Researches On Localization Of Wireless Sensor Network Based On Acoustic Energy

Source location is an important application in wireless sensor net-work. Existing source localization methods make use of four types of physical measurements:time of arrival(TOA) method, time difference of arrival(TDOA) method, angles of arrival(AOA) method, received signal strength indication-based(RSSI) method. Herein we focus on the energy-based acoustic source localization technique, since the sound propagation is regular, and the collected signal is relatively stable and reliable, the en-ergy can be accurately sampled, we can use the RSSI method to solve the acoustic localization problem.In this paper, according to the characteristic that the amount of source energy at a sensor is inversely proportional to the square of the distance between the source and the sensor, we derived the acoustic energy attenu-ation model. Based on this model, basic target localization algorithms are introduced. For energy-based localization problem, due to the mathemati-cal model is a fractional nonlinear and non-convex problem, how to obtain the source location from the model is the key issue. In this paper, we pro-pose the improved weighted least squares(WLS) algorithm and the max-imum likelihood(ML) estimation method:in the WLS algorithm, we put forward four different approximation models, and prove that they can be equivalently transform into convex optimization problems, then construc-t new algorithms by means of both semi-definite programming relaxation and matrix rank-one decomposition technique; in the ML algorithm, we propose two new models based on the ML model, and transform them into convex problems in the similar way.Finally, based on the platform of MATLAB, simulations are conducted for the above-mentioned algorithms and other algorithms. The simulation results show that the LS1model based on ML model are indeed efficient and outperform other methods in low noise environment, while in high noise levels, the approximate model2based on WLS method has better performance.