28,131 research outputs found
Geometric Cross-Modal Comparison of Heterogeneous Sensor Data
In this work, we address the problem of cross-modal comparison of aerial data
streams. A variety of simulated automobile trajectories are sensed using two
different modalities: full-motion video, and radio-frequency (RF) signals
received by detectors at various locations. The information represented by the
two modalities is compared using self-similarity matrices (SSMs) corresponding
to time-ordered point clouds in feature spaces of each of these data sources;
we note that these feature spaces can be of entirely different scale and
dimensionality. Several metrics for comparing SSMs are explored, including a
cutting-edge time-warping technique that can simultaneously handle local time
warping and partial matches, while also controlling for the change in geometry
between feature spaces of the two modalities. We note that this technique is
quite general, and does not depend on the choice of modalities. In this
particular setting, we demonstrate that the cross-modal distance between SSMs
corresponding to the same trajectory type is smaller than the cross-modal
distance between SSMs corresponding to distinct trajectory types, and we
formalize this observation via precision-recall metrics in experiments.
Finally, we comment on promising implications of these ideas for future
integration into multiple-hypothesis tracking systems.Comment: 10 pages, 13 figures, Proceedings of IEEE Aeroconf 201
Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices
Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments;
where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range
estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in
delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both
ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient
signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution,
tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on
TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate
an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally,
we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible
acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance
Practical classification of different moving targets using automotive radar and deep neural networks
In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed
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
Using EMD-FrFT filtering to mitigate high power interference in chirp tracking radars
This letter presents a new signal processing subsystem for conventional monopulse tracking radars that offers an improved solution to the problem of dealing with manmade high power interference (jamming). It is based on the hybrid use of empirical mode decomposition (EMD) and fractional Fourier transform (FrFT). EMD-FrFT filtering is carried out for complex noisy radar chirp signals to decrease the signal's noisy components. An improvement in the signal-to-noise ratio (SNR) of up to 18 dB for different target SNRs is achieved using the proposed EMD-FrFT algorithm
C-band radar pulse Doppler error: Its discovery, modeling, and elimination
The discovery of a C Band radar pulse Doppler error is discussed and use of the GEOS 3 satellite's coherent transponder to isolate the error source is described. An analysis of the pulse Doppler tracking loop is presented and a mathematical model for the error was developed. Error correction techniques were developed and are described including implementation details
Detecting Targets above the Earth's Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1
: Global Navigation Satellite System (GNSS) reflected signals can be used to remotely sense
the Earth’s surface, known as GNSS reflectometry (GNSS-R). The GNSS-R technique has been applied
to numerous areas, such as the retrieval of wind speed, and the detection of Earth surface objects.
This work proposes a new application of GNSS-R, namely to detect objects above the Earth’s surface,
such as low Earth orbit (LEO) satellites. To discuss its feasibility, 14 delay Doppler maps (DDMs) are
first presented which contain unusually bright reflected signals as delays shorter than the specular
reflection point over the Earth’s surface. Then, seven possible causes of these anomalies are analysed,
reaching the conclusion that the anomalies are likely due to the signals being reflected from objects
above the Earth’s surface. Next, the positions of the objects are calculated using the delay and
Doppler information, and an appropriate geometry assumption. After that, suspect satellite objects
are searched in the satellite database from Union of Concerned Scientists (UCS). Finally, three objects
have been found to match the delay and Doppler conditions. In the absence of other reasons for these
anomalies, GNSS-R could potentially be used to detect some objects above the Earth’s surface.Peer ReviewedPostprint (published version
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