Simultaneously determining the 3-dimensional (3- D) position and orientation of a rigid object is termed Rigid Body Localization (RBL). The RBL has potential applications in systems of virtual reality (VR), spacecraft docking, etc. In this letter, a new RBL scheme based on Direction of Arrival (DoA) measurements is proposed, which needs only a single base station (BS) and has no requirement for time synchronization between the target of interest and the BS. For determining the position and orientation, the rigid object of interest has wireless sensors mounted on its surface, which are distributed with known topology. We firstly build a geometrical model fusing the measured DoAs from the wireless sensors and their known topology to determine the 3-D coordinates of these sensors. Then using the obtained coordinate information, we achieve the RBL by rigid body transform methodologies. The constrained Cramer- Rao bound (CCRB) is derived to evaluate the performance of the developed method in terms of the DoA noise level and the rigid body size.

Rigid body localization (RBL) is to simultaneously estimate the position and attitude of a rigid target. In this paper, we focus on the RBL problem using a single base station (BS) and direction of arrival (DoA) measurements. Several wireless sensors are mounted on the rigid body of interest, and their topology information is known a priori. The single BS measures the DoAs of wireless sensor signals and fuses them with the sensor topology information to estimate the position and orientation of the rigid body and achieve RBL. We propose two RBL methods, namely, the observation matching (OM) algorithm and topology matching (TM) algorithm with refinement. The emerging participatory searching algorithm (PSA) is adopted in both methods to solve the nonlinear matching problems. Simulations show that, compared with the existing approach, the OM method can achieve better RBL accuracy under high DoA noise levels, while the performance of the TM algorithm with refinement is closer to the constrained Cramér–Rao bound (CCRB) under low DoA noise levels.

Device-free localization (DFL) is a technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which limits its application in some resource-limited scenarios. To solve this problem, a geometric midpoint (GM) algorithm based on the computations of simple geometric objects is proposed to realize effective tracking of moving targets in low-density wireless networks. First, we proposed a signal processing method for raw RSS signals collected from wireless links that can detect the fluctuations caused by a moving target effectively. Second, a geometric midpoint algorithm is proposed to estimate the location of the target. Finally, simulations and experiments were performed to validate the proposed scheme. The experimental results show that the proposed GM algorithm outperforms the geometric filter algorithm, which is a state-of-the-art DFL method that yields tracking root-mean-square errors up to 0.86 m and improvements in tracking accuracy up to 67.66%.

Due to the large measurement error in the practical non-cooperative scene, the passive localization algorithms based on traditional numerical calculation using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) often have no solution, i.e., the estimated result cannot meet the localization background knowledge. In this context, this paper intends to introduce interval analysis theory into joint FDOA/TDOA-based localization algorithm. The proposed algorithm uses the dichotomy algorithm to fuse the interval measurement of TDOA and FDOA for estimating the velocity and position of a moving target. The estimation results are given in the form of an interval. The estimated interval must contain the true values of the position and velocity of the radiation target, and the size of the interval reflects the confidence of the estimation. The point estimation of the position and the velocity of the target is given by the midpoint of the estimation interval. Simulation analysis shows the efficacy of the algorithm.

For many data mining applications under the Internet of Things (IoT) environments, the attitude and the position of a rigid target are indispensable and hidden information to be dug. The term Rigid Body Localization (RBL) refers to simultaneously estimation of the position and the attitude of a rigid target. The RBL framework which adopts only one single base station (BS) is considered in this paper for IoT applications. Several wireless sensor nodes with known topology information are fixed on the surface of the rigid target. The single BS fuses the angle of arrival (AoA) measurements from the nodes with the topology information for the RBL purpose. In this paper, we propose a two-stage RBL method to efficiently fusing the aforementioned two pieces of information. Firstly, we built the maximum likelihood estimator (MLE) of the information fusion and adopted the modified Newton’s iteration algorithm (mNIA) to determine the wireless node position; then we used the unit quaternion (UQ) algorithm for estimating the relative position and attitude with respect to the predetermined reference state, which completed the RBL task. Finally, we evaluated the proposed RBL performance in terms of the root mean squared error (RMSE), convergence success rate, as well as the computation costs. Simulation results showe that the proposed mNIA-based RBL algorithm can achieve a finer RBL performance with obviously higher speed and 100 percent success convergence rate, comparing with existing heuristic methods.

Target tracking technologies in wireless sensor network (WSNs) environments fall into two categories: active and passive schemes. Unlike with the active positioning schemes, in which the targets are required to hold cooperative devices, the research on passive tracking, i.e., tracking device-free targets, has recently showed promise. In the WSN, device-free targets can be tracked by sensing radio frequency tomography (RFT) on the line-of-sight links (LOSLs). In this paper, we propose a passive tracking scheme exploiting both adaptive-networking LOSL webs and geometric constraint methodology for tracking single targets, as well as multiple targets. Regarding fundamental knowledge, we firstly explore the spatial diversity technique for RFT detection in realistic situations. Then, we analyze the power consumption of the WSN and propose an adaptive networking scheme for the purpose of energy conservation. Instead of maintaining a fixed LOSL density, the proposed scheme can adaptively adjust the networking level to save energy while guaranteeing tracking accuracy. The effectiveness of the proposed scheme is evaluated with computer simulations. According to the results, it is observed that the proposed scheme can sufficiently reduce power consumption, while providing qualified tracking performance.