10,936 research outputs found
Array of sensors: A spatiotemporal-state-space model for target trajectory tracking
In this paper, with the objective of tracking the trajectory of multiple mobile targets, a novel spatiotemporal-state-space model is introduced for an array of sensors distributed in space. Under the wideband assumption, the proposed model incorporates the array geometry in conjunction with crucial target parameters namely (i) ranges, (ii) directions, (iii) velocities and (iv) associated Doppler effects. Computer simulation studies show some representative examples where the proposed model is utilised to track the locations of sources in space with a very high accuracy
Adaptive OFDM Radar for Target Detection and Tracking
We develop algorithms to detect and track targets by employing a wideband orthogonal frequency division multiplexing: OFDM) radar signal. The frequency diversity of the OFDM signal improves the sensing performance since the scattering centers of a target resonate variably at different frequencies. In addition, being a wideband signal, OFDM improves the range resolution and provides spectral efficiency. We first design the spectrum of the OFDM signal to improve the radar\u27s wideband ambiguity function. Our designed waveform enhances the range resolution and motivates us to use adaptive OFDM waveform in specific problems, such as the detection and tracking of targets. We develop methods for detecting a moving target in the presence of multipath, which exist, for example, in urban environments. We exploit the multipath reflections by utilizing different Doppler shifts. We analytically evaluate the asymptotic performance of the detector and adaptively design the OFDM waveform, by maximizing the noncentrality-parameter expression, to further improve the detection performance. Next, we transform the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the -constrained minimal singular value. We solve a constrained multi-objective optimization algorithm to design the OFDM waveform and infer that the resultant signal-energy distribution is in proportion to the distribution of the target energy across different subcarriers. Then, we develop tracking methods for both a single and multiple targets. We propose an tracking method for a low-grazing angle target by realistically modeling different physical and statistical effects, such as the meteorological conditions in the troposphere, curved surface of the earth, and roughness of the sea-surface. To further enhance the tracking performance, we integrate a maximum mutual information based waveform design technique into the tracker. To track multiple targets, we exploit the inherent sparsity on the delay-Doppler plane to develop an computationally efficient procedure. For computational efficiency, we use more prior information to dynamically partition a small portion of the delay-Doppler plane. We utilize the block-sparsity property to propose a block version of the CoSaMP algorithm in the tracking filter
Channel Dynamics and SNR Tracking in Millimeter Wave Cellular Systems
The millimeter wave (mmWave) frequencies are likely to play a significant
role in fifth-generation (5G) cellular systems. A key challenge in developing
systems in these bands is the potential for rapid channel dynamics: since
mmWave signals are blocked by many materials, small changes in the position or
orientation of the handset relative to objects in the environment can cause
large swings in the channel quality. This paper addresses the issue of tracking
the signal to noise ratio (SNR), which is an essential procedure for rate
prediction, handover and radio link failure detection. A simple method for
estimating the SNR from periodic synchronization signals is considered. The
method is then evaluated using real experiments in common blockage scenarios
combined with outdoor statistical models
Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter
In this paper, we propose fusion of dynamic TOA (time of arrival) from
multiple non-coherent detectors like energy detectors operating at sub-Nyquist
rate through Kalman filtering. We also show that by using multiple of these
energy detectors, we can achieve the performance of a digital matched filter
implementation in the AWGN (additive white Gaussian noise) setting. We derive
analytical expression for number of energy detectors needed to achieve the
matched filter performance. We demonstrate in simulation the validity of our
analytical approach. Results indicate that number of energy detectors needed
will be high at low SNRs and converge to a constant number as the SNR
increases. We also study the performance of the strategy proposed using IEEE
802.15.4a CM1 channel model and show in simulation that two sub-Nyquist
detectors are sufficient to match the performance of digital matched filter
An MHT algorithm for UWB radar-based multiple human target tracking
This paper presents a multiple hypothesis tracking
(MHT) framework for tracking the ranges and velocities of
a variable number of moving human targets via a monostatic
ultra-wideband (UWB) radar. The multi-target tracking
(MTT) problem for UWB radar-based human target tracking
differs from traditional applications because of the multitude of
observations (multipath scattering) per target in each scan, due
to the short spatial extent of the transmitted UWB signal pulse
width. We develop an MHT framework for UWB radar-based
multiple human target tracking that extends a previously studied
human tracking algorithm. We present experimental results in
which a monostatic UWB radar tracks both individual and
multiple human targets, even with changing numbers of targets
across radar scans
Jointly Tracking and Separating Speech Sources Using Multiple Features and the generalized labeled multi-Bernoulli Framework
This paper proposes a novel joint multi-speaker tracking-and-separation
method based on the generalized labeled multi-Bernoulli (GLMB) multi-target
tracking filter, using sound mixtures recorded by microphones. Standard
multi-speaker tracking algorithms usually only track speaker locations, and
ambiguity occurs when speakers are spatially close. The proposed multi-feature
GLMB tracking filter treats the set of vectors of associated speaker features
(location, pitch and sound) as the multi-target multi-feature observation,
characterizes transitioning features with corresponding transition models and
overall likelihood function, thus jointly tracks and separates each
multi-feature speaker, and addresses the spatial ambiguity problem. Numerical
evaluation verifies that the proposed method can correctly track locations of
multiple speakers and meanwhile separate speech signals
An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters
A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values
Joint Ultra-wideband and Signal Strength-based Through-building Tracking for Tactical Operations
Accurate device free localization (DFL) based on received signal strength
(RSS) measurements requires placement of radio transceivers on all sides of the
target area. Accuracy degrades dramatically if sensors do not surround the
area. However, law enforcement officers sometimes face situations where it is
not possible or practical to place sensors on all sides of the target room or
building. For example, for an armed subject barricaded in a motel room, police
may be able to place sensors in adjacent rooms, but not in front of the room,
where the subject would see them. In this paper, we show that using two
ultra-wideband (UWB) impulse radios, in addition to multiple RSS sensors,
improves the localization accuracy, particularly on the axis where no sensors
are placed (which we call the x-axis). We introduce three methods for combining
the RSS and UWB data. By using UWB radios together with RSS sensors, it is
still possible to localize a person through walls even when the devices are
placed only on two sides of the target area. Including the data from the UWB
radios can reduce the localization area of uncertainty by more than 60%.Comment: 9 pages, conference submissio
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