Accurate location information is critical for many engineering applications (e.g., radar, sonar, autonomous robots, intelligent transportation systems). In traditional source localization algorithms, the perfect knowledge of noisy Time-of-Arrival (ToA) measurements are assumed to be obtained by the fusion center in a sensor network. This assumption is not practical for wireless sensor networks, especially for a resource-limited sensor network with stringent power and communication bandwidth constraints. In this paper, we propose a novel channel-aware source localization method based on quantized asynchronous ToA measurements, where the quantization errors as well as the imperfect communication link between each sensor and the fusion center are considered. The maximum-likelihood (ML) source localization by jointly estimating the signal transmission instant and source location is formulated. An efficient relaxation is provided to transform the non-convex ML optimization problem into a convex problem. The Cramér-Rao lower bounds (CRLBs) for the quantized ToA measurements with the uncertainty of data exchange are derived. Furthermore, a Fisher information based heuristic quantization scheme is proposed to design quantized thresholds for asynchronous ToA measurements. The simulation and experimental results demonstrate that our proposed method can yield an efficient estimate under different scenarios.

Vehicle self-positioning is of significant importance for intelligent transportation applications. However, accurate positioning (e.g., with lane-level accuracy) is very difficult to obtain due to the lack of measurements with high confidence, especially in an environment without full access to a global navigation satellite system (GNSS). In this paper, a novel information fusion algorithm based on a particle filter is proposed to achieve lane-level tracking accuracy under a GNSS-denied environment. We consider the use of both coarse-scale and fine-scale signal measurements for positioning. Time-of-arrival measurements using the radio frequency signals from known transmitters or roadside units, and acceleration or gyroscope measurements from an inertial measurement unit (IMU) allow us to form a coarse estimate of the vehicle position using an extended Kalman filter. Subsequently, fine-scale measurements, including lane-change detection, radar ranging from the known obstacles (e.g., guardrails), and information from a high-resolution digital map, are incorporated to refine the position estimates. A probabilistic model is introduced to characterize the lane changing behaviors, and a multi-hypothesis model is formulated for the radar range measurements to robustly weigh the particles and refine the tracking results. Moreover, a decision fusion mechanism is proposed to achieve a higher reliability in the lane-change detection as compared to each individual detector using IMU and visual (if available) information. The posterior Cramér-Rao lower bound is also derived to provide a theoretical performance guideline. The performance of the proposed tracking framework is verified by simulations and real measured IMU data in a four-lane highway.

The widespread applications of wireless sensor network (WSN) and the advancement of the micro-electro-mechanical systems (MEMS) technology, and wireless communication develop the underwater wireless sensor network (UWSN). The passive source localization application which is an important application in underwater signal processing is focused on in this paper. We model that the underwater source radiates acoustic noise or energy isotropically and utilize the receiving signal energy (RSS) taken at individual sensors of the UWSN to estimate passively the location of the source. Firstly, we choose the node whose energy is maximal as the reference node to decrease the computational complexity compared to our previous work which use division between pairs of sensor energy output. Secondly, the one-step least-square (OS) method is reviewed and another description based on maximum likelihood source location estimator (MLE) is given. Thirdly, a semidefinite programming (SDP) method is developed to covert the nonconvex problem into the convex optimization problem (CVX). Compared to the least-square (LS) method, this CVX-SDP method based on RSS achieves more accurate results and has better robustness with less number of sensor nodes and with lower SNR.

The received energy has been becoming an efficient and attractive measure for acoustic source localization due to its cost saving in both energy and computation capability. We investigated the acoustic source localization problem based on received energy measurements in sensor networks. Focusing on the non-logarithmic energy attenuation model, we developed and compared a suite of semidefinite programming (SDP) based source localization methods due to computational efficiency and numerical reliability. First, we proposed a general SDP-based estimator by jointly estimating the source location and the source radiation power. It yields an efficient estimate for both the scenario where the source is located inside the convex hull formed by sensors and the scenario where the source is located outside the convex hull. Next, a min-max approximation (MMA) is given to cope with the applicable application of existing energy-based source localization algorithms relying on Gaussian energy noise assumption. Further, a novel norm approximation (NA) method is proposed according to norm equivalence, which can provide comparable performance with lower computational complexity. Simulations show that our proposed methods exhibit superior performance than the existing energy-based source localization estimators.