A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arr...A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arrival（FDOA） measurements of a signal received at a number of receivers.The maximum likelihood（ML） technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares（WLS） approach,which makes it possible to attain the Cramér-Rao lower bound（CRLB） at a sufficiently high noise level before the threshold effect occurs.展开更多
This paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival （TDOA） and frequency difference of arrival （FDOA）...This paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements obtained at satellites. The number of satellites available for the geolocation task is more than sufficient and their locations are subject to random errors. This paper derives the constrained Cramor-Rao lower bound （CCRLB） of the target position, and on the basis of the CCRLB analysis, an approximately efficient constrained maximum likelihood estimator （CMLE） for geolocating the target is established. A new iterative algorithm for solving the CMLE is then proposed, where the updated target position estimate is shown to be the globally optimal solution to a generalized trust region sub-problem （GTRS） which can be found via a simple bisection search. First-order mean square error （MSE） analysis is conducted to quantify the performance degradation when the known target altitude is assumed to be precise but indeed has an unknown but deterministic error. Computer simulations are used to compare the performance of the proposed iterative geolocation technique with those of two benchmark algorithms. They verify the approximate efficiency of the proposed algorithm and the validity of the MSE analysis.展开更多
This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time ...This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival(TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization(PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram′er-Rao lower bound(CRLB) and quickly achieve the global optimal solutions.展开更多
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 fr...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.展开更多
Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) u...Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) unifies near-field and farfield models, alleviating the thresholding effect. Current localization methods in MPR based on the angle of arrival (AOA) and time difference of arrival (TDOA) measurements resort to semidefinite relaxation (SDR) and Gauss-Newton iteration, which are computationally complex and face the possible diverge problem. This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem, namely hybrid constrained optimization(HCO). HCO attains Cramér-Rao bound (CRB)-level accuracy for mild Gaussian noise. Compared with the existing closed-form solutions for the hybrid TDOA-AOA case, HCO provides comparable performance to the hybrid generalized trust region subproblem (HGTRS) solution and is better than the hybrid successive unconstrained minimization (HSUM) solution in large noise region. Its computational complexity is lower than that of HGTRS. Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator (MLE) attains if the noise is small, but the MLE deviates from CRB earlier.展开更多
In this paper,we consider the double-satellite localization under the earth ellipsoid model of the Wideband Geodetic System(WGS-84)using the Time Difference of Arrival(TDOA)and the Angle-of-Arrival(AOA).Several closed...In this paper,we consider the double-satellite localization under the earth ellipsoid model of the Wideband Geodetic System(WGS-84)using the Time Difference of Arrival(TDOA)and the Angle-of-Arrival(AOA).Several closed-form solution algorithms via the pseudolinearization of the measurement equations are presented to efficiently estimate the location.These algorithms include the Weighted Least Squares(WLS),the Constrained Total Least Squares(CTLS),and the Taylor-Series Iteration(TSI).Performance comparison of the proposed methods with the Cramér-Rao Lower Bound(CRLB)in the simulation is shown to demonstrate that the proposed algorithms are feasible and have stable performance.展开更多
To solve the problem of time difference of arrival(TDOA)positioning and tracking of targets by the unmanned aerial vehicles(UAV)swarm in future air combat,this paper adopts the TDOA positioning method and uses time di...To solve the problem of time difference of arrival(TDOA)positioning and tracking of targets by the unmanned aerial vehicles(UAV)swarm in future air combat,this paper adopts the TDOA positioning method and uses time difference sensors of the UAV swarm to locate target radiation sources.Firstly,a TDOA model for the target is set up for the UAV swarm under the condition that the error variance varies with the received signal-to-noise ratio.The accuracy of the positioning error is analyzed by geometric dilution of precision(GDOP).The D-optimality criterion of the positioning model is theoretically derived.The target is positioned and settled,and the maximum value of the Fisher information matrix determinant is used as the optimization objective function to optimize the track of the UAV in real time.Simulation results show that the track optimization improves the positioning accuracy and stability of the UAV swarm to the target.展开更多
The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA position...The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle(UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision(GDOP) factor. Second, the Cramer-Rao lower bound(CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.展开更多
In the underwater medium,the speed of sound varies with water depth,temperature,and salinity.The inhomogeneity of water leads to bending of sound rays,making the existing localization algorithms based on straightline ...In the underwater medium,the speed of sound varies with water depth,temperature,and salinity.The inhomogeneity of water leads to bending of sound rays,making the existing localization algorithms based on straightline propagation less precise.To realize high-precision node positioning in underwater acoustic sensor networks(UASNs),a multi-layer isogradient sound speed profile(SSP)model is developed using the linear segmentation approximation approach.Then,the sound ray tracking problem is converted into a polynomial root-searching problem.Based on the derived gradient of the signal’s Doppler shift at the sensor node,a novel underwater node localization algorithm is proposed using both the time difference of arrival(TDOA)and frequency difference of arrival(FDOA).Simulations are implemented to illustrate the effectiveness of the proposed algorithm.Compared with the traditional straight-line propagation method,the proposed algorithm can effectively handle the sound ray bending phenomenon.Estimation accuracy with different SSP modeling errors is also investigated.Overall,accurate and reliable node localization can be achieved.展开更多
Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival(DOA) and time difference of arrival(TDOA) system is deri...Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival(DOA) and time difference of arrival(TDOA) system is derived and it is shown that this hybrid system may inferior to the single system when the ratio of angular measurements error to distance measurements error exceeds a threshold. To avoid this problem, an effective DOA/TDOA adaptive cascaded(DTAC) technique is presented. The rotation feature of UAVs and spatial filtering technique are applied to gain the signal-to-noise ratio(SNR), which leads to more accurate estimation of time delay by using DOAs. Nevertheless, the time delay estimation precision is still limited by the sampling frequency, which is constrained by the finite load of UAV. To break through the limitation, an enhanced self-delay-compensation(SDC) method is proposed, which aims at detecting the overlooked time delay within the sampling interval by adding a tiny time delay. Finally, the position of the source is estimated by the Chan algorithm. Compared to DOA-only algorithm, TDOA-only algorithm and joint DOA/TDOA(JDT) algorithm, the proposed method shows better localization accuracy regardless of different SNRs and sampling frequencies. Numerical simulations are presented to validate the effectiveness and robustness of the proposed algorithm.展开更多
The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute th...The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.展开更多
To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)metho...To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)method to obtain the initial TDE.Secondly,a signal model using normalized cross spectrum is established,and the noise subspace is extracted by eigenvalue decomposition(EVD)of covariance matrix.Using the orthogonal relation between the steering vector and the noise subspace,the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE.Finally,the offsets are compensated via simple least squares(LS).Compared to other state-of-the-art methods,the proposed method significantly reduces the computational complexity and achieves better estimation performance.Experiments on both simulation and real-world data verify the efficiency of the proposed approach.展开更多
Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWL...Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.展开更多
For Time Difference Of Arrival(TDOA) location based on multi-ground stations scene,two direct solution methods are proposed to solve the target position in TDOA location.Therein,the solving methods are realized in the...For Time Difference Of Arrival(TDOA) location based on multi-ground stations scene,two direct solution methods are proposed to solve the target position in TDOA location.Therein,the solving methods are realized in the rectangular and polar coordinates.On the condition of rectangular coordinates,first of all,it solves the radial range between the target and reference station,then cal-culates the location of the target.In the case of polar coordinates,the azimuth between the target and reference station is solved first,then the radial range between the target and reference station is figured out,finally the location of the target is obtained.Simultaneously,the simulation and comparison analysis are given in detail,and show that the polar solving method has the better fuzzy performance than that of rectangular coordinate.展开更多
For the frequency difference of arrival （FDOA） esti-mation in passive location, this paper transforms the frequency difference estimation into the radial velocity difference estimation, which is difficult to achieve...For the frequency difference of arrival （FDOA） esti-mation in passive location, this paper transforms the frequency difference estimation into the radial velocity difference estimation, which is difficult to achieve a high accuracy due to the mismatch between the sampling period and the pulse repetition interval. The proposed algorithm firstly estimates the point-in-time that each pulse arrives at two receivers accurately. Secondly two time of arrival （TOA） sequences are subtracted. And final y the radial ve-locity difference of a target relative to two stations with the least square method is estimated. This algorithm only needs accurate estimation of the time delay between pulses and is not influenced by parameters such as frequency and modulation mode. It avoids transmitting a large amount of data between two stations in real time. Simulation results corroborate that the performance is bet-ter than the arithmetic average of the Cramer-Rao lower bound （CRLB） for monopulse under suitable conditions.展开更多
High accuracy seamless positioning is required to support a vast number of applications in varying operational environments.Over the last few years,the global positioning system(GPS)has become the de facto technology ...High accuracy seamless positioning is required to support a vast number of applications in varying operational environments.Over the last few years,the global positioning system(GPS)has become the de facto technology for positioning applications.However,its performance is limited in indoor and dense urban environments due to multipath as well as signal attenuation and blockage.A number of techniques integrating GPS with other positioning technologies have been developed to address the limitations of standalone GPS in these difficult environments.While most of the developed techniques cover the outages of GPS in such environments,they do not provide acceptable performance,in terms of positioning accuracy,especially for some mission-critical(e.g.safety)applications.This paper proposes a tightly coupled(i.e.in the measurement domain)GPS/WiFi integration method which,in addition to addressing GPS outages,improves the overall positioning accuracy to the meter-level,thus satisfying the requirements of a number of location based services and intelligent transport systems applications.The performance of the proposed GPS/WiFi integration method is assessed for a number of scenarios in a simulation environment for an identified dense urban area in London,UK.展开更多
基金National High-tech Research and Development Program of China (2010AA7010422,2011AA7014061)
文摘A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arrival（FDOA） measurements of a signal received at a number of receivers.The maximum likelihood（ML） technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares（WLS） approach,which makes it possible to attain the Cramér-Rao lower bound（CRLB） at a sufficiently high noise level before the threshold effect occurs.
基金co-supported by the National Natural Science Foundation of China (Nos. 61304264 and 61305017)the Innovation Foundation of Industry, Education and Research of Jiangsu Province (No. BY2014023-25)
文摘This paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements obtained at satellites. The number of satellites available for the geolocation task is more than sufficient and their locations are subject to random errors. This paper derives the constrained Cramor-Rao lower bound （CCRLB） of the target position, and on the basis of the CCRLB analysis, an approximately efficient constrained maximum likelihood estimator （CMLE） for geolocating the target is established. A new iterative algorithm for solving the CMLE is then proposed, where the updated target position estimate is shown to be the globally optimal solution to a generalized trust region sub-problem （GTRS） which can be found via a simple bisection search. First-order mean square error （MSE） analysis is conducted to quantify the performance degradation when the known target altitude is assumed to be precise but indeed has an unknown but deterministic error. Computer simulations are used to compare the performance of the proposed iterative geolocation technique with those of two benchmark algorithms. They verify the approximate efficiency of the proposed algorithm and the validity of the MSE analysis.
基金the National Natural Science Foundation of China(No.61571146)the Fundamental Research Funds for the Central Universities of China(No.HEUCFP201769)
文摘This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival(TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization(PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram′er-Rao lower bound(CRLB) and quickly achieve the global optimal solutions.
基金supported by the Fundamental Research Funds for the Central Universities(2082604194194)
文摘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.
基金supported by the National Natural Science Foundation of China (62101359)Sichuan University and Yibin Municipal People’s Government University and City Strategic Cooperation Special Fund Project (2020CDYB-29)+1 种基金the Science and Technology Plan Transfer Payment Project of Sichuan Province (2021ZYSF007)the Key Research and Development Program of Science and Technology Department of Sichuan Province (2020YFS0575,2021KJT0012-2 021YFS-0067)。
文摘Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) unifies near-field and farfield models, alleviating the thresholding effect. Current localization methods in MPR based on the angle of arrival (AOA) and time difference of arrival (TDOA) measurements resort to semidefinite relaxation (SDR) and Gauss-Newton iteration, which are computationally complex and face the possible diverge problem. This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem, namely hybrid constrained optimization(HCO). HCO attains Cramér-Rao bound (CRB)-level accuracy for mild Gaussian noise. Compared with the existing closed-form solutions for the hybrid TDOA-AOA case, HCO provides comparable performance to the hybrid generalized trust region subproblem (HGTRS) solution and is better than the hybrid successive unconstrained minimization (HSUM) solution in large noise region. Its computational complexity is lower than that of HGTRS. Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator (MLE) attains if the noise is small, but the MLE deviates from CRB earlier.
基金supported by Meteorological information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes of Chengdu University of Information Technology,China(No.QXXCSYS201702)
文摘In this paper,we consider the double-satellite localization under the earth ellipsoid model of the Wideband Geodetic System(WGS-84)using the Time Difference of Arrival(TDOA)and the Angle-of-Arrival(AOA).Several closed-form solution algorithms via the pseudolinearization of the measurement equations are presented to efficiently estimate the location.These algorithms include the Weighted Least Squares(WLS),the Constrained Total Least Squares(CTLS),and the Taylor-Series Iteration(TSI).Performance comparison of the proposed methods with the Cramér-Rao Lower Bound(CRLB)in the simulation is shown to demonstrate that the proposed algorithms are feasible and have stable performance.
基金This work was supported by the National Natural Science Foundation of China(61502522)the Equipment Pre-Research Field Fund(JZX7Y20190253036101)+1 种基金the Equipment Pre-Research Ministry of Education Joint Fund(6141A02033703)the Hubei Provincial Natural Science Foundation(2019CFC897).
文摘To solve the problem of time difference of arrival(TDOA)positioning and tracking of targets by the unmanned aerial vehicles(UAV)swarm in future air combat,this paper adopts the TDOA positioning method and uses time difference sensors of the UAV swarm to locate target radiation sources.Firstly,a TDOA model for the target is set up for the UAV swarm under the condition that the error variance varies with the received signal-to-noise ratio.The accuracy of the positioning error is analyzed by geometric dilution of precision(GDOP).The D-optimality criterion of the positioning model is theoretically derived.The target is positioned and settled,and the maximum value of the Fisher information matrix determinant is used as the optimization objective function to optimize the track of the UAV in real time.Simulation results show that the track optimization improves the positioning accuracy and stability of the UAV swarm to the target.
基金supported by the National Natural Science Foundation of China (61502522)Equipment Pre-Research Field Fund(JZX7Y20190253036101)+1 种基金Equipment Pre-Research Ministry of Education Joint Fund (6141A02033703)Hubei Provincial Natural Scie nce Foundation (2019CFC897)。
文摘The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle(UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision(GDOP) factor. Second, the Cramer-Rao lower bound(CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.
基金Project supported by the National Key Research and Development Program of China(No.2017YFC0305900)the Zhejiang University K.P.Chao’s High Technology Development Foundation(No.2020ZL013)。
文摘In the underwater medium,the speed of sound varies with water depth,temperature,and salinity.The inhomogeneity of water leads to bending of sound rays,making the existing localization algorithms based on straightline propagation less precise.To realize high-precision node positioning in underwater acoustic sensor networks(UASNs),a multi-layer isogradient sound speed profile(SSP)model is developed using the linear segmentation approximation approach.Then,the sound ray tracking problem is converted into a polynomial root-searching problem.Based on the derived gradient of the signal’s Doppler shift at the sensor node,a novel underwater node localization algorithm is proposed using both the time difference of arrival(TDOA)and frequency difference of arrival(FDOA).Simulations are implemented to illustrate the effectiveness of the proposed algorithm.Compared with the traditional straight-line propagation method,the proposed algorithm can effectively handle the sound ray bending phenomenon.Estimation accuracy with different SSP modeling errors is also investigated.Overall,accurate and reliable node localization can be achieved.
基金co-supported by China Scholarship Council(201806830081)National science foundation of China(61827801,61371169,61601167,61601504)+3 种基金Jiangsu NSF(BK20161489)the open research fund of State Key Laboratory of Millimeter Waves,Southeast University(No.K201826)the Fundamental Research Funds for the Central Universities(NO.NE2017103and NT2019013)the postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX18_0293).
文摘Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival(DOA) and time difference of arrival(TDOA) system is derived and it is shown that this hybrid system may inferior to the single system when the ratio of angular measurements error to distance measurements error exceeds a threshold. To avoid this problem, an effective DOA/TDOA adaptive cascaded(DTAC) technique is presented. The rotation feature of UAVs and spatial filtering technique are applied to gain the signal-to-noise ratio(SNR), which leads to more accurate estimation of time delay by using DOAs. Nevertheless, the time delay estimation precision is still limited by the sampling frequency, which is constrained by the finite load of UAV. To break through the limitation, an enhanced self-delay-compensation(SDC) method is proposed, which aims at detecting the overlooked time delay within the sampling interval by adding a tiny time delay. Finally, the position of the source is estimated by the Chan algorithm. Compared to DOA-only algorithm, TDOA-only algorithm and joint DOA/TDOA(JDT) algorithm, the proposed method shows better localization accuracy regardless of different SNRs and sampling frequencies. Numerical simulations are presented to validate the effectiveness and robustness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(61101173)
文摘The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.
基金supported in part by National Key R&D Program of China under Grants 2020YFB1807602 and 2020YFB1807600National Science Foundation of China(61971217,61971218,61631020,61601167)+1 种基金the Fund of Sonar Technology Key Laboratory(Range estimation and location technology of passive target viamultiple array combination),Jiangsu Planned Projects for Postdoctoral Research Funds(2020Z013)China Postdoctoral Science Foundation(2020M681585).
文摘To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)method to obtain the initial TDE.Secondly,a signal model using normalized cross spectrum is established,and the noise subspace is extracted by eigenvalue decomposition(EVD)of covariance matrix.Using the orthogonal relation between the steering vector and the noise subspace,the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE.Finally,the offsets are compensated via simple least squares(LS).Compared to other state-of-the-art methods,the proposed method significantly reduces the computational complexity and achieves better estimation performance.Experiments on both simulation and real-world data verify the efficiency of the proposed approach.
基金supported by the National Natural Science Foundation of China(61201282)the Science and Technology on Communication Information Security Control Laboratory Foundation(9140C130304120C13064)
文摘Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.
基金Supported by the National Natural Science Foundation of China (No. 60825104,61072107)the National Postdoctor Fundation (No. 20090451251)+1 种基金the Shaanxi Industry Surmount Foundation (2009K08-31)the Fundamental Research Funds for the Central Universities(JY10000-902025) of China
文摘For Time Difference Of Arrival(TDOA) location based on multi-ground stations scene,two direct solution methods are proposed to solve the target position in TDOA location.Therein,the solving methods are realized in the rectangular and polar coordinates.On the condition of rectangular coordinates,first of all,it solves the radial range between the target and reference station,then cal-culates the location of the target.In the case of polar coordinates,the azimuth between the target and reference station is solved first,then the radial range between the target and reference station is figured out,finally the location of the target is obtained.Simultaneously,the simulation and comparison analysis are given in detail,and show that the polar solving method has the better fuzzy performance than that of rectangular coordinate.
基金supported by the National Natural Science Foundationof China(61201208)
文摘For the frequency difference of arrival （FDOA） esti-mation in passive location, this paper transforms the frequency difference estimation into the radial velocity difference estimation, which is difficult to achieve a high accuracy due to the mismatch between the sampling period and the pulse repetition interval. The proposed algorithm firstly estimates the point-in-time that each pulse arrives at two receivers accurately. Secondly two time of arrival （TOA） sequences are subtracted. And final y the radial ve-locity difference of a target relative to two stations with the least square method is estimated. This algorithm only needs accurate estimation of the time delay between pulses and is not influenced by parameters such as frequency and modulation mode. It avoids transmitting a large amount of data between two stations in real time. Simulation results corroborate that the performance is bet-ter than the arithmetic average of the Cramer-Rao lower bound （CRLB） for monopulse under suitable conditions.
文摘High accuracy seamless positioning is required to support a vast number of applications in varying operational environments.Over the last few years,the global positioning system(GPS)has become the de facto technology for positioning applications.However,its performance is limited in indoor and dense urban environments due to multipath as well as signal attenuation and blockage.A number of techniques integrating GPS with other positioning technologies have been developed to address the limitations of standalone GPS in these difficult environments.While most of the developed techniques cover the outages of GPS in such environments,they do not provide acceptable performance,in terms of positioning accuracy,especially for some mission-critical(e.g.safety)applications.This paper proposes a tightly coupled(i.e.in the measurement domain)GPS/WiFi integration method which,in addition to addressing GPS outages,improves the overall positioning accuracy to the meter-level,thus satisfying the requirements of a number of location based services and intelligent transport systems applications.The performance of the proposed GPS/WiFi integration method is assessed for a number of scenarios in a simulation environment for an identified dense urban area in London,UK.
