This paper investigates the constrained weighted least squares (CWLS) source localization problem by using time difference of arrival and frequency difference of arrival measurements. The problem can be formulated as a quadratic programming with two indefinite quadratic equality constraints, which is nonconvex and NP-hard. Moreover, the weighting matrix is coupled with the unknown source position and velocity. We propose an iterative CWLS method that can efficiently solve this problem. It iteratively performs a linearization procedure on the quadratic equality constraints to obtain an approximate programming with linear constraints, which can be analytically solved, and the weighting matrix is updated in each iteration. Theoretical analysis reveals that the proposed method, if converges, can lead to the global optimal solution of the formulated problem that reaches the CRLB accuracy under mild assumptions on the measurement noises. The Monte Carlo simulation results indicate that the percentage of convergence within 20 iterations is more than 96%, and the localization accuracy is significantly improved over the previous methods with less computation time requirement. Moreover, it is found from simulations that the iterative CWLS method retains acceptable performance even under the ill-conditioned situation when the sensor geometry is not desirable.

Accurate vehicle position is crucial to vehicular Ad-Hoc network (VANET) applications. The wildly used global navigation satellite systems (GNSS) have limited localization accuracy, especially when GNSS signals are blocked or contaminated with reflected signals. Cooperative localization based on multi-vehicle information fusion will be the core component in VANET in the near future. This paper strives to enhance the localization accuracy using the fusion of GNSS and relative range information in static scenes. The formulation following the Maximum-Likelihood strategy is a quadratic non-convex programming, which is NP-hard. An iterative localization algorithm is proposed, where an approximate quadratic programming with linear constraints is solved in each iteration. Additional inspection steps and correction step are designed to ensure the convergence and feasibility of the proposed algorithm. The theoretical analysis shows that the limit of the proposed algorithm must be a global optimal solution of the formulated problem and the mean squared error can reach the Cramér-Rao lower bound. In terms of localization accuracy, the proposed method outperforms the existing linearized weighted least-squares method and semidefinite relaxation method, while exhibiting moderate computational complexity.

Ambient radio frequency (RF) energy harvesting technology provides a hopeful solution to power the low-power electronic system. Nevertheless, the extremely low power density and randomness of ambient RF energy pose a significant obstacle to the application of ambient RF energy harvesting technology. Aiming at wireless transmission by ambient RF-powered sensors, this paper presents a cooperative communication technology by combining ambient backscattering and energy beamforming to significantly decrease the transmission energy consumption of each sensor. For wireless transmission via this cooperative communication technology, a novel optimization problem is formulated to maximize the long-term time-averaged sensed data size, which jointly takes into account sensing scheduling, transmission power allocation and the control of sensed data size. Moreover, we employ techniques of Lyapunov drift and Lyapunov optimization to propose an online optimal control algorithm, which achieves the optimal solution to the proposed problem without requiring a-priori knowledge of harvested energy and wireless channels. By exploiting mixed integer programming (MIP) and signomial geometric programming (SGP) that cause the proposed problem difficult to solve, a novel Sequential Convex Approximation algorithm is further introduced into the online optimal control algorithm. Simulation results indicate the proposed online algorithm offers a dramatic performance gain over alternative online algorithms.

Intelligent reflecting surface (IRS) provides a promising technology that can improve the energy efficiency of wireless communications, by building the controllable wireless propagation environment with the utilization of massive passive reflecting elements. Motivated by this, we apply the IRS to assist the data collection based on collaborative beamforming in Wireless Sensor Networks (WSNs). With the objective to maximize the network lifetime, we present an energy efficient joint collaborative and passive beamforming design for an IRS-assisted WSN, which jointly optimizes the beamforming vector for all nodes and phase shifts for IRS’s reflecting elements. To efficiently resolve the joint optimization problem, we develop a penalty dual decomposition (PDD) based algorithm combined with augmented Lagrangian method, penalty-based method and block coordinate descent algorithm, which decomposes the joint optimization problem into four simplified subproblems. Especially in solving IRS’s phase shifts optimization subproblem, we formulate this subproblem as a quadratically constrained quadratic programming (QCQP) problem and further propose a low computational complexity approximate iteration algorithm to resolve this QCQP problem, which iteratively linearizes this QCQP problem to a sequence of approximate programming with linear equality constraints, whose closed-form solution is presented. Theoretical analysis indicates the proposed algorithm can converge to a KKT solution. Simulation results indicate that the IRS-assisted design and proposed algorithm improve the energy efficiency and prolong the network lifetime as compared to alternative algorithms.

This paper investigates the nonlinear unscented Kalman filtering (UKF) problem for discrete nonlinear dynamic systems with random parameters. We develop an improved unscented transformation by incorporating the random parameters into the state vector to enlarge the number of sigma points. The theoretical analysis reveals that the approximated mean and covariance via the improved unscented transformation match the true values correctly up to the third order of Taylor series expansion. Based on the improved unscented transformation, an improved UKF method is proposed to expand the application of the UKF for nonlinear systems with random parameters. An application to the mobile source localization with time difference of arrival (TDOA) measurements and sensor position uncertainties is provided where the simulation results illustrate that the improved UKF method leads to a superior performance in comparison with the normal UKF method.

Accurate vehicle positioning is critical for many Intelligent Transportation Systems (ITS) applications. However, Global Navigation Satellite Systems (GNSS) often face significant challenges in various demanding scenarios. This paper explores cooperative vehicle localization in GNSS-denied environments by utilizing a fusion of relative distance and angle of arrival information gathered from vehicles and roadside Ultra-Wideband (UWB) base stations with known coordinates. In dense traffic situations, UWB base stations may experience Non-Line-of-Sight (NLOS) effects when localizing vehicles. To tackle this issue, we propose a strategy that begins by classifying vehicles into Line-of-Sight (LOS) and NLOS categories based on distance and angle of arrival measurements, as well as discrepancies observed between two different localization methods. Initially, we estimate the positions of LOS-vehicles, which are then used to determine the positions of NLOS-vehicles through the distance information connecting LOS and NLOS-vehicles. Once the preliminary positions of all vehicles are established, we employ an iterative optimization algorithm to refine these estimates, ultimately yielding more accurate localization outcomes. Simulation experiments indicate that the proposed method demonstrates excellent localization performance in both static and dynamic positioning scenarios.