Exact Solution of an Approximate Weighted Least Squares Estimate of Energy-Based Source Localization in Sensor Networks

Distributed Event Localization via Alternating Direction Method of Multipliers

“…And compared with centralized SDP-based algorithms, our clustered architecture is robust to processing center failure or traffic bottleneck problems. In addition, the convex relaxation methods used at each cluster can be further improved by using recent works such as [10], [15], [16], [17], [21], [22], [23].…”

“…Typical centralized methods include the parallel projection method [20], convex relaxation plus semidefinite programming *The work was supported in part by the Institute for Collaborative Biotechnologies through grant W911NF-09-0001. Chunlei (SDP) or second-order cone programming method [10], [15], [16], [17], [21], [22], [23]. However, a severe shortcoming of centralized localization algorithms is that the computation complexity at the processing center might be quite high which poses great challenges for low-cost sensor nodes with limited computational capabilities.…”

Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks

“…Different measurement models are defined to localize the source, such as time of arrival (TOA) [ 10 , 11 , 12 , 13 ], time difference of arrival (TDOA) [ 14 , 15 , 16 , 17 ], angle of arrival (AOA) [ 18 ], received signal strength (RSS) [ 19 , 20 , 21 ], received signal energy [ 5 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ], distance measurement (DM) [ 31 ], and a combination of part of them [ 32 , 33 ]. The range information between sensor nodes and the source is reflected in TOA, TDOA, and RSS, while the angular information of the emitting signal relative to self-nodes is reflected in AOA.…”

“…Aiming to efficiently solve the ML problem, and enhance the source localization performance of LS and WLS based methods at higher noise level, some authors in [ 20 , 21 , 26 , 27 , 28 , 29 , 30 ] proposed a kind of convex approximation technique to convert the nonconvex optimization problems into convex ones, which can be reliably and efficiently solved to derive globally optimal solution [ 35 , 36 ]. It should be noted that the core idea of acoustic energy based source localization via convex optimization is to convert the original optimization problem into a convex one.…”

“…Under the same scenario, an approximate of the original ML problem was proposed by joint estimating (JE) the unknown transmission power and source location in [ 27 ]. Based on the same acoustic energy attenuation model with decay factor 2, a quadratic programming problem via SDR (labeled as SQP) was formulated in [ 29 ], which requires additional matrix rank-one decomposition to find the optimal source location. However, such a source localization method is quite sensitive to some matrix parameters.…”