monitoring stations were analyzed, but the number and the station layout of the minimum RSSD localization network have not been investigated. In the present work, first, based on the existing RSSD localization equation, the constants of the commonly used wave propagation models are provided. Then, the minimum RSSD localization network is proved through algebraic analysis, which is that four monitoring stations not distributed on a straight line can locate the signal source at one point. The relationship between the localization accuracy and the signal strength error of the RSSD location network with different scales is studied further and formulated as a nonlinear programming optimization problem. It is found that the localization stability of the network with four stations is poor, and there is a serious localization deviation outlier phenomenon. Therefore, the network with four stations is not available for radio monitoring networks with a signal strength error of ± 5 to  ± 10 dB. The RSSD network with five stations is basically the minimum available size, and the RSSD network with nine stations can approach the localization accuracy of the angle of arrival (AOA) network with three stations.

scholarly journals Longitude and Latitude Based Received Signal Strength Difference Localization Models

2021 ◽  
Author(s):  
Fangli Ma ◽  
Yang Xu ◽  
Peng Xu
Keyword(s):  
Measurement Errors ◽  
Weak Localization ◽  
Signal Strength ◽  
Middle Latitude ◽  
Received Signal Strength ◽  
Projection Model ◽  
Good Convergence ◽  
Distance Calculation ◽  
Localization Errors ◽  
Strength Difference

Abstract In order to use the latitude and longitude coordinates for received signal strength difference (RSSD) localization, the errors of several spherical distance calculation methods and the error of arc length relative to string length were compared. The distance-calculation RSSD localization equations were established, including spherical accurate calculation RSSD, spherical approximate calculation RSSD, and normal cylindrical projection RSSD. And then, the optimization RSSD localization models based on geodetic coordinates and corresponding to the above equations were established, and the models were verified using the point by point search method with good convergence. The numerical results show there are a lot of weak localization areas for the RSSD localization networks lack of central stations with 4,5,6 stations. Among networks with central stations, there are only a small number of weak-localization areas for the concave 4 stations network, while there are no weak-localization areas for the networks composed of more stations. When the measurement errors and the additional losses of radio wave propagation are not considered, the localization errors of the spherical accurate model, the spherical approximate model and the equianglular projection model are very small, among which the second model has the shortest localization time. The localization errors of equidistance projection model and equal-area projection model are large, neither of which is suitable for the middle latitude and high latitude areas.

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