The omnipresent signals of opportunity (SOOP) enable an effective way for passive target tracking using multiple agents. However, cooperative target tracking via non-cooperative SOOP is challenging since the positions of agents are not precisely known. In this paper, we determine the performance bounds of cooperative tracking using SOOP by multiple asynchronous agents equipped with antenna arrays. The Fisher information matrix of joint target and agent positions can be decomposed as the sum of the information from SOOP and self-localization networks, where the correlation of signals introduces an additional Fisher information component and the multipath effect is characterized by path-overlap coefficients. We demonstrate how the location information coupling between the target and agents affects localization accuracy and how the cooperation among agents improves tracking performance. Moreover, the angular information is shown to mitigate multipath and asynchronous effects by spatiotemporal separation and time-independent measurements, respectively. Then we propose a distributed hybrid belief propagation based algorithm for cooperative tracking and network synchronization via likelihood consensus. Finally, numerical results validate our theoretical analysis and the performance of the proposed algorithm.

Accurate wireless positioning of mobile agents is challenging in non-line-of-sight (NLOS) propagation environments due to unknown range or angle biases. In this paper, we develop a cooperative localization algorithm for mixed line-of-sight (LOS)/NLOS environments where the NLOS effect is mitigated by exploiting the geometric relationship of the range biases. In particular, we cast the localization problem as a detection-aided optimization program, in which all the distance measurements are initially treated as NLOS links with unknown nonnegative biases, followed by iterative agent position estimation and LOS identification. Moreover, the maximum-likelihood estimator for the agent positions and NLOS biases is relaxed into a semidefinite program where the geometric relationship of the biases is introduced as constraints. We also characterize the cooperation gain for LOS identification, and derive the constrained Cramér-Rao bound to show the localization accuracy improvement by the geometric constraints. Finally, numerical results validate the superior performance of the proposed algorithm compared with other competitive methods.

Target localization using signals of opportunity (SOOP) suffers performance degradation due to location uncertainties of mobile agents. In this paper, we investigate the performance limits of cooperative target localization in array networks using SOOP. The Fisher information matrix for target and agent positions is derived as a weighted sum of delay and angle information. The former is proportional to the effective bandwidth, and the latter is proportional to the product of the carrier frequency and geometric factor. In addition, we demonstrate how the cooperation among agents improves target localization performance, and how the information coupling between the target and agents affects localization accuracy. Then a general distance resolution limit based on the Fisher information analysis is derived as the metric for resolution performance. Finally, numerical results are provided to validate our theoretical analyses.

Source localisation withtime‐difference‐of‐arrival (TDOA) and frequency‐difference‐of‐arrival (FDOA)measurements is of great interest since it can provide the location information with highaccuracy.Although the maximumlikelihood (ML) estimator exhibits excellent asymptotic properties, the non‐linearity and non‐convexity of ML estimator requiremuch computation resources.In this study, source localisation with TDOA and FDOA measurementsis developed viaMonteCarlo importance sampling (IS).In particular, the optimalperformance can be guaranteed by constructing an optimalimportance function whosecovariance is equivalent to the inverse of Fisher information matrix.The derived variance of the proposed estimator showsgood consistency with the theoretical lowerbound. The improved performance of the proposed method is due to the optimal selection ofimportance function and it canconverge to the global optimum with a large number of samples. Although an initial estimate of source localisation information isrequired, the proposedmethod is robust to this a priori knowledge via IS. Moreover, the scenario ofconsidering sensor location uncertainties is analysed and the corresponding IS based solution is derived. Simulation results show that the proposed methods can achieve the Cramér–Rao lower bound at moderate level noises and is superior to several existing methods.

Single-station passive localization technology avoids the complex time synchronization and information exchange between multiple observatories, and is increasingly important in electronic warfare. Based on a single moving station localization system, a new method with high localization precision and numerical stability is proposed when the measurements from multiple disjoint sources are subject to the same station position and velocity displacement. According to the available measurements including the angle-of-arrival (AOA), time-of-arrival (TOA), and frequency-of-arrival (FOA), the corresponding pseudo linear equations are deduced. Based on this, a structural total least squares (STLS) optimization model is developed and the inverse iteration algorithm is used to obtain the stationary target location. The localization performance of the STLS localization algorithm is derived, and it is strictly proved that the theoretical performance of the STLS method is consistent with that of the constrained total least squares method under first-order error analysis, both of which can achieve the Cramér-Rao lower bound accuracy. Simulation results show the validity of the theoretical derivation and superiority of the new algorithm.

The emerging technologies in 5G network not only bring revolutions in communications, but also promise new solutions in wireless localization. In this paper, we investigate how large-scale arrays impact the localization performance and derive the accuracy bound for wireless localization with multiple-input multiple-output (MIMO) systems. We then prove that when the number of array elements goes large, the multipath effects will vanish due to the asymptotic orthogonality of spatial channels in MIMO localization. The multipath mitigation can also be interpreted as the temporal and spatial separation supported by the signal bandwidth and the number of antennas, respectively. Finally, numerical results are given to validate our analysis.

Passive source localization via a maximum likelihood (ML) estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper, we use the Pincus theorem and Monte Carlo importance sampling (MCIS) to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function (PDF) of Gaussian distribution, which is called an important function for efficient sampling, to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is attributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation, but an initial solution estimation is required resulting from Taylor series expansion. However, the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cramér-Rao lower bound at a moderate Gaussian noise level and outperforms the existing methods.

Determining the position of an emitter on Earth by using a satellite cluster has many important applications, such as in navigation, surveillance, and remote sensing. However, in realistic situations, a number of factors, such as errors in the measurement of signal parameters, uncertainties regarding the position of satellites, and errors in the location of calibration sources, are known to degrade the accuracy of target localization in satellite geolocation systems. We systematically analyze the performance of multi-satellite joint geolocation based on time difference of arrival (TDOA) measurements. The theoretical analysis starts with Cramér–Rao bound (CRB) derivations for four localization scenarios under an altitude constraint and Gaussian noise assumption. In scenario 1, only the TDOA measurement errors of the emitting source are considered and the satellite positions are assumed to be perfectly estimated. In scenario 2, both the TDOA measurement errors and satellite position uncertainties are taken into account. Scenario 3 assumes that some calibration sources with accurate position information are used to mitigate the influence of satellite position perturbations. In scenario 4, several calibration sources at inaccurate locations are used to alleviate satellite position errors in target localization. Through comparing the CRBs of the four localization scenarios, some valuable’s insights are gained into the effects of various error sources on the estimation performance. Two kinds of location mean-square errors (MSE) expressions under the altitude constraint are derived through first-order perturbation analysis and the Lagrange method. The first location MSE provides the theoretical prediction when an estimator assumes that the satellite locations are accurate but in fact have errors. The second location MSE provides the localization accuracy if an estimator assumes that the known calibration source locations are precise while in fact erroneous. Simulation results are included to verify the theoretical analysis.