| dc.description.abstract | In this thesis, the fundamental knowledge about wireless localization is first introduced. The compressed sensing techniques exploiting the signal sparsity can increase the efficiency of data collection and thus are widely investigated. Some linear programming algorithms can be adopted to solve the range-free localization problem from compressive sensing, such as the simplex algorithm, interior-point algorithm, and OMP algorithm.
The steps of the simplex algorithm perform as follows: listing a tableau, finding an entering variable, and pivoting. In our simulation environments, the network sizes are set to 5m×5m, 7.5m×7.5m, 10m×10m with grid sizes 12×12, 18×18, 24×24. Several sensor nodes are spread inside the network close to the boundary to provide the measurements of received signal strengths. From the simulation results, we show that the performance of the simplex algorithm approaches to that of the interior-point algorithm for l_1 optimization. However, the complexity of the former is much less. Consequently, the implementation of the simplex algorithm is considered. Since the simplex algorithm is an iterative algorithm, the error will be easily accumulated. Thus, a floating-point representation with total 25 bits is adopted for the data path. The hardware parallelism will affect the complexity and execution clock cycles. After evaluation, the degree of parallelism is set to 13 to strike a balance between processing time and hardware complexity. As a result, the total execution time are 496 clock cycles for one iteration.
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