UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system

ARTIFICIAL NEURAL NETWORK APPLICATION FOR THE TEMPORAL PROPERTIES OF ACOUSTIC PERCEPTION

Though acoustic perception is well established in literature, it seems to be insufficiently implemented in practice. Experimental results are excellent but a lot of issues arise when it comes to the application in real conditions. Using artificial neural networks makes acoustic signal processing very comfortable from the mathematical point of view. However, a great job has to be done in order to make it possible. The procedure includes data acquisition, filtering, feature extraction and selection. These techniques require much more resources than mere artificial neural networks and this represents a limiting factor for the implementation. The paper investigates the complete procedure of acoustic perception, in terms of time, in order to identify limitations

Adaptive sampling in autonomous marine sensor networks

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints, and an approach to cooperative robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches. The mobility of the sensor platforms is a key advantage of this strategy, allowing dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time. Experimental results are presented for a 2-D target tracking application in which fully autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate. In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network

Sensorimotor Model of Obstacle Avoidance in Echolocating Bats

Bat echolocation is an ability consisting of many subtasks such as navigation, prey detection and object recognition. Understanding the echolocation capabilities of bats comes down to isolating the minimal set of acoustic cues needed to complete each task. For some tasks, the minimal cues have already been identified. However, while a number of possible cues have been suggested, little is known about the minimal cues supporting obstacle avoidance in echolocating bats. In this paper, we propose that the Interaural Intensity Difference (IID) and travel time of the first millisecond of the echo train are sufficient cues for obstacle avoidance. We describe a simple control algorithm based on the use of these cues in combination with alternating ear positions modeled after the constant frequency bat Rhinolophus rouxii. Using spatial simulations (2D and 3D), we show that simple phonotaxis can steer a bat clear from obstacles without performing a reconstruction of the 3D layout of the scene. As such, this paper presents the first computationally explicit explanation for obstacle avoidance validated in complex simulated environments. Based on additional simulations modelling the FM bat Phyllostomus discolor, we conjecture that the proposed cues can be exploited by constant frequency (CF) bats and frequency modulated (FM) bats alike. We hypothesize that using a low level yet robust cue for obstacle avoidance allows bats to comply with the hard real-time constraints of this basic behaviour

Perceptual strategies in active and passive hearing of neotropical bats

Basic spectral and temporal sound properties, such as frequency content and timing, are evaluated by the auditory system to build an internal representation of the external world and to generate auditory guided behaviour. Using echolocating bats as model system, I investigated aspects of spectral and temporal processing during echolocation and in relation to passive listening, and the echo-acoustic object recognition for navigation. In the first project (chapter 2), the spectral processing during passive and active hearing was compared in the echolocting bat Phyllostomus discolor. Sounds are ubiquitously used for many vital behaviours, such as communication, predator and prey detection, or echolocation. The frequency content of a sound is one major component for the correct perception of the transmitted information, but it is distorted while travelling from the sound source to the receiver. In order to correctly determine the frequency content of an acoustic signal, the receiver needs to compensate for these distortions. We first investigated whether P. discolor compensates for distortions of the spectral shape of transmitted sounds during passive listening. Bats were trained to discriminate lowpass filtered from highpass filtered acoustic impulses, while hearing a continuous white noise background with a flat spectral shape. We then assessed their spontaneous classification of acoustic impulses with varying spectral content depending on the background’s spectral shape (flat or lowpass filtered). Lowpass filtered noise background increased the proportion of highpass classifications of the same filtered impulses, compared to white noise background. Like humans, the bats thus compensated for the background’s spectral shape. In an active-acoustic version of the identical experiment, the bats had to classify filtered playbacks of their emitted echolocation calls instead of passively presented impulses. During echolocation, the classification of the filtered echoes was independent of the spectral shape of the passively presented background noise. Likewise, call structure did not change to compensate for the background’s spectral shape. Hence, auditory processing differs between passive and active hearing, with echolocation representing an independent mode with its own rules of auditory spectral analysis. The second project (chapter 3) was concerned with the accurate measurement of the time of occurrence of auditory signals, and as such also distance in echolocation. In addition, the importance of passive listening compared to echolocation turned out to be an unexpected factor in this study. To measure the distance to objects, called ranging, bats measure the time delay between an outgoing call and its returning echo. Ranging accuracy received considerable interest in echolocation research for several reasons: (i) behaviourally, it is of importance for the bat’s ability to locate objects and navigate its surrounding, (ii) physiologically, the neuronal implementation of precise measurements of very short time intervals is a challenge and (iii) the conjectured echo-acoustic receiver of bats is of interest for signal processing. Here, I trained the nectarivorous bat Glossophaga soricina to detect a jittering real target and found a biologically plausible distance accuracy of 4–7 mm, corresponding to a temporal accuracy of 20–40 μs. However, presumably all bats did not learn to use the jittering echo delay as the first and most prominent cue, but relied on passive acoustic listening first, which could only be prevented by the playback of masking noise. This shows that even a non-gleaning bat heavily relies on passive acoustic cues and that the measuring of short time intervals is difficult. This result questions other studies reporting a sub-microsecond time jitter threshold. The third project (chapter 4) linked the perception of echo-acoustic stimuli to the appropriate behavioural reactions, namely evasive flight manoeuvres around virtual objects presented in the flight paths of wild, untrained bats. Echolocating bats are able to orient in complete darkness only by analysing the echoes of their emitted calls. They detect, recognize and classify objects based on the spectro-temporal reflection pattern received at the two ears. Auditory object analysis, however, is inevitably more complicated than visual object analysis, because the one-dimensional acoustic time signal only transmits range information, i.e., the object’s distance and its longitudinal extent. All other object dimensions like width and height have to be inferred from comparative analysis of the signals at both ears and over time. The purpose of this study was to measure perceived object dimensions in wild, experimentally naïve bats by video-recording and analysing the bats’ evasive flight manoeuvres in response to the presentation of virtual echo-acoustic objects with independently manipulated acoustic parameters. Flight manoeuvres were analysed by extracting the flight paths of all passing bats. As a control to our method, we also recorded the flight paths of bats in response to a real object. Bats avoided the real object by flying around it. However, we did not find any flight path changes in response to the presentation of several virtual objects. We assume that the missing spatial extent of virtual echo-acoustic objects, due to playback from only one loudspeaker, was the main reason for the failure to evoke evasive flight manoeuvres. This study therefore emphasises for the first time the importance of the spatial dimension of virtual objects, which were up to now neglected in virtual object presentations