Zhang Yuanyuan, Wu Huafeng, Mei Xiaojun, Xian Jiangfeng, Wang Weijun, Zhang Qiannan, Liang Linian
Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China.
Sensors (Basel). 2021 Mar 2;21(5):1724. doi: 10.3390/s21051724.
Target localization plays a vital role in ocean sensor networks (OSNs), in which accurate position information is not only a critical need of ocean observation but a necessary condition for the implementation of ocean engineering. Compared with other range-based localization technologies in OSNs, the received signal strength (RSS)-based localization technique has attracted widespread attention due to its low cost and synchronization-free nature. However, maintaining relatively good accuracy in an environment as dynamic and complex as the ocean remains challenging. One of the most damaging factors that degrade the localization accuracy is the uncertainty in transmission power. Besides the equipment loss, the uncertain factors in the fickle ocean environment may result in a significant deviation between the standard rated transmission power and the usable transmission power. The difference between the rated and actual transmission power would lead to an extra error when it comes to the localization in OSNs. In this case, a method that can locate the target without needing prior knowledge of the transmission power is proposed. The method relies on a two-phase procedure in which the location information and the transmission power are jointly estimated. First, the original nonconvex localization problem is transformed into an alternating non-negativity-constrained least square framework with the unknown transmission power (UT-ANLS). Under this framework, a two-stage optimization method based on interior point method (IPM) and majorization-minimization tactic (MMT) is proposed to search for the optimal solution. In the first stage, the barrier function method is used to limit the optimization scope to find an approximate solution to the problem. However, it is infeasible to approach the constraint boundary due to its intrinsic error. Then, in the second stage, the original objective is converted into a surrogate function consisting of a convex quadratic and concave term. The solution obtained by IPM is considered the initial guess of MMT to jointly estimate both the location and transmission power in the iteration. In addition, in order to evaluate the performance of IPM-MM, the Cramer Rao lower bound (CRLB) is derived. Numerical simulation results demonstrate that IPM-MM achieves better performance than the others in different scenarios.
目标定位在海洋传感器网络（OSN）中起着至关重要的作用，在该网络中，准确的位置信息不仅是海洋观测的关键需求，也是实施海洋工程的必要条件。与OSN中其他基于距离的定位技术相比，基于接收信号强度（RSS）的定位技术因其低成本和无需同步的特性而受到广泛关注。然而，在像海洋这样动态且复杂的环境中保持相对较高的精度仍然具有挑战性。降低定位精度的最具破坏性的因素之一是发射功率的不确定性。除了设备损耗外，多变的海洋环境中的不确定因素可能导致标准额定发射功率与可用发射功率之间存在显著偏差。额定发射功率与实际发射功率之间的差异在OSN定位时会导致额外的误差。在这种情况下，提出了一种无需发射功率先验知识即可定位目标的方法。该方法依赖于一个两阶段过程，其中联合估计位置信息和发射功率。首先，将原始的非凸定位问题转化为具有未知发射功率的交替非负约束最小二乘框架（UT-ANLS）。在此框架下，提出了一种基于内点法（IPM）和主元最小化策略（MMT）的两阶段优化方法来寻找最优解。在第一阶段，使用障碍函数方法限制优化范围以找到问题的近似解。然而，由于其固有误差，接近约束边界是不可行的。然后，在第二阶段，将原始目标转换为由凸二次项和凹项组成的替代函数。通过IPM获得的解被视为MMT的初始猜测，以便在迭代中联合估计位置和发射功率。此外，为了评估IPM-MM的性能，推导了克拉美罗下界（CRLB）。数值模拟结果表明，在不同场景下，IPM-MM比其他方法具有更好的性能。
