Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

Indoor location and positioning systems (ILPS) are used to locate and track people, as well as mobile and/or connected targets, such as robots or smartphones, not only inside buildings with a lack of global navigation satellite systems (GNSS) signals but also in constrained outdoor situations with reduced coverage. Indoor positioning applications and their interest are growing in certain environments, such as commercial centers, airports, hospitals or factories. Several sensory technologies have already been applied to indoor positioning systems, where ultrasounds are a common solution due to its low cost and simplicity. This work proposes a 3D ultrasonic local positioning system (ULPS), based on a set of three asynchronous ultrasonic beacon units, capable of transmitting coded signals independently, and on a 3D mobile receiver prototype. The proposal is based on the aforementioned beacon unit, which consists of five ultrasonic transmitters oriented towards the same coverage area and has already been proven in 2D positioning by applying hyperbolic trilateration. Since there are three beacon units available, the final position is obtained by merging the partial results from each unit, implementing a minimum likelihood estimation (MLE) fusion algorithm. The approach has been characterized, and experimentally verified, trying to maximize the coverage zone, at least for typical sizes in most common public rooms and halls. The proposal has achieved a positioning accuracy below decimeters for 90% of the cases in the zone where the three ultrasonic beacon units are available, whereas these accuracies can degrade above decimeters according to whether the coverage from one or more beacon units is missing. The experimental workspace covers a large volume, where tests have been carried out at points placed in two different horizontal planes.

The eigendecompostion of the coherency matrix and the related parameters, i.e., entropy H, α, and anisotropy A, are effective and popular tools for the analysis and quantitative estimation of the physical parameters of polarimetric synthetic aperture radar images. However, the speckle noise constitutes the main obstacle that hinders these goals and should be filtered. In this paper, based on studies obtained from extensive simulated data sets, we tried to determine how this noise is transmitted to sample eigendecomposition parameters. The dependence between means and variances of sample parameters leads to building their speckle models and to the definition of a bias elimination procedure. We found that sample eigenvalues were affected by multiplicative noise, whereas sample entropy and sample anisotropy were affected by a mixture of multiplicative and additive noise sources. In addition to its versatility, independence to knowledge of the equivalent number of looks and high ability to bias compensation, the proposed bias suppression technique reduced the variance of noise. Simulated and real data as well as the existing theories are used for validation in this paper.

The emergence of new IoT applications in regional and remote areas has increased the need for a global IoT connectivity beyond existing terrestrial network coverage. However, in many cases, it is not economically viable to build a dedicated terrestrial network to cover these remote areas due to population sparsity and the lack of business case. In this paper, we propose a framework for designing a solar rectenna for IoT‐over‐satellite applications using nanosatellites. Utilizing such a framework will allow valuable radio spectrum resources to be shared between satellite and terrestrial users. Thus, the autonomous power supply of these objects becomes a big challenge. Indeed, the harvest of solar energy and the conversion of RF energy into electric voltage are a hot topic. Our contribution consists in offering a solar rectenna system to collect solar and RF energy as well as the radio frequency transmission. A parametric study is carried out to follow the influence on the performance of this system. A topology of rectifying circuits is proposed in the present work. The parametric study has shown that the efficiency RF/DC conversion can reach 23.2% for an input power of 5 dBm and a load resistance of 2 kΩ. To ensure the satellite communication of IoT‐connected autonomous objects, this system is operated in the X or Ku band.