Ileana Milani

Bio: Ileana Milani is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Passive radar & Bistatic radar. The author has an hindex of 2, co-authored 5 publications receiving 18 citations.

WiFi emission-based vs passive radar localization of human targets

Abstract: In this paper two approaches are considered for human targets localization based on the WiFi signals: the device emission-based localization and the passive radar. Localization performance and characteristics of the two localization techniques are analyzed and compared, aiming at their joint exploitation inside sensor fusion systems. The former combines the Angle of Arrival (AoA) and the Time Difference of Arrival (TDoA) measures of the device transmissions to achieve the target position, while the latter exploits the AoA and the bistatic range measures of the target echoes. The results obtained on experimental data show that the WiFi emission-based strategy is always effective for the positioning of human targets holding a WiFi device, but it has a poor localization accuracy and the number of measured positions largely depends on the device activity. In contrast, the passive radar is only effective for moving targets and has limited spatial resolution but it provides better accuracy performance, thanks to the possibility to integrate a higher number of received signals. These results also demonstrate a significant complementarity of these techniques, through a suitable experimental test, which opens the way to the development of appropriate sensor fusion techniques.

Impact of Beacon Interval on the performance of WiFi-based passive radar against human targets

Abstract: The capability of WiFi-based passive radar to detect, track and profile human targets in both indoor and outdoor environment has been widely demonstrated This paper investigates the impact of the Beacon Interval (BI) on the passive radar performance The results of a dedicated acquisition campaign show that both the detection capability and the localization accuracy progressively degrade as the BI increases due to both the reduction of the received beacons and to the intrinsic undersampling of the target motion Limit values are suggested for practical applications

Fusing active and passive measurements for drone localization

Abstract: This paper investigates the joint exploitation of Wi-Fi based Passive Bistatic Radar (PBR) and Wi-Fi based Passive Source Location (PSL) for drone localization. The inherent features of the two strategies and the results obtained from their comparison on experimental data show an interesting complementarity between them. Following this consideration, a proper sensor fusion strategy combining these two methodologies is investigated in order to achieve improved results in terms of positioning capability. The three strategies (PBR, PSL and sensor fusion) are evaluated against experimental data. The comparison between the use of the sensor fusion approach and the localization based on a single sensor (PBR or PSL) shows the benefits coming from the exploitation of multiple sensors.

Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance

Abstract: In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) Passive Bistatic Radar (PBR) that exploits the Wi-Fi Access Point (AP) as an illuminator of opportunity to perform uncooperative target detection and localization and (ii) Passive Source Location (PSL) that uses radio frequency (RF) transmissions from the target to passively localize it, assuming that it is equipped with Wi-Fi devices. First, we show that these techniques have complementary characteristics with respect to the considered surveillance applications that typically include targets with highly variable motion parameters. Therefore, an appropriate sensor fusion strategy is proposed, based on a modified version of the Interacting Multiple Model (IMM) tracking algorithm, in order to benefit from the information diversity provided by the two sensors. The performance of the proposed strategy is evaluated against both simulated and experimental data and compared to the performance of the single sensors. The results confirm that the joint exploitation of the considered sensors based on the proposed strategy largely improves the positioning accuracy, target motion recognition capability and continuity in target tracking.

2D Localization with WiFi Passive Radar and Device-Based Techniques: An Analysis of Target Measurements Accuracy

Abstract: The aim of the work is to investigate the performance of two localization techniques based on WiFi signals: the WiFi-based passive radar and a device-based technique that exploits the measurement of angle of arrival (AoA) and time difference of arrival. This paper focuses specifically on the accuracy of the AoA measurements. As expected, the results show that for both techniques the AoA accuracy depends on the signal-to-noise ratio also in terms of the number of exploited received signal samples. For the passive radar, very accurate estimates are obtained; however, loss of detections can appear only when the rate of the Access Point packets is strongly reduced. In contrast, device-based estimates accuracy is lower, since it suffers of the limited number of emitted packets when the device is not uploading data. However, it allows localization also of stationary targets, which is impossible for the passive radar. This suggests that the two techniques are complementary and their fusion could provide a sensibly increase performance with respect to the individual techniques.

Passive WiFi Radar for Human Sensing Using a Stand-Alone Access Point

Abstract: Human sensing using WiFi signal transmissions is attracting significant attention for future applications in e-healthcare, security, and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of channel state information (CSI) data which originates from commodity WiFi access points (APs) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate Orthogonal Frequency-Division Multiplexing (OFDM) signals, or periodic WiFi beacon signals while in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, while a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In this article, we present experimental data which verifies our proposed methods for using any type of signal transmission from a standalone WiFi device, and demonstrate the capability for human activity sensing.

Future Millimeter-Wave Indoor Systems: A Blueprint for Joint Communication and Sensing

Abstract: Millimeter-wave (mm-wave) technology is emerging as a de facto enabler for next-generation high-rate communications. We propose that a unified mm-wave system for combined communication and robust sensing will turbocharge the capabilities of application domains from in-home digital health to new possibilities for building analytics.

Two-stage clutter and interference cancellation method in passive bistatic radar

Abstract: Target echoes are inevitably contaminated by the direct-path signal, multipath signal and possible interference in passive bistatic radar (PBR). Cancellation performance is deteriorated as the interference signal is irrelevant to the transmitted signal and the target echoes are submerged by in the sidelobe of the spread clutter echoes. In this study, the cancellation problem is addressed, involving clutter and interference. In this method, the spread clutter echoes are cancelled by constructing a tailored clutter subspace. Besides, the interference is also cancelled by constructing a tailored interference subspace. The sample of the interference signal is obtained from the beam channel after clutter cancellation. The performance of the proposed method is first verified by the simulation result. Then its effectiveness is also demonstrated with the application of the real data collected from an experimental PBR system.

Physical Activity Sensing via Stand-Alone WiFi Device

Abstract: WiFi signals for physical activity sensing show great practical potentials for pervasive healthcare applications due to the widespread WiFi deployments and high levels of public acceptance of such systems. Traditionally, WiFi-based sensing uses the Channel State Information (CSI) from an off-the-shelf WiFi Access Point (AP) which transmits signals that have high pulse repetition frequencies. However, when there are no users on the local network only beacon signals are transmitted from the WiFi AP which significantly deteriorates the sensitivity and specificity of such systems. Surprisingly, WiFi based sensing under these conditions have received little attention given that WiFi APs are frequently in idle state. This paper presents a practical system based on passive radar techniques which does not require any special setup or firmware changes to be able to work with any commercial WiFi device. To cope with the low duty cycles associated with beacon signal transmissions, a modified Cross Ambiguity Function (CAF) has been proposed to reduce redundant samples. In addition, an external device has been developed to send WiFi probe request signals which stimulates an idle AP to transmit WiFi probe responses, thus generate usable transmission signals for sensing applications without the need to authenticate and join the network. Detection performance shows that the proposed concept can significantly improve activity detection and is a viable candidate in future healthcare applications.

Fusing active and passive measurements for drone localization

Abstract: This paper investigates the joint exploitation of Wi-Fi based Passive Bistatic Radar (PBR) and Wi-Fi based Passive Source Location (PSL) for drone localization. The inherent features of the two strategies and the results obtained from their comparison on experimental data show an interesting complementarity between them. Following this consideration, a proper sensor fusion strategy combining these two methodologies is investigated in order to achieve improved results in terms of positioning capability. The three strategies (PBR, PSL and sensor fusion) are evaluated against experimental data. The comparison between the use of the sensor fusion approach and the localization based on a single sensor (PBR or PSL) shows the benefits coming from the exploitation of multiple sensors.