Sensor Radar for Object Tracking

Chiani, Marco; Giorgetti, Andrea; Paolini, Enrico

doi:10.1109/jproc.2018.2819697

Cited by 98 publications

(62 citation statements)

References 141 publications

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“…More sophisticated mechanisms, like one in Chiani et al, 38 implement a non-cooperative target tracking framework for an indoor environment using impulse radio (IR) and ultra-wideband (UWB) technology. A radar sensor network (RSN) coupled with signal processing steps is used to achieve high localization accuracy.…”

Section: Target Tracking In Iiotmentioning

confidence: 99%

Exploiting cooperative sensing for accurate target tracking in industrial Internet of things

Khan

Rahman

et al. 2019

International Journal of Distributed Sensor Networks

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Wireless sensor networks are a cornerstone of the Internet of things with many applications. An important aspect of such applications is target tracking using self-positioned known sensor nodes. Over the years, many schemes have been proposed to locate and track the target path. However, accuracy and reliable tracking remain an open area of research. In this article, we propose a dynamic cooperative multilateral sensing scheme for indoor industrial environments to improve target localization and tracking accuracy. The scheme is designed to select reliable nodes based on the distance between nodes within-cluster and to the target for reduced positioning error. Furthermore, a cluster node is dynamically selected based on distance from the base station. We simulate the proposed technique in scenarios with tracking at regular intervals and with the complete path. Furthermore, the performance of the scheme is also tested under different sensor coverage areas. The results show that the proposed scheme provides better target tracking with up to 19% higher accuracy in comparison to the traditional trilateration scheme.

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Section: Target Tracking In Iiotmentioning

confidence: 99%

Exploiting cooperative sensing for accurate target tracking in industrial Internet of things

Khan

Rahman

et al. 2019

International Journal of Distributed Sensor Networks

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“…Assuming the StD composed by independent symbols, if p = OSF the rows of Y tend to be independent, while columns are correlated. 2 Alternatively, some tests are based on the sample correlation matrix obtained by normalizing the SCM as…”

Section: Time-domain Representation: Sample Covariancementioning

confidence: 99%

“…The performance of frequency-domain detectors introduced in Section 3.1 depends on the total number of samples collected N . Setting N , it is possible to tradeoff between N fft and N avg for the estimation of the PSD in (2). In particular, in the following, we set N = 1600, and we vary N fft and N avg pairs such that N avg = ⌊N/N fft ⌋.…”

Section: Parameters Settingmentioning

confidence: 99%

“…The increasing demand for a limited resource such as the radio-frequency (RF) spectrum is the propelling force toward new ways radio ecosystems can coexist. For example, the coexistence between radar and wireless communications is a topic which recently received increasing attention by the DARPA and the US National Spectrum Consortium [1,2]. To this aim, cognitive radio (CR) systems represent a paradigm to opportunistically access the spectrum provided that the RF environment is monitored through the so-called spectrum sensing (SS) [3,4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Oversampling-Based Signal Detection

Mariani¹,

Giorgetti

Chiani

2019

Int J Wireless Inf Networks

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The availability of inexpensive devices allows nowadays to implement cognitive radio (CR) functionalities in large-scale networks such as the internetof-things (IoT) and future mobile cellular systems. In this paper, we focus on wideband spectrum sensing (WSS) in the presence of oversampling, i.e., the sampling frequency of a digital receiver is larger than the signal bandwidth, where signal detection must take into account the front-end impairments of low-cost devices. Based on the noise model of a software-defined radio (SDR) dongle, we address the problem of robust signal detection in the presence of noise power uncertainty and non-flat noise power spectral density (PSD). In particular, we analyze the receiver operating characteristic (ROC) of several detectors in the presence of such front-end impairments, to assess the performance attainable in a real-world scenario. We propose new frequency-domain detectors, some of which are proven to outperform previously proposed spectrum sensing techniques such as, e.g., eigenvalue-based tests. The

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“…The jitter w is uniformly distributed over the interval [−0.5, 0.5] 2 in order to meet the constraints in (9). Results are compared with two multi-target detection algorithms, namely window threshold (WT) [38] and binary clustering (BC) [17], with CA-CFAR detection. The WT threshold value and the CA-CFAR window length are optimized such that the counting RMSE is minimized at each offline phase.…”

Section: Case Studymentioning

confidence: 99%

Crowd-Centric Counting via Unsupervised Learning

Morselli¹,

Bartoletti²,

Mazuelas

et al. 2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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Counting targets (people or things) within a monitored area is an important task in emerging wireless applications, including those for smart environments, safety, and security. Conventional device-free radio-based systems for counting targets rely on localization and data association (i.e., individual-centric information) to infer the number of targets present in an area (i.e., crowd-centric information). However, many applications (e.g., affluence analytics) require only crowd-centric rather than individual-centric information. Moreover, individual-centric approaches may be inadequate due to the complexity of data association. This paper proposes a new technique for crowdcentric counting of device-free targets based on unsupervised learning, where the number of targets is inferred directly from a low-dimensional representation of the received waveforms. The proposed technique is validated via experimentation using an ultra-wideband sensor radar in an indoor environment. Index terms-Crowd-centric counting, unsupervised learning, dimensionality reduction, ultra-wideband, sensor radar. I. INTRODUCTION Counting people and things is important for crowd sensing and behavior analysis applications, including those related to the Internet-of-Things [1], smart environments [2], social networking [3], and surveillance [4]. For counting tasks, radiobased systems are preferred to image-based systems [5]-[7], especially when privacy, implementation costs, and obstructed line-of-sight represent important limitations. Among radiobased systems, device-free systems are often preferred to systems that rely on dedicated or personal devices [8]-[12]. Device-free systems are based on networks of sensor radars (SRs) that sense the wireless environment and detect targets from signal reflections (backscattering) [13]-[18]. The presence of obstacles and other scatterers (e.g., furniture, walls, and windows) leads to clutter and multipath propagation, which have detrimental effects on the detection performance. These phenomena are particularly severe in indoor environments, where the number of scatterers is large [19]-[21]. Conventional approaches for device-free counting via SRs rely on multi-target localization or tracking [22]-[24], where each SR estimates a set of metrics (e.g., ranges or angles) associated to a single detected target (namely, individualcentric approach). Typically, this approach has a complexity that grows exponentially with the number of targets due

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Sensor Radar for Object Tracking

Cited by 98 publications

References 141 publications

Exploiting cooperative sensing for accurate target tracking in industrial Internet of things

Exploiting cooperative sensing for accurate target tracking in industrial Internet of things

On Oversampling-Based Signal Detection

Crowd-Centric Counting via Unsupervised Learning

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