No abstract

No abstract

No abstract

No abstract

No abstract

Research of target localization and tracking is always a remarkable problem in the application of wireless sensor networks (WSNs) technology. There are many kinds of research and applications of target localization and tracking, such as Angle of Arrival (AOA), Time of Arrival (TOA), and Time Difference of Arrival (TDOA). The target localization accuracy for TOA, TDOA, and AOA is better than RSS. However, the required devices in the TOA, TDOA, and AOA are more expensive than RSS. In addition, the computational complexity of TOA, TDOA, and AOA is also more complicated than RSS. This paper uses a particle swarm optimization (PSO) algorithm with the received signal strength index (RSSI) channel model for indoor target localization and tracking. The performance of eight different method combinations of random or regular points, fixed or adaptive weights, and the region segmentation method (RSM) proposed in this paper for target localization and tracking is investigated for the number of particles in the PSO algorithm with 12, 24, 52, 72, and 100. The simulation results show that the proposed RSM method can reduce the number of particles used in the PSO algorithm and improve the speed of positioning and tracking without affecting the accuracy of target localization and tracking. The total average localization time for target localization and tracking with the RSM method can be reduced by 48.95% and 34.14%, respectively, and the average accuracy of target tracking reaches up to 93.09%.

In orthogonal frequency division multiplexing (OFDM) communication systems, due to the environmental impact generated the multipath effect caucused signals distortion and attenuation in transmitted process, and due to relative motion between transmitter and receiver caused the Doppler Effect that makes the signal carrier offset. Therefore, the knowledge of the channel characteristics is very important. To remove the effect from received signal, the receiver needs to have knowledge of channel impulse response (CIR) by channel estimation, and then compensates signals. In this paper, a back propagation neural network (BPNN) is used to estimate channel and compensate signals. Our proposed BPNN channel estimation would compare bit error rate (BER) and mean square error (MSE) with least square (LS) and minimum mean square error (MMSE) algorithms in an existing OFDM channel environment. From the results, our proposed algorithm has better performance than LS algorithm and closes to MMSE algorithm.

No abstract