Contributions of vision-proprioception interactions to the estimation of time-varying hand and target locations

We investigated the relative importance of vision and proprioception in estimating target and hand locations in a dynamic environment. Subjects performed a position estimation task in which a target moved horizontally on a screen at a constant velocity and then disappeared. They were asked to estimate the position of the invisible target under two conditions: passively observing and manually tracking. The tracking trials included three visual conditions with a cursor representing the hand position: always visible, disappearing simultaneously with target disappearance, and always invisible. The target’s invisible displacement was systematically underestimated during passive observation. In active conditions, tracking with the visible cursor significantly decreased the extent of underestimation. Tracking of the invisible target became much more accurate under this condition and was not affected by cursor disappearance. In a second experiment, subjects were asked to judge the position of their unseen hand instead of the target during tracking movements. Invisible hand displacements were also underestimated when compared with the actual displacement. Continuous or brief presentation of the cursor reduced the extent of underestimation. These results suggest that vision–proprioception interactions are critical for representing exact target–hand spatial relationships, and that such sensorimotor representation of hand kinematics serves a cognitive function in predicting target position. We propose a hypothesis that the central nervous system can utilize information derived from proprioception and/or efference copy for sensorimotor prediction of dynamic target and hand positions, but that effective use of this information for conscious estimation requires that it be presented in a form that corresponds to that used for the estimations.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Vitamin D intake needed to maintain target serum 25-Hydroxyvitamin D concentrations in participants with low sun exposure and dark skin pigmentation is substantially higher than current recommendations

Cutaneous cholecalciferol synthesis has not been considered in making recommendations for vitamin D intake. Our objective was to model the effects of sun exposure, vitamin D intake, and skin reflectance (pigmentation) on serum 25-hydroxyvitamin D (25[OH]D) in young adults with a wide range of skin reflectance and sun exposure. Four cohorts of participants (n = 72 total) were studied for 7-8 wk in the fall, winter, spring, and summer in Davis, CA [38.5° N, 121.7° W, Elev. 49 ft (15 m)]. Skin reflectance was measured using a spectrophotometer, vitamin D intake using food records, and sun exposure using polysulfone dosimeter badges. A multiple regression model (R^sup 2^ = 0.55; P < 0.0001) was developed and used to predict the serum 25(OH)D concentration for participants with low [median for African ancestry (AA)] and high [median for European ancestry (EA)] skin reflectance and with low [20th percentile, ~20 min/d, ~18% body surface area (BSA) exposed] and high (80th percentile, ~90 min/d, ~35% BSA exposed) sun exposure, assuming an intake of 200 IU/d (5 ug/d). Predicted serum 25(OH)D concentrations for AA individuals with low and high sun exposure in the winter were 24 and 42 nmol/L and in the summer were 40 and 60 nmol/L. Corresponding values for EA individuals were 35 and 60 nmol/L in the winter and in the summer were 58 and 85 nmol/L. To achieve 25(OH)D ≥75 nmol/L, we estimate that EA individuals with high sun exposure need 1300 IU/d vitamin D intake in the winter and AA individuals with low sun exposure need 2100-3100 IU/d year-round.

Decentralised particle filtering for multiple target tracking in wireless sensor networks

Record 8 of 29

Validation Gating for Non-Linear Non-Gaussian Target Tracking

This paper develops a general theory of validation gating for non-linear non-Gaussian mod- els. Validation gates are used in target tracking to cull very unlikely measurement-to-track associa- tions, before remaining association ambiguities are handled by a more comprehensive (and expensive) data association scheme. The essential property of a gate is to accept a high percentage of correct associ- ations, thus maximising track accuracy, but provide a su±ciently tight bound to minimise the number of ambiguous associations. For linear Gaussian systems, the ellipsoidal vali- dation gate is standard, and possesses the statistical property whereby a given threshold will accept a cer- tain percentage of true associations. This property does not hold for non-linear non-Gaussian models. As a system departs from linear-Gaussian, the ellip- soid gate tends to reject a higher than expected pro- portion of correct associations and permit an excess of false ones. In this paper, the concept of the ellip- soidal gate is extended to permit correct statistics for the non-linear non-Gaussian case. The new gate is demonstrated by a bearing-only tracking example.

PTZ camera pose estimation by tracking a 3D target

We present a technique for estimating the 6DOF pose of a PTZ camera by tracking a single moving target in the image with known 3D position. This is useful in situations where it is not practical to measure the camera pose directly. Our application domain is estimating the pose of a PTZ camerso so that it can be used for automated GPS-based tracking and filming of UAV flight trials. We present results which show the technique is able to localize a PTZ after a short vision-tracked flight, and that the estimated pose is sufficiently accurate for the PTZ to then actively track a UAV based on GPS position data.