2D localization with WiFi passive radar and device-based techniques: an analysis of target measurements accuracy

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

WiFi emission-based vs passive radar localization of human targets

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

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

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

Wi-Fi sensing: fusion of non-cooperative and device-based RF sensors for short-range localization

Through the years, target localization has captured the attention of both academic and industrial worlds, thanks to the huge amount of applications which require the knowledge of the position information. Several works can be found on this topic, where the target localization has been addressed in different ways, depending on the type of target, the specific application and the surrounding scenario. The main goal of this thesis is the definition of innovative methodologies able to solve the problem of the localization of human targets and small objects in local area environments in any operating conditions. In addition to the achievement of important improvements in positioning accuracy, we are also interested in performing the localization for the entire observation time where the target stays in the area of interest. To achieve this result, in this work we decided to propose the joint use of different positioning techniques, based on their fusion in a unified system. The advantage of this fusion lies in the possibility of compensating for the intrinsic limitations of each proposed methodology, especially when complementary techniques are employed. Two different sensors are considered in this work. Both exploit the Wi-Fi transmissions, based on the IEEE 802.11 Standard, therefore also the same receiver can be employed to receive measurements and information about the target present in the area of interest from multiple sensors, without increasing the complexity of the receiving system. Specifically, the first candidate to be used is the Passive Bistatic Radar (PBR) that exploits the Access Point (AP) as illuminator of opportunity. Due to the possibility to obtain the human target position without the necessity for the target to carry a device, this technique can be inserted into the group of the “Device-free localization” methodologies. It makes the WiFi-based passive radar attractive for local area surveillance and monitoring applications, especially where the targets cannot be assumed to be cooperative, as in typical security applications. With reference to the second sensor, the Passive Source Location (PSL) is another possible strategy to estimate the target position. In contrast to the PBR, this is a device-based technique that uses the device transmissions to perform the localization of the specific target. Considering the characteristics of the proposed strategies, it is evident that they present complementary aspects. We can take advantage from this complementarity in several ways. Firstly, due to the Time Division Multiple Access (TDMA) approach used in the Wi-Fi Standard, devices and AP cannot transmit simultaneously, so we can compensate for the lack of signals from one sensor with the measures estimated by the other one. Secondly, we can use the device-based strategy when the target is stationary, and the Passive Radar cannot estimate its position because of the cancellation stage performed during the processing. On the other hand, the Passive Radar is necessary when the target does not carry an active mobile device, or it does not want to be localized (surveillance and monitoring activities). Finally, we can discriminate even very closely spaced target (if both carry an active mobile device) thanks to the possibility to read the MAC Address written into the packets of their devices. The first part of this thesis is dedicated to the characterization of the single sensors, based on the description of the measurement extraction and the evaluation of the related positioning techniques. With respect to the measurement extraction, the PBR provides the target position through the combination of different sets of measures as range/Doppler/Angle of Arrival (AoA). For the PSL, the Time Difference of Arrival (TDoA) and the AoA can be exploited for the same purpose. Since the properties of the PBR have been extensively defined by our research group in the past, in this work more attention has been dedicated to the PSL description. In particular, proper techniques for measurement estimation are reviewed and innovative techniques for TDoA estimation of the PSL sensor are proposed, which provide improved performance with respect to existing techniques. The accuracies achieved with different positioning techniques exploiting several combinations of the estimated measurements are then evaluated. The results show that in short range applications it is desirable to use only AoA measurements, if possible. After the characterization of the sensors, the localization performance of the two techniques are analyzed and compared. This analysis has shown both the effectiveness of the two sensors in target localization and their inherent limitations. In particular, we have studied the relationship between data traffic conditions and performance, and we have seen that it is strictly linked to the number of data available for the estimation of the measures of interest. In addition, the complementarity of the two methodologies has been demonstrated through the evaluation on experimental data acquired in appropriate measurement campaigns, in different network traffic conditions. In this phase, a tracking stage has not been performed. In order to improve the localization performance and carry out the desired sensor fusion, the second part of the thesis has been dedicated to the definition of innovative techniques for target tracking which exploit the characteristics of the employed sensors. Specifically, a new Sensor Fusion tracking filter is proposed. It uses a modified version of the Interacting Multiple Model (IMM) approach, where a Modified Innovation (MI) is introduced, together with Data Fusion techniques. In particular, in this strategy the information related to the presence or absence of the PBR estimates is used to help the choice between the employed filters, in order to improve the localization performance of human targets in the typical “stop & go” motion scenario. The performance of the proposed strategy has been evaluated on both simulated and experimental data. The performance has shown that the IMM-MI outperforms the other strategies, since it provides the best performance in terms of positioning accuracy, target motion recognition capability and percentage of acquisition time covered by this strategy

Surgery versus Physiotherapy for Stress Urinary Incontinence

<p>BackgroundPhysiotherapy involving pelvic-floor muscle training is advocated as first-line treatment for stress urinary incontinence; midurethral-sling surgery is generally recommended when physiotherapy is unsuccessful. Data are lacking from randomized trials comparing these two options as initial therapy.</p><p>MethodsWe performed a multicenter, randomized trial to compare physiotherapy and midurethral-sling surgery in women with stress urinary incontinence. Crossover between groups was allowed. The primary outcome was subjective improvement, measured by means of the Patient Global Impression of Improvement at 12 months.</p><p>ResultsWe randomly assigned 230 women to the surgery group and 230 women to the physiotherapy group. A total of 49.0% of women in the physiotherapy group and 11.2% of women in the surgery group crossed over to the alternative treatment. In an intention-to-treat analysis, subjective improvement was reported by 90.8% of women in the surgery group and 64.4% of women in the physiotherapy group (absolute difference, 26.4 percentage points; 95% confidence interval [CI], 18.1 to 34.5). The rates of subjective cure were 85.2% in the surgery group and 53.4% in the physiotherapy group (absolute difference, 31.8 percentage points; 95% CI, 22.6 to 40.3); rates of objective cure were 76.5% and 58.8%, respectively (absolute difference, 17.8 percentage points; 95% CI, 7.9 to 27.3). A post hoc per-protocol analysis showed that women who crossed over to the surgery group had outcomes similar to those of women initially assigned to surgery and that both these groups had outcomes superior to those of women who did not cross over to surgery.</p><p>ConclusionsFor women with stress urinary incontinence, initial midurethral-sling surgery, as compared with initial physiotherapy, results in higher rates of subjective improvement and subjective and objective cure at 1 year.</p>