Existing integrated sensing and communication technology designed for monostatic settings segregates sensing and communication (S&C) at distinct terminals, impeding their adaptability to network paradigms. In this paper, we propose a general framework for joint target-localization and data-detection (JTD) in bistatic settings by using a combination of the deterministic known (DK) and random unknown (RU) symbols, e.g., pilot and data symbols. We first derive the performance limits for target localization-related parameters with combined symbols. We demonstrate that the Fisher information matrix (FIM) for target localization using the DK part can be expressed in a closed form through orthogonal projection, while that using the RU part aligns with the modified FIM in monostatic settings at an exponential rate with respect to the signal-to-noise ratio. Then, we propose a JTD scheme to exploit the reciprocal advantages for S&C, i.e., the detected data symbols are harnessed to enhance target localization in an alternating way. Finally, simulation results validate our theoretical analysis and the effectiveness of the proposed JTD scheme.

Reliable and accurate localization awareness is of great importance for the distributed multi-agent system (DMAS). Instead of global information, measurements only between neighbors pose locatability and accuracy challenges for distributed systems, which leads to the development and application of relative localization. In this paper, we put forward a backbone-listener localization scheme for the D-MAS. Agents switch backbone-listener modes through a node selection strategy. Position and orientation angle of agents are jointly estimated by range and angle information fusion. A distributed multidimensional scaling method is proposed for backbone agents to maintain the topology estimation. And listener agents ensure the localization capacity through a least square range and angle fusion algorithm. Extensive simulation and real-world experiments validate that our method achieves decimeter-level accuracy relative localization.

Accurate location information is essential for many emerging applications of the Internet of Things. Compared with absolute positions, relative positions of nodes are often more relevant in tasks such as formation control and autonomous driving. In this article, we propose a relative localization scheme for terminals aided by unmanned aerial vehicles. We first derive the performance limit of the relative position error as the constrained Cramér-Rao lower bound (CRLB), and proved that the equivalent Fisher information of a subnetwork retains all the information for its relative localization. We then develop a distributed localization algorithm based on local geometry transforming for the proposed scheme with low computation complexity. Moreover, an iterative descent algorithm is designed for joint power and spectrum allocation for relative localization. Numerical results show that the proposed localization algorithm significantly outperforms existing algorithms and the optimal allocation scheme can reduce the relative CRLB by about 50% compared with the uniform allocation scheme.

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.

Cooperative detection in multi-agent networks has raised increasing concern for a variety of collaborative applications. Thorough research has been developed assuming exact knowledge about the space or space-time signature vector embedded at the received signal in fixed networks, but most of them fail to apply to a mobile scenario with inaccurate agent positions. In this paper, we propose a direct generalized likelihood ratio test (DGLRT) not only taking the position uncertainty of agents into consideration, but also obtaining the optimal steering vector by directly estimating target and agent positions instead of implementing testing under each possible intermediate parameter combination of range and angle-of-arrival (AOA). Furthermore, we conduct performance analysis on the proposed DGLRT and introduce the notation of effective signal-to-noise ratio (SNR) to measure its detection capability, from which the detection performance loss induced by position uncertainty is revealed. Comparison to the conventional detector demonstrates the superior detection performance of the DGLRT based on direct estimation of target and agent positions.

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.

Multiple sources localization based on time difference of arrival (TDOA) measurements is investigated in this paper. Different from the traditional methods, a novel and practical multisource localization algorithm is proposed by adopting a priori information of relative distance among emitting sources. Since the maximum likelihood (ML) cost function for multisource estimation is highly nonconvex, the semidefinite relaxation (SDR) is utilized to reformulate the ML cost function. A robust estimator is obtained, which can be solved by semidefinite programming (SDP). Moreover, the constrained Cramér‐Rao bound is also derived as a benchmark by considering the range constraints between sources. Simulation results verify the superior performance of the proposed algorithm over the traditional methods.

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.