| 1. |
- Etzlinger, B., et al.
(författare)
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Cooperative simultaneous localization and synchronization: Toward a low-cost hardware implementation
- 2014
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Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. - 9781479914814 ; , s. 33-36
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Konferensbidrag (refereegranskat)abstract
- Cooperative sensor self-localization (CSL) in wireless networks usually requires the nodes to be equipped with specific ranging hardware including ultra-wideband or ultrasonic distance sensors. Such designs are not suitable for application in low-cost, low-power sensor networks. Here, we demonstrate how low-cost, low-power, asynchronous sensor nodes can be used to perform CSL (and, simultaneously, distributed synchronization) by means of time-stamped communication without additional ranging hardware. Our method combines a belief propagation message passing algorithm for cooperative simultaneous localization and synchronization (CoSLAS) with a MAC-layer time stamping scheme.We validate the models underlying the CoSLAS algorithm by means of measurements, and we demonstrate that the localization accuracy achieved by our hardware implementation is far better than that corresponding to the time resolution and measurement errors of the hardware.
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| 2. |
- Panahi, Ashkan, 1986, et al.
(författare)
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Basis pursuit over continuum applied to range-Doppler estimation problem
- 2014
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Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. - 9781479914814 ; , s. 381-384
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Konferensbidrag (refereegranskat)abstract
- Sparse estimation and compressive sensing techniques have been recently considered for radar estimation problems. It is frequently observed that these methods are robust to model uncertainties and substantially improve performance in scenarios with a low signal-to-noise. However, since current sparsity-based techniques are computationally costly and require a suitable discretization (grid), which strongly restricts resolution, they practically receive less attention. In this work, we present an application of a new sparsity-based technique to the specific problem of range-Doppler estimation. The method, generalizing basis pursuit, is less computationally complex and its performance is independent of the grid selection. We demonstrate that the proposed technique can improve estimation performance in difficult cases, as compared to the SAGE technique.
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| 3. |
- Ström, Marie, 1984, et al.
(författare)
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Wideband waveform design for clutter suppression
- 2014
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Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. - 9781479914814 ; :Art. no. 6882399, s. 297-300
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Konferensbidrag (refereegranskat)abstract
- Modern signal generators offer the capability to synthesize arbitrary wideband waveforms for radar and sonar applications. This makes it possible to optimize signals for specific purposes or scenarios. Herein, we discuss how to design a wideband waveform for clutter suppression. We formulate an optimization in terms of mainlobe and sidelobe properties of the ambiguity function, then we provide an approximate solution based on convex relaxation. Numerical evaluation shows the advantage of selecting a smart signal compared to a conventional waveform design. The advantages are shown as a lower probability of false alarm and a higher probability of correct target detection.
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| 4. |
- Björklund, Svante, et al.
(författare)
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A three-dimensional displaced phase center antenna condition for clutter cancellation
- 2014
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Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - : IEEE. - 9781479914814 ; , s. 305-308
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Konferensbidrag (refereegranskat)abstract
- In moving radar, e.g. airborne radar, the clutter from land and sea needs to be suppressed in order to detect the target. One approach to total cancellation of the clutter is Displaced Phase Center Antenna (DPCA). DPCA assumes that the antenna elements are positioned on a line parallel to the velocity vector of the radar platform so that the elements can take each others positions at different points of times. This paper theoretically investigates if it is possible with other antenna element positions, e.g. in three dimensions, for a total cancellation of the clutter. We arrive at a condition which conforms to the principle that the elements should take each others positions at different times but allows other antennas than the single line parallel to the velocity vector. Our condition could be used as constraints in an optimization problem where the target signal performance is optimized. The multipulse DPCA condition is one solution to our condition.We also give two examples of non-linear antennas fulfilling our condition.
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| 5. |
- Ciuonzo, Domenico, et al.
(författare)
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Massive MIMO meets decision fusion : Decode-and-fuse vs. decode-then-fuse
- 2014
-
Ingår i: 2014 IEEE 8TH Sensor Array and Multichannel Signal Processing Workshop (SAM). - 9781479914814 ; , s. 265-268
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Konferensbidrag (refereegranskat)abstract
- We study channel-aware decision fusion over a multiple-input multiple-output (MIMO) channel in the large-array regime at the decision-fusion center (DFC). Inhomogeneous large-scale fading between the sensors and the DFC is consider in addition to the small-scale fading, and pilot-based channel estimation is performed at the DFC. Linear processing techniques are analyzed in order to design low-complexity alternatives to the optimum log-likelihood ratio test (LLRT). Performance evaluation based on Monte Carlo simulations are presented.
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| 6. |
- Malek Mohammadi, Mohammadreza, et al.
(författare)
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DOA estimation in partially correlated noise using low-rank/sparse matrix decomposition
- 2014
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Ingår i: 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM). - : IEEE Computer Society. - 9781479914814 ; , s. 373-376
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of direction-of-arrival (DOA) estimation in unknown partially correlated noise environments where the noise covariance matrix is sparse. A sparse noise covariance matrix is a common model for a sparse array of sensors consisted of several widely separated subarrays. Since interelement spacing among sensors in a subarray is small, the noise in the subarray is in general spatially correlated, while, due to large distances between subarrays, the noise between them is uncorrelated. Consequently, the noise covariance matrix of such an array has a block diagonal structure which is indeed sparse. Moreover, in an ordinary nonsparse array, because of small distance between adjacent sensors, there is noise coupling between neighboring sensors, whereas one can assume that non-adjacent sensors have spatially uncorrelated noise which makes again the array noise covariance matrix sparse. Utilizing some recently available tools in low-rank/sparse matrix decomposition, matrix completion, and sparse representation, we propose a novel method which can resolve possibly correlated or even coherent sources in the aforementioned partly correlated noise. In particular, when the sources are uncorrelated, our approach involves solving a second-order cone programming (SOCP), and if they are correlated or coherent, one needs to solve a computationally harder convex program. We demonstrate the effectiveness of the proposed algorithm by numerical simulations and comparison to the Cramer-Rao bound (CRB).
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| 7. |
- Mannesson, Anders, et al.
(författare)
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Optimal Virtual Array Length Under Position Imperfections
- 2014
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Ingår i: [Host publication title missing]. - 1551-2282 .- 2151-870X. ; , s. 5-8
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Konferensbidrag (refereegranskat)abstract
- This article contains a study of how spatial errors and receiver imperfections affect the angle of arrival estimation accuracy for virtual antenna arrays. A virtual antenna array consists of one receiver element whose location is tracked as the element is moved and in this work, linear arrays are studied. If the location of the receiver is tracked using an inertial measurement unit, an interesting trade-off emerges. The array should extend as far as possible but since the position estimates from the unaided inertial measurement unit become increasingly uncertain over time, the angle of arrival estimation will deteriorate. Several algorithms are available for estimating the angle of arrival in such a scenario but the one used for evaluation here is a sparse enforcing least squares method.
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| 8. |
- Muller, A., et al.
(författare)
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Efficient linear precoding for massive MIMO systems using truncated polynomial expansion
- 2014
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Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 9781479914814 ; , s. 273-276
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Konferensbidrag (refereegranskat)abstract
- Massive multiple-input multiple-output (MIMO) techniques have been proposed as a solution to satisfy many requirements of next generation cellular systems. One downside of massive MIMO is the increased complexity of computing the precoding, especially since the relatively 'antenna-efficient' regularized zero-forcing (RZF) is preferred to simple maximum ratio transmission. We develop in this paper a new class of precoders for single-cell massive MIMO systems. It is based on truncated polynomial expansion (TPE) and mimics the advantages of RZF, while offering reduced and scalable computational complexity that can be implemented in a convenient parallel fashion. Using random matrix theory we provide a closed-form expression of the signal-to-interference-and-noise ratio under TPE precoding and compare it to previous works on RZF. Furthermore, the sum rate maximizing polynomial coefficients in TPE precoding are calculated. By simulation, we find that to maintain a fixed peruser rate loss as compared to RZF, the polynomial degree does not need to scale with the system, but it should be increased with the quality of the channel knowledge and signal-to-noise ratio.
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