This paper presents a WiFi-based device-free crowd counting and occupancy estimation system that can be used in rooms/environments different from the ones in which the training process has been performed. Therefore, crowd counting is achieved without requiring another training phase in each new environment. The proposed approach analyzes the shape of the Doppler spectrum of the received signal which is correlated to the number of people moving in the monitored environment. Unlike a radar-like approach, the use of a reference signal is not required. Experimental results are presented for two different rooms/environments without any constraint on the movements of the volunteers.

This paper addresses the use of channel state information (CSI) for Long Term Evolution (LTE) signal fingerprinting localization. In particular, the paper proposes a novel CSI-based signal fingerprinting approach, where fingerprints are descriptors of the “shape” of the channel frequency response (CFR) calculated on CSI vectors, rather than direct CSI vectors. Experiments have been carried out to prove the feasibility and the effectiveness of the proposed method and to study the impact on the localization performance of (i) the bandwidth of the available LTE signal and (ii) the availability of more LTE signals transmitted by different eNodeB (cell diversity). Comparisons with other signal fingerprinting approaches, such as the ones based on received signal strength indicator or reference signal received power, clearly show that using LTE CSI, and in particular, descriptors as fingerprints, can bring relevant performance improvement.

Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients’ movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.

This paper investigates the possibility to use Channel State Information (CSI) extracted from Long Term Evolution (LTE) signals for signal fingerprinting localization. Being the first work in this direction, several types of signal fingerprinting-based approaches have been compared (e.g., CSI-based vs RSSI-based, statistic vs deterministic matching rule). In particular, the paper proposes a novel CSI-based signal fingerprinting that uses as fingerprint not directly the vector of channel gains per subcarrier, but rather some features extracted from these vectors. This method would greatly reduce the memory requirement of the database as well as the computational complexity of the matching phase. Experimental results, shown for both indoor and outdoor environments, confirm the effectiveness of the proposed method and also provide interesting insights on the use of LTE signal fingerprinting based on CSI.

This paper shows the feasibility of an LTE signal fingerprinting Device Free Passive localization system. The experimental setup considers one single LTE receiver in the monitored room. The paper shows that when ambient signals such as LTE are used, the use of RSSI as fingerprint provides very poor performance. CSI-based approaches demonstrate much better performance. Moreover, the system using CSI shape and statistical descriptors rather than direct CSI vectors is characterized by more stable performance also when the room configuration is changed (i.e., the furniture is moved).

The near Earth environment has radically changed since the first launch of Sputnik in 1957. So far, the number of uncontrolled artificial objects orbiting around the Earth has incredibly increased and the need of protecting space systems has become almost common to all spacecraft operators. Even though they are currently supported by the large U.S. Space Surveillance Network, most of them have noticed the necessity of creating their own space surveillance sensors. This paper presents a preliminary design of a small and easy to implement radar able to track LEO objects and provide orbits that are more accurate than Two-Line Element (TLE) orbits available online, which do not allow to perform a safe collision avoidance analysis. The adopted configuration is a monostatic pulsed radar with a maximum unambiguous range of 3000 km, a range resolution and angular resolutions of 20 m and 0.01°, respectively. According to the theoretical assessment, the proposed radar is able to detect targets with a Radar Cross Section (RCS) of at least 1 sq. m., which means circular targets with a minimum radius of 50 cm, with a detection probability of 0.9 and a false alarm probability of 0.1. To assess the radar performance, it has been developed CASSIM (Collision Avoidance System SIMulator), which models the system at all stages, from the targets to the signal processing techniques, going through the synthesis of the transmitted and received signals. The simulations have been conducted considering a scenario described in a Collision Data Message (CDM) sent by the U.S. Space Surveillance Network. The simulations have involved the less accurate TLE orbit as radar input and the CDM State Vector orbits as reference to model the simulated target. Results have shown that the radar orbit is comparable to the accurate CDM orbit and that the closest approach distance is very similar to the one indicated in the message (less than 30 m error on all components). The analysis presented in this paper shows the feasibility of this small and simple radar able to get more accurate orbits than TLEs. Moreover, it proved that a collision avoidance analysis based on the radar orbit can be equivalent and could be adequately integrated to the use of the U.S. Space Surveillance Network data.