Simulation-Based Resilience Quantification of an Indoor Ultrasound Localization System in the Presence of Disruptions.

Time difference of arrival (TDOA) based indoor ultrasound localization systems are prone to multiple disruptions and demand reliable, and resilient position accuracy during operation. In this challenging context, a missing link to evaluate the performance of such systems is a simulation approach to test their robustness in the presence of disruptions. This approach cannot only replace experiments in early phases of development but could also be used to study susceptibility, robustness, response, and recovery in case of disruptions. The paper presents a simulation framework for a TDOA-based indoor ultrasound localization system and ways to introduce different types of disruptions. This framework can be used to test the performance of TDOA-based localization algorithms in the presence of disruptions. Resilience quantification results are presented for representative disruptions. Based on these quantities, it is found that localization with arc-tangent cost function is approximately 30% more resilient than the linear cost function. The simulation approach is shown to apply to resilience engineering and can be used to increase the efficiency and quality of indoor localization methods.

Comparison of Direct Intersection and Sonogram Methods for Acoustic Indoor Localization of Persons.

We discuss two methods to detect the presence and location of a person in an acoustically small-scale room and compare the performances for a simulated person in distances between 1 and 2 m. The first method is Direct Intersection, which determines a coordinate point based on the intersection of spheroids defined by observed distances of high-intensity reverberations. The second method, Sonogram analysis, overlays all channels' room impulse responses to generate an intensity map for the observed environment. We demonstrate that the former method has lower computational complexity that almost halves the execution time in the best observed case, but about 7 times slower in the worst case compared to the Sonogram method while using 2.4 times less memory. Both approaches yield similar mean absolute localization errors between 0.3 and 0.9 m. The Direct Intersection method performs more precise in the best case, while the Sonogram method performs more robustly.

Asynchronous Chirp Slope Keying for Underwater Acoustic Communication.

We propose an asynchronous acoustic chirp slope keying to map short bit sequences on single or multiple bands without preamble or error correction coding on the physical layer. We introduce a symbol detection scheme in the demodulator that uses the superposed matched filter results of up and down chirp references to estimate the symbol timing, which removes the requirement of a preamble for symbol synchronization. Details of the implementation are disclosed and discussed, and the performance is verified in a pool measurement on laboratory scale, as well as the simulation for a channel containing Rayleigh fading and Additive White Gaussian Noise. For time-bandwidth products (TB) of 50 in single band mode, a raw data rate of 100 bit/s is simulated to achieve bit error rates (BER) below 0.001 for signal-to-noise ratios above -6 dB. In dual-band mode, for TB of 25 and a data rate of 200 bit/s, the same bit error level was achieved for signal-to-noise ratios above 0 dB. The simulated packet error rates (PER) follow the general behavior of the BER, but with a higher error probability, which increases with the length of bits in each packet.

Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning.

An acoustic transmitter can be located by having multiple static microphones. These microphones are synchronized and measure the time differences of arrival (TDoA). Usually, the positions of the microphones are assumed to be known in advance. However, in practice, this means they have to be manually measured, which is a cumbersome job and is prone to errors. In this paper, we present two novel approaches which do not require manual measurement of the receiver positions. The first method uses an inertial measurement unit (IMU), in addition to the acoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional source of information, the non-convex optimizers are less likely to fall into local minima. Consequently, the success rate is increased and measurements with large errors have less influence on the final estimation. The second method we present in this paper consists of using machine learning to learn the TDoA signatures of certain regions of the localization area. By doing this, the target can be located without knowing where the microphones are and whether the received signals are in line-of-sight or not. We use an artificial neural network and random forest classification for this purpose.

Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies.

Wake-up receivers are the natural choice for wireless sensor networks because of their ultra-low power consumption and their ability to provide communications on demand. A downside of ultra-low power wake-up receivers is their low sensitivity caused by the passive demodulation of the carrier signal. In this article, we present a novel communication scheme by exploiting purposefully-interfering out-of-tune signals of two or more wireless sensor nodes, which produce the wake-up signal as the beat frequency of superposed carriers. Additionally, we introduce a communication algorithm and a flooding protocol based on this approach. Our experiments show that our approach increases the received signal strength up to 3 dB, improving communication robustness and reliability. Furthermore, we demonstrate the feasibility of our newly-developed protocols by means of an outdoor experiment and an indoor setup consisting of several nodes. The flooding algorithm achieves almost a 100% wake-up rate in less than 20 ms.

Acoustic NLOS Identification Using Acoustic Channel Characteristics for Smartphone Indoor Localization.

As the demand for indoor localization is increasing to support our daily life in large and complex indoor environments, sound-based localization technologies have attracted researchers' attention because they have the advantages of being fully compatible with commercial off-the-shelf (COTS) smartphones, they have high positioning accuracy and low-cost infrastructure. However, the non-line-of-sight (NLOS) phenomenon poses a great challenge and has become the technology bottleneck for practical applications of acoustic smartphone indoor localization. Through identifying and discarding the NLOS measurements, the positioning performance can be improved by incorporating only the LOS measurements. In this paper, we focus on identifying NLOS components by characterizing the acoustic channels. Firstly, by analyzing indoor acoustic propagations, the changes of acoustic channel from the line-of-sight (LOS) condition to the NLOS condition are characterized as the difference of channel gain and channel delay between the two propagation scenarios. Then, an efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed, which considers the mitigation of the Doppler Effect and reduction of the computational complexity. Nine novel features have been extracted, and a support vector machine (SVM) classifier with a radial-based function (RBF) kernel is used to realize NLOS identification. The experimental result with an overall 98.9% classification accuracy based on a data set with more than 10 thousand measurements shows that the proposed identification approach and features are effective in acoustic NLOS identification for acoustic indoor localization via a smartphone. In order to further evaluate the performance of the proposed SVM classifier, the performance of an SVM classifier is compared with that of traditional classifiers based on logistic regression (LR) and linear discriminant analysis (LDA). The results also show that a SVM with the RBF kernel function method outperforms others in acoustic NLOS identification.