We have devised a greedy method for finding solutions to the sparse Deconvolution Approach for the Mapping of Acoustic Sources inverse problem using a variant of Orthogonal Matching Pursuit. The algorithm has two stages, wherein the first stage consists of selecting a subset of the basis vectors iteratively via a regularized inverse of the point spread function, and the second stage consists of constructing point source solutions using this basis subset and its coefficients via hierarchical agglomerative clustering. We have evaluated the algorithm on both synthetic and real data, and show that the overall accuracy in terms of direction of arrival and reconstructed source power is better than four other state of the art methods.

Real time radar imaging is desirable in a number of applications like robot navigation and land mine detection. However, Synthetic Aperture Radar (SAR) techniques may be hard to speed up due to the inherent scanning required. Achieving a high frame rate in radar imaging necessitates the use of a physical aperture instead of the often used SAR and thus the need for fairly compact and cost effective equipment arises. This paper investigates the use of nine single chip transceivers exploring both RX and TX beamforming in close range for frame rate.

Ultrasound positioning systems for indoor use can be distinguished by what kind of information they extract from the received signal. Time-of-flight (TOF) is measured with reference to a radio signal to get ultrasound time-of- arrival (TOA). Line-of-sight to three or more nodes is required for 3D positioning. Accuracy is in the cm or sub-cm range. A radio-free alternative can be made if time-difference-of-arrival (TDOA) is measured instead. Coding of the pulse is often used in order to allow simultaneous transmission from multiple transmitters. The simplest received signal strength (RSS) systems are binary and will just determine if the ultrasound signal can be detected or not. This is used on its own for room-level positioning. Another important application is in assisting RSS-based RF-systems such as WLAN positioning. The ultrasound RSS-system helps reduce the number of large errors (5-10 m) of the WLAN-system. Such systems have recently been deployed world-wide today by companies like Sonitor and Aeroscout. It has also just been demonstrated that RSS-based ultrasound positioning can be done with accuracies in the 10 cm range. This parallels the ubiquitous RF-based RSS systems and requires a propagation model. For ultrasound the model involves spherical spreading and absorption. There are also hybrid systems where the entire echo structure in the time history of the received signal is analyzed. Both the amplitude and time information are used in order to obtain a position of the node using only a single transmitter.

The objective of this paper is to analyze the performance of an indoor positioning system based on the ultrasound level, usually called RSSI (received signal strength indicator). This paper builds on past experiences where RSSI values from beams sent in different directions were compared [1]. There relative values of RSSI were measured but herein we go one step further and perform localization based on absolute values of RSSI. The use of RSSI could potentially mean a simplification of the traditional positioning systems design in relation to other methods such as TOF (time-of-flight), being an alternative and interesting method for positioning. The distance between nodes (transmitter and receiver) is estimated from RSSI values using a signal propagation model in which the power losses due to the spherical divergence and atmospheric absorption are considered. However, in real conditions, other factors related to the characteristics of the nodes have their impact on RSSI measurements. One of them has to do with the beamwidth of the transducers. The signal strength will be conditioned by the angle of incidence of the transducers when these have a narrow beamwidth. On the other hand, the common use of wireless nodes in the deployment of these systems leads to the RSSI measurement to be affected by the battery level in the nodes. These effects introduce significant errors in the distance estimation and therefore on the localization precision. Herein we propose a mechanism for modeling the power loss due to the orientation of the ultrasonic transducers, as well as an algorithm to compensate for the effect of the variations in the battery level on RSSI measurements. Some experimental results have been obtained with 5 nodes (4 transmitters and 1 receiver) to show the quality of our compensation using a real positioning system. Location errors smaller than 10 cm for each coordinate are obtained in contrast to errors of several meters which are normally attained in this kind of systems based on RF (radio frequency) RSSI-values.

Spherical microphone arrays are suited for phase mode processing, which complements spatio-temporal processing and often simplifies both the understanding and development of different beamforming techniques. Since a spherical geometry cannot benefit directly from methods specially developed for uniform linear arrays, the weight and algorithm design for spherical arrays is mostly optimized and found numerically. One of the exceptions is a recent study that develops the well-known Dolph-Chebyshev weights in the phase mode framework. We show that for the case of a symmetric response, the spherical and linear array geometries are one-to-one, related through a herein developed linear transformation. With this transformation, uniform linear array specific processing techniques become readily available in closed form for spherical array processing. Any uniform linear array weight design technique can be directly applied to spherical arrays. We also show how this transformation can be used to generate virtual uniform linear array data from spherical array data, enabling us to apply uniform linear array specific adaptive algorithms to spherical arrays.

Minimum variance (MV) based beamforming techniques have been successfully applied to medical ultrasound imaging. These adaptive methods offer higher lateral resolution, lower sidelobes, and better definition of edges compared to delay and sum beamforming (DAS). In standard medical ultrasound, the bone surface is often visualized poorly, and the boundaries region appears unclear. This may happen due to fundamental limitations of the DAS beamformer, and different artifacts due to, e.g., specular reflection, and shadowing. The latter can degrade the robustness of the MV beamformers as the statistics across the imaging aperture is violated because of the obstruction of the imaging beams. In this study, we employ forward/backward averaging to improve the robustness of the MV beamforming techniques. Further, we use an eigen-spaced minimum variance technique (ESMV) to enhance the edge detection of hard tissues. In simulation, in vitro, and in vivo studies, we show that performance of the ESMV beamformer depends on estimation of the signal subspace rank. The lower ranks of the signal subspace can enhance edges and reduce noise in ultrasound images but the speckle pattern can be distorted.

As time-delay based ultrasound positioning often is noise sensitive at long ranges, the goal of the research reported here is to achieve sub-room ultrasound positioning with other methods. By combining a portable ultrasound receiver which measures signal strength with a transmitter array that sends steered, coded beams inside a room, the tag can determine which beam it is located in and carry out fine-positioning. The concept is demonstrated in an experiment using a 40 kHz system with 4-7 transmitter elements.

We have tested an indoor data communications system based on ultrasound which is the core of an indoor positioning system. Unlike most other such systems, it relies on ultrasound alone. No radio or infrared channel nor any tether is used for communication. The main hardware components are a tag and a detector. The detector has an Ethernet interface and uses digital signal processing to cope with the acoustic environment and its noise, reverberations and Doppler shift. The attainable range is 10-20 meters and, by making a comparison with the range for speech, we find that the range predictions are consistent with our experience. The channel efficiency of the system is found to be somewhat less than for human speech which also has to deal with a similar environment. This comparison is done by using the Shannon channel capacity theorem.

One of the most promising approaches to construct sparse arrays is sparse periodic arrays. The transmitter, Tx, and receiver, Rx, are constructed with different element spacing, both having grating lobes in the radiation pattern occurring at different angles. These are only partially suppressed in the two-way radiation pattern. We present a simple approach to suppress the grating lobes even further. The enhancement is obtained by forcing zeros in the Tx radiation pattern at the occurrence of the Rx grating lobes and vice versa. Weighting of a few of the outer elements has proven sufficient. Enhancement of 30 dB is found for an array with 64 elements aperture with half the elements active. Only the outer group of elements had non-unity weights. The result is comparable to or better than a dense unity weighted array. If desired, additional weighting can be applied.

Rule-based thinning and element placement methods are first discussed. This includes random arrays, binned random arrays, periodic thinning, element shadowing properties, and fractal arrays. The algorithmic optimization of layouts of 1-D and 2-D sparse arrays is presented and it is empirically derived that the peak sidelobe level for an array with K active elements is proportional to 1/K for a 1-D array and 2/K for a 2-D array. The constant of proportionality depends strongly on the width of the mainlobe relative to that of the full array. With an increase of 50%, the constant is 1.5.

Starts by giving the medical imaging modalities that are in practical use and lists several of the new medical imaging modalities under development. The remainder of the paper is concentrated on progress in MR imaging, ultrasound imaging and X-ray CT imaging. These modalities are major radiological imaging tools, which will have growing significance in the next decade. They are surpassed only by ordinary X-ray projection imaging, which is much more static in its development. Particular attention is given to applications where image processing and image analysis tasks are needed.

A high-performance processing facility for synthetic aperture radar (SAR) is described. The SAR processor is designed for the ERS-1 remote sensing satellite and will process a 100-km by 100-km scene in less than eight minutes. This is three times the throughput of comparable facilities. The SAR processor is built around a 320-MFLOP parallel processor. The front-end processor is a superminicomputer, which provides the input/output capacity necessary to avoid idle time in the parallel processor, and more than 1-Gbyte storage. The design of hardware and software for the front-end processor is given as well as a brief description of the parallel processor. The output products and processing algorithms are also given along with system design philosophy.>

Distance measures between a reference signal and the autoregressive estimate are used as an objective reference for comparing autoregressive order-determining criteria. The distance measures discussed are the rms log spectral deviation, its normalized cepstral distance approximation, the normalized autoregressive transfer function error, the equivalent Itakura distance, and the average squared prediction error. Using them it is found that the AIC criterion is one of the best criteria and performs better than the consistent Schwartz' and Hannan and Quinn's criteria.

When the cross spectrum is estimated where there is a delay between the two input signals, it is shown that the corresponding misalignment gives both magnitude and phase errors. The existence of the phase error is a new result. The importance of the errors depend on the algorithms used. It is shown that the misalignment may either be given by the total observation time or by the section length in the processor. In the latter case the effect of phase errors must be considered when the section length is low, that is when the cross spectrum is smooth, requiring little resolution.

The formula for the spectral moments is expanded in a series consisting of autocorrelation terms. By using the autocorrelation extrapolation inherent in the maximum entropy method (MEM) for spectral analysis, it is found that good estimates of the moments can be found from a very Iow order autoregressive model.

The main objective of this work has been to add the model for fricative excitation to the LPC synthesis model. From the LPC model one finds the acoustic tube section with the greatest constriction and adds a modulated noise signal. The results from this model demonstrate that one is able to produce noise bursts at the right time instants that are shorter than the frame length. This gives a more natural sound for certain phonemes, but adds a quantization type of background noise.