This paper investigates the problem of received signal strength (RSS) based target localization with sensor position uncertainties and unknown model parameters. Transmit power and path-loss exponent (PLE) are two key parameters of the path-loss model that is critical for RSS schemes to work well. In practice, however, these parameters change dynamically and are difficult to estimate in real time. Four combinations of the two parameters are considered: both parameters are known; only one of the two is known; and both are unknown. We develop new semidefinite programming (SDP) methods to relax the nonconvex maximum likelihood estimator (MLE) problem for each case, and these methods are further optimized when sensor position uncertainties are taken into consideration. We present a comprehensive set of simulation results to compare the performance of the proposed scheme with existing methods.

This paper investigates the problem of source localization using signal time-difference-of-arrival (TDOA) measurements in the presence of clock synchronization bias and sensor position errors. Our existing work has developed a unified solution for TDOA localization in the presence of sensor position errors but clock synchronization bias was not considered. Clock synchronization bias is a more complex problem often encountered in practical localization networks. This paper further generalizes this framework to include clock synchronization bias. The proposed technique employs multiple calibration emitters to simultaneously alleviate both the sensor position errors and clock synchronization bias. The maximum likelihood estimator (MLE) for this problem is optimal, but too complex to be applied in practice. We develop a semidefinite programming (SDP) based localization algorithm to effectively solve the MLE problem. This SDP algorithm can reach the Cramer-Rao lower bound when sensor position errors are not unrealistically large.

A new joint synchronization and localization method for wireless sensor networks using two-way exchanged timestamps is proposed in this paper. The goal is to jointly localize and synchronize the source node, assuming that the locations and clock parameters of the anchor nodes are known. We first form the measurement model and derive the Cramér-Rao lower bound (CRLB). An analysis of the advantages and disadvantages of a recent scheme on joint synchronization and localization motivates us to develop a maximum likelihood estimator (MLE) that effectively resolves the issues of this existing scheme. A novel semidefinite programming method is then proposed to transform the nonconvex MLE problem into a convex optimization problem. Extensive simulation results are obtained to compare the synchronization and localization performances of proposed scheme and a few state-of-the-art existing schemes.

This paper develops a unified solution for time-difference-of-arrival (TDOA) localization in the presence of sensor position errors. This technique starts with maximum likelihood estimation (MLE), which is known to be nonconvex. A semidefinite programming technique to effectively transform the MLE problem into a convex optimization is proposed, together with a unified solution for four scenarios: 1) without a calibration emitter; 2) with a single calibration emitter, whose position is subject to measurement errors; 3) with a single calibration emitter, whose position is perfectly known; and 4) with a single calibration emitter, whose position is completely unknown. The results are finally extended to the case of multiple calibration emitters, whose positions are also subject to errors. Similar to the existing schemes that are known to have good performances, the proposed solution also reaches the Cramer-Rao lower bound when sensor position errors and TDOA measurement noise are sufficiently small. However, as TDOA measurement noise or sensor position errors increase, comparison with the existing state-of-the-art methods for each scenario shows that the proposed solution performs significantly better.

Using angle-of-arrival (AOA) measurements from several sensors to locate a target in 3-D space is one of the commonly used wireless localization techniques. A challenge of this technique is that the weighted least-squares (WLS) needed for it tend to incur the threshold effect when the noise level is high or when the localization geometry is poor, because its implementation requires the inversion of a matrix whose condition number will be large under such conditions. This paper presents an iterative method that does not require any matrix operation and is proved to be convergent with any initial value. Additionally, it outperforms the WLS-based methods as validated by comprehensive simulation results.