In this paper we introduce a MEMS based pedestrian navigation system (PNS) which consists of the low cost MEMS inertial sensor. An adaptive step length estimation algorithm using the awareness of the walk or run status is presented. Future u-Health monitoring systems will be essential equipment for mobile users under the ubiquitous computing environment. It is well known that the cost of energy expenditure in human walk or run changes with the speed of movement. Also the accurate walking distance is an important factor in calculating energy expenditure in human daily life. In order to compute the walking distance precisely, the number of occurred steps has to be counted exactly and the step length should be exactly estimated as well. However the step length varies considerably with the movement's speed and status. Therefore, we recognize the movement status such as walk or run of a pedestrian using the small-sized MEMS inertial sensors. Based on the result, a step length is estimated adaptively. The developed method can be applied to PNS and health monitoring mobile system.

In this paper, we present a novel visual-inertial navigation algorithm for low-cost and computationally constrained vehicle in global positioning system denied environments by modeling the state space as the matrix Lie group (LG), based on the recent theory of the invariant Kalman filter. The multistate constraint Kalman filter (MSCKF) is a well-known visual-inertial odometry algorithm that performs the fusion of the visual and inertial information by constraining each other through the stochastically cloned pose within a sliding window. However, conventional MSCKF (MSCKF-Conv) suffers from the inconsistent state estimates caused by the spurious gain along the unobservable directions, resulting in large estimation errors. To tackle this problem, we extend the concepts of the state and noise of the MSCKF from Euclidean space to matrix LG. We model the state of the MSCKF as the element of the specially customized matrix LG and use the noise uncertainty modeling with the corresponding Lie algebra. The detailed derivation and observability analysis of the proposed filter are provided to prove that the proposed filter is more consistent than the MSCKF-Conv. The proposed MSCKF on matrix LG naturally enforces the state vector to exist in the state space that maintains the unobservability characteristics without any artificial remedies. The performance of the proposed filter is validated through the Monte-Carlo simulation and the real-world experimental dataset.

Since synthetic aperture radar (SAR) images contain severe noise, it is important to extract noise excluded feature when recognizing the target in the SAR image. Therefore, previous SAR automatic target recognition (ATR) methods use separate preprocessing process or pose information of SAR to reduce the influence of noise. However, since noise characteristics of SAR images are different from image to image, recognition accuracy cannot be guaranteed if the preprocessing process is conducted improperly or there is no pose information. For this reason, we propose multiple feature-based convolutional neural networks (MFCNNs) recognizing the target of the SAR image without using a separate preprocessing process or pose information. MFCNN consists of three steps. First, extract strong features of the target with more effect of noise and smoothed features with lesser effect of noise. Second, aggregate extracted features with complementary relationships into a single column vector. Last, fully connected networks recognize the target using aggregated features. We used moving and stationary target acquisition and recognition SAR public data set for simulations and confirmed that the proposed method can recognize the target of the SAR image more accurately than previous SAR ATR methods without preprocessing process and pose information.

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future.

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.