Sensory data is only meaningful if correctly associated with originating locations, e.g., in three-dimensional wireless sensor systems inside an aircraft cabin. Received signal strength is a cost-effective option to locate low-cost sensors due to its universal availability, but suffers from coarse ranging accuracy with multiplicative errors. This paper presents a new received signal strength-based approach to locating a large number of low-cost sensors given their possible three-dimensional installation points. Our approach achieves the accuracy which has not been achieved in the literature. The approach first cooperatively locates the sensors in the continuous three-dimensional spaces, then associates the continuous location estimates to the installation points, and finally refines the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous three-dimensional spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local three-dimensional regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated experimentally with signal strength measurements collected in a Fokker 100 passenger plane, achieves 100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly with much shorter execution time. Note to Practitioners—This paper is motivated by a goal to realize various aircraft automation applications through three-dimensional localization and association of wireless sensors. For example, automatic identification of seat locations can be implemented efficiently with wireless sensors, which enables effortless re-association between control buttons in the seats and the corresponding functions after cabin refurbishment. Meanwhile, automatic detection of missing/misplaced safety equipment can be carried out to improve the efficiency of flight preparation. There currently lacks an received signal strength-based three-dimensional sensor association method in indoor environments. The theoretical framework proposed in this paper aims to utilize the readily available received signal strength measurements to associate Commercial-Off-The-Shelf sensors to their possible installation points. This approach avoids the requirement of labor-intensive fingerprinting effort and high-cost equipment. The proposed approach has been tested and evaluated using both synthetic data and real data collected in a passenger plane as a proof of concept.

Sensory data is only meaningful if correctly associated with originating locations. Received signal strength (RSS) is a cost-effective option to locate low-cost sensors due to its universal availability, but yet to be practical because of its coarse ranging accuracy with multiplicative errors. This paper presents a new RSS-based approach to the localization and association of a large number of low-cost sensors given their possible 3D installation locations. The approach addresses the mathematically challenging combinatorial optimization of association by first co-operatively locating the sensors in the continuous 3D spaces, then associating the continuous location estimates to the installation points, and finally refining the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous 3D spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramer-Rao Lower Bounds are derived to specify the local 3D regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated through both computer simulation and lab test. It achieves close-to-100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly.

The performance of cooperative localization using received signal strength (RSS) benefits from accurate radio channel modeling. While log-normal shadowing is commonly used to model the relationship between RSS and range, the RSS error distribution in indoor environments has been observed to be neither normal nor symmetric. In this paper, we propose a skew log-normal channel model, which includes the standard log-normal model as a special case. We further propose an algorithm for using this model for RSS based cooperative localization. The algorithm was evaluated using data from an electro-magnetic simulation of an aircraft cabin, and was shown to generate more accurate node locations compared to the use of log-normal shadowing in the same localization algorithm.

Time of arrival is a commonly used mechanism for geolocation. In many environments, multipath propagation of RF signals limits accuracy. Wide bandwidths reduce the impact of multipath propagation, but frequently results in interference from other devices. When the bandwidth is assembled from several measured sub-bands, there is an additional difficulty of a random phase offset between bands. In this paper, we introduce a compressive sensing scheme which recovers both corrupted samples and phase offsets between sub-bands. For interferers with bandwidths up to 12 MHz, we further show that the proposed scheme leads to improved localisation accuracy compared to previously published techniques.

Cooperative localization can improve both the availability and accuracy of positioning systems, and distributed belief propagation is a promising enabling technology. Difficulties with belief propagation lie in achieving high accuracy without causing high communication overhead and computational complexity. This limits its application in practical systems with mobile nodes that have limited battery size and processing capabilities. In this paper, we propose an efficient cooperative localization algorithm that can be applied to a real indoor localization system with a non-Gaussian ranging error distribution. We first propose an asymmetric double exponential ranging error model based on empirical ranging data. An efficient cooperative localization algorithm based on distributed belief propagation is then proposed. The communication and computational cost is reduced by passing approximate beliefs represented by Gaussian distributions between neighbours and by using an analytical approximation to compute peer-to-peer messages. An extension of the proposed algorithm is also proposed for tracking dynamic nodes. The proposed algorithms are validated on an indoor localization system deployed with 28 nodes covering $8000 \mbox{m}^{2} $ , and are shown to outperform existing algorithms. In particular, the fraction of nodes located to one-meter accuracy is doubled using the proposed ranging error model and localization algorithm.

To maximize power transfer for inductively charging electric vehicles (EVs), charger and battery coils must be aligned. Wireless sensors can be installed to estimate misalignments; however, existing ranging techniques cannot satisfy the precision requirements of the misalignment estimation. We propose a high-precision wireless ranging and misalignment estimation scheme, where high precision is achieved by iteratively measuring, estimating, and aligning the coils. Another key aspect is to convert the nonconvex misalignment estimation to a more tractable problem with a convex objective. We develop a conditional gradient descent method to solve the problem, which performs gradient descent (or conditional gradient descent on the boundary of the search space) and projects out-of-boundary points back into the space. Employing experimentally validated models, we show that our scheme can achieve 92% of the efficiency of perfectly aligned coils in 90% of operations, and tolerate correlated distance measurement errors. In contrast, the prior art is susceptible to correlation, undergoing a significant efficiency degradation of 18.5%.

We derive the posterior Cramér–Rao bound (PCRB) for tracking in anchorless dynamic networks consisting of static nodes at unknown locations and mobile nodes. We show that this is a type of PCRB under parametric constraints where the parameter space cannot be modeled by equation constraints. We propose a novel framework for modeling the constrained parameter manifold, as a factor manifold under the actions of a group. The approach we propose is generic, and can be used to derive lower error bounds for other parameter estimation problems where the parametric constraints cannot be optimally described by equation constraints.

The provision of location-based services with high positional accuracy requires the use of Time of Arrival (TOA)-based techniques. However, existing TOA-based WLAN location service schemes are inefficient due to the individual query and response ranging method employed. We present a highly efficient WLAN location service architecture which includes a modification to the Transmit Opportunity (TXOP) technique in the IEEE 802.11e standard. Our Location Service with TXOP (LSOP) scheme achieves high efficiency by minimizing the number of TOA transmissions and eliminating the contention overhead for TOA messages. The adaptation of TXOP technique also improves location accuracy by protecting TOA messages from collision and by grouping the TOA messages into one compact burst. Our analysis shows that the LSOP scheme achieves the highest location update rate compared to previous schemes. Our simulation results show that the LSOP scheme has minimum impact on data traffic and achieves higher accuracy than the previous schemes. Experimental results demonstrate the degradation in localization performance caused by packet collisions. These results validate that our LSOP scheme, which implements contention-free broadcast of TOA messages with a modified TXOP, provides the best combination of high location update rate, low network load, and high location accuracy compared to other schemes.

The utility of wireless networks for many applications is increased if the locations of the nodes in the network can be tracked based on the measurements between communicating nodes. Many applications, such as tracking fire fighters in large buildings, require the deployment of mobile ad hoc networks. Real-time tracking in such environments is a challenging task, particularly combined with restrictions on computational and communication resources in mobile devices. In this paper, we present a new algorithm using the Bayesian framework for cooperative tracking of nodes, which allows accurate tracking over large areas using only a small number of anchor nodes. The proposed algorithm requires lower computational and communication resources than existing algorithms. Simulation results show that the algorithm performs well with the tracking error being close to the posterior Cramer-Rao lower bound that we derive for cooperative tracking. Experimental results for a network deployed in an indoor office environment with external global position system-referenced anchor nodes are presented. A computationally simple indoor range error model for measurements at the 5.8-GHz ISM band that yields positioning accuracy close to that obtained when using the actual range error distribution is also presented.

The threat of rogue Access Points (APs) has attracted significant attentions from both industrial and academic researchers. However existing solutions focus on rogue AP detection, rather than localization. We propose a Rogue AP Detection and Localization (RAPDL) architecture, which integrates rogue AP detection and localization into one software system. A RAPDL demonstration system has been developed in our laboratory. In the RAPDL system, the monitors identify potential rogue APs, measure their properties and report relevant information to the server. The RAPDL server collects information from all monitors, and runs a localization algorithm to identify and locate the rogue APs. We implemented two localization algorithms in the RAPDL system based on received signal strength (RSS) and compare their performance. Experimental results acquired in an office environment show that RAPDL can detect and locate rogue APs quickly and accurately.

Location tracking is being increasingly used across many applications. While GPS is the most widely used location tracking technology, it is unavailable in many environments such as indoors and underground. Local positioning systems (LPS) that use time of arrival based ranging can provide high accuracy location tracking for many applications. Tracking location using range measurements is a non-linear state estimation problem and the measurement noise is often non-Gaussian in environments where LPS are typically used. Hence a particle filter is an appropriate state estimator for location tracking in LPS. Particle filters are computationally complex and have a serial bottleneck that prevents straightforward parallel implementation. In this paper we present a parallel architecture for the particle filter that can be efficiently implemented in a field programmable gate array or a fixed-point digital signal processor. We show that processing can be divided into up to twenty parallel particle filters to massively increase the processing speed. Mixing between the filters is essential and we present a new algorithm for this that minimises computational complexity and memory bandwidth. Finally, for efficient hardware implementation fixed point arithmetic should be used and we empirically determine the required precision.

802.11e enhanced distributed channel access (EDCA) introduces different parameters to provide differentiated QoS support for various traffic classes. This brings additional difficulties and complexities to the traditional per-slot based Markov chain modeling techniques, where the transmission probability relies on generic time slots. Channel access cycle based modeling, which does not depend on per-slot states, breaks new ground in 802.11e EDCA study. However, the current cycle based model only considers saturated traffic load conditions, while typical network conditions are unsaturated with significant idle periods. We define a new unsaturated channel access cycle that includes a potential idle period and post-backoff procedure, in addition to the backoff procedure and a transmission attempt of the saturated cycle. A new EDCA model for unsaturated traffic conditions is proposed based on this channel access cycle. Our model achieves high accuracy in describing channel access operations over a wide range of traffic conditions. The model solutions, validated by ns-2 simulations, accurately characterize the unique features of 802.11e EDCA in unsaturated traffic conditions.

Wireless tracking is being increasingly used in elite and professional sports for training and performance monitoring. Accurate and high update rate tracking in indoor venues is the challenge that we are addressing. In this paper we propose a new algorithm for network round-trip ranging based on the measurement of time of arrival that supports high update rates and tracks and removes errors caused by changes in propagation delay in the analog electronics. The localization performance of our proposed algorithm is validated by results acquired in a sporting venue using our localization system, which is based on a custom TDMA protocol. We then discuss the issues with implementing the scheme in an 802.11 wireless network, propose an extension to the standard to support our scheme, and present analysis and simulation results to demonstrate the benefit over other schemes.

We present a new method for anchorless localization of mobile nodes in wireless networks using only measured distances between pairs of nodes. The novelty of the algorithm lies in the method to complete the Euclidean distance matrix by inferring the unknown distances such as to align all nodes on a k-hyperplane, where typically k is 2 or 3. The relative locations of the nodes are then computed using multidimensional scaling. We also present experimental results with data collected using our WASP platform to verify the improvement in localization using our algorithm over previously published techniques.

Tracking nodes in a wireless network is beneficial for many applications. This is, however, a challenging task in multihop networks, particularly when combined with the limitations in processing power and communication capability of the nodes. The task is further complicated when rapid deployment of the network is required. In this paper we present a new algorithm for cooperative tracking of nodes, which allows accurate tracking over large indoor areas using only a small number of anchor nodes. The proposed algorithm is based on the multiple model particle filter and addresses several tracking issues not previously addressed in the literature. Further, we present a new indoor ranging error model based on our measurements that is simple, yet widely applicable, and gives much better localization accuracy compared to the standard Gaussian error model. Experimental results using a deployed wireless network, where GPS referenced nodes are used as anchors, demonstrate the effectiveness of the proposed algorithm.

In this paper, we present a low-cost wireless sensor network platform, called wireless ad hoc system for positioning (WASP), that has been developed for high-accuracy localization and tracking. This platform uses the time of arrival (TOA) of beacon signals periodically transmitted by the nodes at known times for localization. The system was designed to have a unique tradeoff between hardware complexity and processing complexity to provide high accuracy at minimal cost in complex radio propagation environments. To enable the system to perform well in realistic environments, it was also necessary to develop novel extensions to existing algorithms for the measurement of TOA, localization, and tracking. In this paper, we describe the architecture, hardware, and algorithms of WASP and present results based on field trials conducted in different radio propagation environments. The results show that WASP achieves a ranging accuracy of 0.15 m outdoors and 0.5 m indoors when around 12 anchor nodes are used. These accuracies are achieved with operating range of up to 200 m outdoors and 30 m indoors. This compares favorably to other published results for systems operating in realistic environments.

Over-the-air programming (OAP) protocols play a key role in wireless sensor network maintenance and task assignment. Most existing OAP protocols are contention based and suffer from collisions and the hidden terminal problem. We propose a novel TDMA based code dissemination service called WCDS that has been implemented on our WASP platform, which is a high performance integrated sensing, communication, and positioning system. WCDS is designed as a background service without interfering with normal network operations, in contrast to the existing OAP protocols that freeze sensing and data service during code download. We implement hand-shaking, pipelining and time slot scheduling to achieve high reliability and fast completion time. Experimental results show that WCDS outperform Deluge by 30% in completion time and up to 40% in energy saving.

In indoor environments, geolocation using Time of Arrival (TOA) estimation must use a wide bandwidth to overcome the effects of multipath propagation. Narrowband interference from other applications is a significant problem. In this paper we investigate a number of signal processing schemes to detect and overcome one or more relatively narrowband interferers. By making use of the limited time duration of the channel impulse response we are able to recover the TOA with high accuracy in most cases, even when the channel frequency response has large amount of missing data due to interference. Our proposed approach treats the corrupted channel frequency response as a non-uniform sampling of the true frequency response and draws on ideas from compressive sensing to recover an estimate of the missing frequency response samples. We also consider the computational complexity of a variety of potential TOA recovery methods and examine their effectiveness as a function of the interferer bandwidth. We demonstrate our method using real data measured in non line-of-sight conditions in a typical office building.

In complex propagation environments with multipath reflections determining the time-of-arrival (TOA) of the line-of-sight (LoS) signal, which is required for localization, is challenging. As a result the localization accuracy of TOA-based systems degrade in such environments. We are pursuing a novel approach where the TOA algorithm returns not one but multiple candidate TOA values as this list is more likely to contain the LoS TOA value and the localization algorithm uses all of these to form a more reliable estimate of the node location. In this paper we present a new algorithm for localization and tracking based on multiple candidate TOA values from each anchor. We show that this is similar to the well-known data association problem in target tracking and exploit this similarity to propose a particle filter based algorithm that has linear computational complexity in the number of anchors. The performance of the algorithm is validated using simulated data.

In many wireless sensor network (WSN) applications the location of the nodes in the network is required. Localization of nodes is difficult in cases where the use of Global Positioning System is not feasible. Such networks will usually contain a small number of anchors at known locations, but their density is sufficiently low that the other nodes cannot determine their location directly from the anchors. Hence cooperative localization within the network is required. In this paper we evaluate the performance of several cooperative localization algorithms using data collected in field trials and show that a modified version of the DV distance algorithm performs significantly better than the other algorithms considered.

This paper considers MIMO-OFDM transmission with low density parity check (LDPC) codes. We employ a low complexity minimum mean-square-error (MMSE) soft-interference-cancelation (SIC) based double iterative receiver (DIR). Results are presented for a real system implementation at 5.2 GHz. We achieve zero packet errors at 90% of the measured indoor locations, when transmitting at 600 Mbit/s, with 15 bit/s/Hz spectral efficiency and 26 dB signal-to-noise ratio. We show that the proposed receiver actually outperforms a list sphere detection (LSD) based single iterative receiver (SIR) at high coding rates, in practice. Investigations reveal that the LSDSIR is adversely affected by the non-Gaussian noise present at the receiver, while at the same time the MMSE-SIC-DIR is better able to handle transmitter noise present in the practical system.

Local positioning systems are valuable for a range of applications where the global positioning system (GPS) is unavailable or unreliable. In this paper work is presented on accurate tracking of athletes in indoor venues using time-of- arrival (TOA) measurements of radio signals exchanged between nodes and an iterative least-squares localization algorithm. The range error characteristics were measured and it is shown that contrary to the usual assumption the distribution is not a function of range, as sources of noise other than at the receiver input dominate except at very low signal levels. It is also shown that the range error does not follow a normal distribution, but has a higher probability of large errors which can result in large localization errors. An algorithm is presented that is robust to these errors, and results presented from measurements in a velodrome demonstrate the effectiveness of the algorithm. The cyclists are constrained to a track which is non-planar, thus two- dimensional localization is inaccurate, yet the number and distribution of nodes prevents accurate three-dimensional localization. An extension to the algorithm for accurate localization on a non-planar surface is also presented and shown to reduce the tracking error.

We propose a scheme for super-resolution estimation of the time of arrival of a radio signal which makes use of a prior model of the radio propagation conditions. The method is based on examination of the leading edge of the channel impulse response, and comparison with a database of leading edge shapes generated by Monte Carlo simulation. We also propose an efficient implementation, which makes the method practical for mobile devices with limited computational power. We verify the performance of the method using actual measured data, taken in a number of different situations, and show improved performance over other super-resolution methods proposed in the literature.

We present the results of a large number of measurements of the indoor radio channel at 5.8 GHz taken in a typical office environment. The time of arrival (TOA) for the measurements are analyzed using two recently proposed super- resolution algorithms, and compared with traditional approaches utilizing the inverse FFT. We find that the performance of the FFT is equal to or better than the super-resolution techniques. Based on a statistical analysis of our measurements, we show that the information content of the signal relating to the TOA is concentrated in just a few coefficients on the leading edge of the impulse response. Drawing on this result, we propose a pattern matching approach for the determination of the TOA which performs significantly better than existing techniques.

We present a novel technique for the detection of spatially multiplexed transmitted symbols in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. The method, termed Selective Detection, observes errors caused by a simple zero-forcing (ZF) on different OFDM sub-carriers, and selectively performs list sphere detection (LSD) on sub-carriers with larger errors. Compared with the full-complexity LSD, the proposed algorithms could significantly reduce the receiver complexity at a small signal-to-noise ratio (SNR) loss. For example, simulation results for a coded MIMO- OFDM system using four transmitters and four receivers with 16 quadrature amplitude modulation (16-QAM) and low density parity check (LDPC) coding show that more than 90% of SNR gain that can be achieved by the use of LSD over ZF can be achieved if only 20% of sub-carriers are intelligently selected for LSD.

This paper presents analysis of performance measurements from a MIMO-OFDM IEEE 802.11n hardware implementation at 5.2 GHz using four transmitters and four receivers. Two spatial multiplexing systems are compared; one which uses a zero-forcing (ZF) detector and the other a list sphere detector (LSD). We show that the measured results do not align with standard prediction based on simulation assuming uncorrelated receiver noise. We show that the discrepancy can be explained by the inclusion of transmitter noise into the channel model. This effect is not included in existing MIMO-OFDM channel models. The measured results from our hardware implementation show successful packet transmission at 600 Mb/s with 15 bits/s/Hz spectral efficiency at 73% coverage for ZF and 84% coverage for LSD with an average receiver signal to noise ratio (SNR) of 26 dB.

In this paper, the uncoded symbol error rate (SER) versus signal to noise ratio (SNR) curves of M-ary quadrature amplitude modulation (M-QAM) multiple-input multiple-output (MIMO) systems using zero-forcing (ZF) or maximum likelihood (ML) detection employing various numbers of antennas are given analytically and empirically. Using the given formulas, the designer of a MIMO system can easily analyse the tradeoff between using ZF or MLD, where ZF is computationally efficient but requires more receivers to achieve the same performance as MLD. An example is given, showing that, for an uncoded MIMO system using QPSK, 16QAM, or 64QAM, to achieve the same or better performance as MLD using 2, 3, and 4 antennas at each end, ZF requires at least 3, 5, and 7 receivers, respectively, at a specified average SER of 10-3

In this paper, the bit error rate (BER) and packet error rate (PER) performance of multiple-input multiple-output orthogonal frequency division multiplexing with bit interleaved coded modulation (MIMO-OFDM-BICM) systems using convolutional, Turbo, or low density parity check (LDPC) coding schemes are evaluated on MIMO-OFDM channels measured in various indoor environments. For a given average signal to noise ratio (SNR) per receiver, the performance of MIMO-OFDM-BICM in a line-of-sight (LoS) environment can vary significantly depending on the existence of scattering, while it is more uniformly distributed in a non-line-of-sight (NLoS) environment. We show that, due to bursty coded bit error characteristics of Turbo and LDPC coding, only marginal coding gain over convolutional coding is observed in terms of BER. However, when PER is compared, a 3 dB coding gain is achieved by the Turbo or LDPC code over the convolutional code.

A test-bed for research in the use of sensor networks for structural health monitoring in aerospace vehicles is presented. An overview of the test-bed configuration for detecting and characterizing micrometeoroid impacts is given, followed by a description of the communications protocols.

The paper presents a new machine learning method to solve the pose estimation problem. The method is based on the soft margin AdaBoost (SMA) algorithm (Ratsch, G. et al., Machine Learning, vol.42, no.3, p.287-320, 2001). The AdaBoost algorithm has been used with great success as a high-level learning procedure to obtain strong classifiers from weak classifiers, but it tends to overfit in the presence of very noisy data. Recent studies show that a regularised AdaBoost algorithm, such as SMA, can achieve better results for noisy data. We propose two new techniques for classifying the image as frontal (face is within /spl plusmn/25/spl deg/) or profile; one is based on the original Adaboost algorithm, the other on SMA. It is shown that the SMA based technique is more robust than the one based on the original AdaBoost, and yields better results. All the techniques were trained and tested on four databases. Experimental results show that the classification error of the SMA method is less than 2% for suitable parameters, regardless of the conditions associated with the face. In addition, the method performs extremely well even when some facial features become partially or wholly occluded.

We present a new approach to modelling variable bit rate (VBR) coded video sources in asynchronous transfer mode (ATM) networks. Unlike the existing methods which model the number of cells generated by the coder for a sequence of video frames, the new approach improves modelling accuracy by considering the characteristics of different cells generated by the coder, and modelling the number of cells in each type of macroblocks of a frame separately. The model is tested by comparing the cell loss rate in simulation of an ATM switch to cell loss rate produced when traces generated by the model are used as the source. Comparison with the existing models are performed in the area of measuring the quality of their predictions for network performance and the grade of service experienced by a user.

We present a new approach to modelling variable bit rate (VBR) coded video sources in asynchronous transfer mode (ATM) networks. Unlike the existing methods which model the number of cells generated by the coder for a sequence of video frames, the new approach improves the modelling accuracy by considering the characteristics of different cells generated by the coder, and modelling the number of cells in each type of macroblocks of a frame separately. The model is tested by comparing the autocorrelation function and mean queue size in the buffer by simulation of an ATM switch to the mean queue size produced when traces generated by the model are used as the source. Comparisons with the existing models are performed in the area of measuring the quality of their predictions for the network performance and the grade of service experienced by a user.

There are many practical problems in which it is required to detect and characterize hidden structures or remote objects by virtue of the scattered acoustic or electromagnetic fields they generate. It remains an open question, however, as to which reconstruction algorithms offer the most informative images for a given set of field measurements. Commonly used time-domain beamforming techniques, and their equivalent frequency-domain implementations, are conceptually simple and stable in the presence of noise, however, large proportions of missing measurements can quickly degrade the image quality. We apply a new algorithm based on the maximum entropy method (MEM) to the reconstruction of images from sparsely sampled coherent field data. The general principles and limitations of the new method are discussed in the framework of regularization theory, and the results of monostatic imaging experiments confirm that superior resolution and artifact suppression are obtained relative to a commonly used linear inverse filtering approach.

Patient motion during data acquisition in magnetic resonance imaging causes artifacts in the reconstructed image, which appear as blurring and ghost repetitions of the moving structures. The acquired data are related to the image by the Fourier transform. With translational motion perpendicular to the imaging phase, the magnitude of the acquired data is corrupted. This magnitude retrieval problem can almost always be solved in theory provided the field of view is larger than the image. The restrictions on the manner in which the magnitude may be corrupted result in the problem being linear. The corruption is determined using a least squares estimate, then the data is corrected. The implementation of the algorithm allows rapid correction of images.>

Patient motion during data acquisition in magnetic resonance imaging causes artifacts in the reconstructed image, which for two-dimensional Fourier transform imaging techniques appear as blurring and ghost repetitions of the moving structures. T. Mitsa et al. (1990) proposed a technique for suppressing artifacts from periodic motion along the slice selection axis. A different approach to the same problem is presented which is not restricted to periodic motion. The algorithm is verified using a simulated phantom and motion. It is also shown to perform well in the presence of noise and motion within the imaging plane.