Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

This paper presents hybrid type-II automatic repeat request (H-ARQ) for wireless wearable body area networks (BANs) based on ultra wideband (UWB) technology. The proposed model is based on three schemes, namely, high rate optimized rate compatible punctured convolutional codes (HRO-RCPC), Reed Solomon (RS) invertible codes and their concatenation. Forward error correction (FEC) coding is combined with simple cyclic redundancy check (CRC) error detection. The performance is investigated for two channels: CM3 (on-body to on-body) and CM4 (on-body to a gateway) scenarios of the IEEE802.15.6 BAN channel models for BANs. It is shown that the improvement in performance in terms of throughput and error protection robustness is very significant. Thus, the proposed H-ARQ schemes can be employed and optimized to suit medical and non-medical applications. In particular we propose the use of FEC coding for non-medical applications as those require less stringent quality of service (QoS), while the incremental redundancy and ARQ configuration is utilized only for medical applications. Thus, higher QoS is guaranteed for medical application of BANs while allowing coexistence with non-medical applications.

With the rapid progress of electronic and information technology, an expectation for the realization of body area network (BAN) by means of ultra wide band (UWB) techniques has risen. Although the signal from a single UWB device is very low, the energy absorption may increase significantly when many UWB devices are simultaneously adorned to a human body. An analysis method is therefore required from the point of view of biological safety evaluation. In this study, two approaches, one is in the time domain and the other is in the frequency domain, are proposed for the specific energy absorption (SA) and the specific absorption rate (SAR) calculation. It is shown that the two approaches have the same accuracy but the time-domain approach is more straightforward in the numerical analysis. By using the time-domain approach, SA and SAR calculation results are given for multiple UWB pulse exposure to an anatomical human body model under the Federal Communications Commission (FCC) UWB limit.