Clock synchronization and ranging over a wireless network with low communication overhead is a challenging goal with tremendous impact. In this paper, we study the use of time-to-digital converters in wireless sensors, which provides clock synchronization and ranging at negligible communication overhead through a sawtooth signal model for round trip times between two nodes. In particular, we derive Cramér-Rao lower bounds for a linearitzation of the sawtooth signal model, and we thoroughly evaluate simple estimation techniques by simulation, giving clear and concise performance references for this technology.

Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in a supervised learning setting, where the inputs are agnostic to the graph. KRG model predicts targets that are smooth graph signals as over the given graph, given the input when all the signals are deterministic. In this work, we consider the development of a stochastic or Bayesian variant of KRG. Using priors and likelihood functions, our goal is to systematically derive a predictive distribution for the smooth graph signal target given the training data and a new input. We show that this naturally results in a Gaussian process formulation which we call Gaussian Processes over Graphs (GPG). Experiments with real-world datasets show that the performance of GPG is superior to a conventional Gaussian Process (without the graph-structure) for small training data sizes and under noisy training.

We consider backward crosstalk in 2 × 2 transmitters, which is caused by crosstalk from the outputs of the transmitter to the inputs or by the combination of output crosstalk and impedance mismatch. We analyze its impact via feedback networks together with third-order power amplifier non-linearities. We utilize the Bussgang decomposition to express the distorted output signals of the transmitter as a linear transformation of the input plus uncorrelated distortion. The normalized mean-square errors (NMSEs) between the distorted and desired amplified signals are expressed analytically and the optimal closed-form power back-off that minimizes the worst NMSE of the two branches is derived. In the second part of the paper, an achievable spectral efficiency (SE) is presented for the communication from this “dirty” transmitter to another single-antenna receiver. The SE-maximizing precoder is optimally found by exploiting the hardware characteristics. Furthermore, the optimal power back-off is analyzed for two sub-optimal precoders, which either do not exploit any hardware knowledge or only partial knowledge. The simulation results show that the performance of these sub-optimal precoders is close-to-optimal. We also discuss how the analysis in this paper can be extended to transmitters with an arbitrary number of antenna branches.

Smartphone-based driver monitoring is quickly gaining ground as a feasible alternative to competing in-vehicle and aftermarket solutions. Currently the main challenges for data analysts studying smartphone-based driving data stem from the mobility of the smartphone. In this paper, we use kernel-based k-means clustering to infer the placement of smartphones within vehicles. The trip segments are mapped into fifteen different placement clusters. As a part of the presented framework, we discuss practical considerations concerning e.g., trip segmentation, cluster initialization, and parameter selection. The proposed method is evaluated on more than 10 000 kilometers of driving data collected from approximately 200 drivers. To validate the interpretation of the clusters, we compare the data associated with different clusters and relate the results to real-world knowledge of driving behavior. The clusters associated with the label “Held by hand” are shown to display high gyroscope variances, low maximum speeds, low correlations between the measurements from smartphone-embedded and vehicle-fixed accelerometers, and short segment durations.

The effects of hardware-induced crosstalk in MIMO transmitters, subject to nonlinear power amplifier distortion, are considered in this paper. A methodology that provides tractable results and a clear understanding of the effects of crosstalk on transmitter performance is introduced and applied to different transmitter models. In particular, a physically motivated 2 × 2 MIMO transmitter model, which is subjected to input and output crosstalk, is studied in detail, as well as a behavior motivated transmitter model, which is subjected to linear crosstalk. For the latter structure, asymptotic results, when the number of transmitters tends to infinity, are derived. These results provide insight into different 1D and 2D transmitter structures in the massive MIMO scenario. The methodology provides tractable analytical results of the performance of the transmitter. It is shown that the transmitter crosstalk degrades the performance in terms of normalized mean squared error with 3 dB going from a 2 × 2 set-up to a 1D array of a massive amount of transmitters, and an additional 3 dB loss going from a 1D to 2D transmitter structure. Transmitter input power back-off optimization is further studied, with back-off determination that takes the effects of MIMO crosstalk into account in order to increase the energy efficiency of the transmitter.

The expectation-maximization algorithm is a commonly employed tool for system identification. However, for a large set of state-space models, the maximization step cannot be solved analytically. In these situations, a natural remedy is to make use of the expectation-maximization gradient algorithm, i.e., to replace the maximization step by a single iteration of Newton's method. We propose alternative expectation-maximization algorithms that replace the maximization step with a single iteration of some other well-known optimization method. These algorithms parallel the expectation-maximization gradient algorithm while relaxing the assumption of a concave objective function. The benefit of the proposed expectation-maximization algorithms is demonstrated with examples based on standard observation models in tracking and localization.

By arranging a large number of inertial sensors in an array and fusing their measurements, it is possible to create inertial sensor assemblies with a high performance-to-price ratio. Recently, a maximum likelihood estimator for fusing inertial array measurements collected at a given sampling instance was developed. In this paper, the maximum likelihood estimator is extended by introducing a motion model and deriving a maximum a posteriori estimator that jointly estimates the array dynamics at multiple sampling instances. Simulation examples are used to demonstrate that the proposed sensor fusion method have the potential to yield significant improvements in estimation accuracy. Further, by including the motion model, we resolve the sign ambiguity of gyro-free implementations, and thereby open up for implementations based on accelerometer-only arrays.

We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective kernel function by appropriate regularization based on graph smoothness. We show that the resulting optimization problem is shown to be convex and propose an accelerated projected gradient descent based solution. Simulation results using real-world graph signals show efficiency of the multi-kernel based approach over a standard kernel based approach.

The radio frequency transmitter is a key component in contemporary multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing systems. A detailed study of a 2×2 MIMO transmitter subjected to correlated input data streams, nonlinear distortion, thermal noise, and crosstalk is provided by stochastic modeling. The effects of correlated input streams, crosstalk, and nonlinearities are studied in detail and exemplified both by approximate expressions and numerical simulations. Key results include exact and approximate expressions for the normalized mean-squared error (NMSE) for systems with or without digital predistortion; the relationship between NMSE and the signal-to-noise-and-distortion ratio, the properties of the distortion noise, and a novel design for power amplifier back-off for MIMO transmitters subject to crosstalk. The theoretical derivations are illustrated by numerical examples and simulation results, and their relationships to the state-of-the-art research are discussed.

In this paper we have proposed clock error mitigation from the measurements in the scheduled based self localization system. We propose measurement model with clock errors while following a scheduled transmission among anchor nodes. Further, RLS algorithm is proposed to estimate clock error and to calibrate measurements of self localizing node against relative clock errors of anchor nodes. A full-scale experimental validation is provided based on commercial off-the-shelf UWB radios under IEEE-standardized protocols.

Shoe-mounted inertial navigation systems, aka pedestrian dead reckoning or PDR sensors, are being preferred for pedestrian navigation because of the accuracy offered by them. Such shoe sensors are, for example, the obvious choice for real time location systems of first responders. The opensource platform OpenShoe has reported application of multiple IMUs in shoe-mounted PDR sensors to enhance noise performance. In this paper, we present an experimental study of the noise performance and the operating clocks based power consumption of multi-IMU platforms. The noise performances of a multi-IMU system with different combinations of IMUs are studied. It is observed that four-IMU system is best optimized for cost, area and power. Experiments with varying operating clocks frequency are performed on an in-house four-IMU shoe-mounted inertial navigation module (the Oblu module). Based on the outcome, power-optimized operating clock frequencies are obtained. Thus the overall study suggests that by selecting a well-designed operating point, a multi-IMU system can be made cost, size and power efficient without practically affecting its superior positioning performance.

Just as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

The multiple access scheduling decides how the channel is shared among the nodes in the network. Typically scheduling algorithms aims at increasing the channel utilization and thereby throughput of the network. This paper describes several algorithms for generating an optimal schedule in terms of channel utilization for multiple access by utilizing range information in a fully connected network. We also provide detailed analysis for the proposed algorithms performance in terms of their complexity, convergence, and effect of non-idealities in the network. The performance of the proposed schemes are compared with non-aided methods to quantify the benefits of using the range information in the communication. The proposed methods have several favorable properties for the scalable systems. We show that the proposed techniques yields better channel utilization and throughput as the number of nodes in the network increases. We provide simulation results in support of this claim. The proposed methods indicate that the throughput can be increased on average by 3-10 times for typical network configurations.

Clock synchronization is ubiquitous in wireless systems for communication, sensing, and control. In this paper, we design a scalable system in which an indefinite number of passively receiving wireless units can synchronize to a single master clock at the level of discrete clock ticks. Accurate synchronization requires an estimate of the node positions to compensate the time-of-flight transmission delay in line-of-sight environments. If such information is available, the framework developed here takes position uncertainties into account. In the absence of such information, as in indoor scenarios, we propose an auxiliary localization mechanism. Furthermore, we derive the Cramer-Rao bounds for the system, which show that it enables synchronization accuracy at sub-nanosecond levels. Finally, we develop and evaluate an online estimation method, which is statistically efficient.

In this paper, we propose fusion of dynamic time of arrival (TOA) from multiple low complexity detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We show that by having a multi-channel sub-Nyquist receiver with each channel having an energy detector can match the performance of a single channel digital receiver with matched filter. We derive analytical expression for number of sub-Nyquist energy detector channels needed to achieve the performance of digital implementation with matched filter and 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 proposed strategy using IEEE 802.15.4a CM1 multipath channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter.

Optimal detection of ultra wideband (UWB) pulses in a UWB transceiver employing multiple detector types is proposed and analyzed in this paper. To enable the transceiver to be used for multiple applications, the designers have different types of detectors such as energy detector, amplitude detector, etc., built in to a single transceiver architecture. We propose several fusion techniques for fusing decisions made by individual IR-UWB detectors. In order to get early insight into theoretical achievable performance of these fusion techniques, we assess the performance of these fusion techniques for commonly used detector types like matched filter, energy detector and amplitude detector under Gaussian assumption. These are valid for ultra short distance communication and in UWB systems operating in millimeter wave (mmwave) band with high directivity gain. In this paper, we utilize the performance equations of different detectors, to device distinct fusion algorithms. We show that the performance can be improved approximately by 4 dB in terms of signal to noise ratio (SNR) for high probability of detection of a UWB signal (> 95%), by fusing decisions from multiple detector types compared to a standalone energy detector, in a practical scenario.

We investigate the range estimate between two wireless nodes without time stamps exchanging. Considering practical aspects of oscillator clocks, we propose a new model for ranging in which the measurement errors include the sum of two distributions, namely, uniform and Gaussian. We then derive an approximate maximum likelihood estimator (AMLE), which poses a difficult global optimization problem. To avoid the difficulty in solving the complex AMLE, we propose a simple estimator based on the method of moments. Numerical results show a promising performance for the proposed technique.

The estimation of the decay rate of a signal section is an integral component of both blind and non-blind reverberation time estimation methods. Several decay rate estimators have previously been proposed, based on, e.g., linear regression and maximum-likelihood estimation. Unfortunately, most approaches are sensitive to background noise, and/or are fairly demanding in terms of computational complexity. This paper presents a low complexity decay rate estimator, robust to stationary noise, for reverberation time estimation. Simulations using artificial signals, and experiments with speech in ventilation noise, demonstrate the performance and noise robustness of the proposed method.

In this paper, we develop a new technique for estimating fine clock errors and range between two nodes simultaneously by two-way time-of-arrival measurements using impulse-radio ultrawideband signals. Estimators for clock parameters and the range are proposed, which are robust with respect to outliers. They are analyzed numerically and by means of experimental measurement campaigns. The technique and derived estimators achieve accuracies below 1 Hz for frequency estimation, below 1 ns for phase estimation, and 20 cm for range estimation, at a 4-m distance using 100-MHz clocks at both nodes. Therefore, we show that the proposed joint approach is practical and can simultaneously provide clock synchronization and positioning in an experimental system.

This paper proposes a novel approach to phase-noise compensation. The basic idea is to approximate the phase-noise statistics by a finite number of realizations, i.e., a phase-noise codebook. The receiver then uses an augmented received signal model, where the codebook index is estimated along with other parameters. The realization of the basic idea depends on the details of the air interface, the phase-noise statistics, the propagation scenario and the computational constraints. In this paper, we will focus on a MQAM-OFDM system with pilot sub-carriers within each OFDM symbol. The channel is frequency selective, fading and unknown. A decision-feedback method is employed to further enhance performance of the system. Simulation results are shown for uncoded and coded systems to illustrate the performance of the algorithm, which is also compared with previously employed methods. Our simulations show that for a 16-QAM coded OFDM system over a frequency selective Rayleigh fading channel affected by phase noise with root-mean-square (RMS) of 14.4 degrees per OFDM symbol, the proposed algorithm is 1.5 dB from the ideal phase-noise free case at a BER of 10-4. The performance of the best reference scheme is 2.5 dB from the ideal case at BER of 10-4. The proposed scheme is also computationally attractive.

We propose a framework for the detection of dangerous vehicle cornering events, based on statistics related to the no-sliding and no-rollover conditions. The input variables are estimated using an unscented Kalman filter applied to global navigation satellite system (GNSS) measurements of position, speed, and bearing. The resulting test statistic is evaluated in a field study where three smartphones are used as measurement probes. A general framework for performance evaluation and estimator calibration is presented as depending on a generic loss function. Furthermore, we introduce loss functions designed for applications aiming to either minimize the number of missed detections and false alarms, or to estimate the risk level in each cornering event. Finally, the performance characteristics of the estimator are presented as depending on the detection threshold, as well as on design parameters describing the driving behavior. Since the estimation only uses GNSS measurements, the framework is particularly well suited for smartphone-based insurance telematics applications, aiming to avoid the logistic and monetary costs associated with, e.g., on-board-diagnostics or black-box dependent solutions. The design of the estimation algorithm allows for instant feedback to be given to the driver and, hence, supports the inclusion of real-time value-added services in usage-based insurance programs.

Despite being around for almost two decades, foot-mounted inertial navigation only has gotten a limited spread. Contributing factors to this are lack of suitable hardware platforms and difficult system integration. As a solution to this, we present an open-source wireless foot-mounted inertial navigation module with an intuitive and significantly simplified dead reckoning interface. The interface is motivated from statistical properties of the underlying aided inertial navigation and argued to give negligible information loss. The module consists of both a hardware platform and embedded software. Details of the platform and the software are described, and a summarizing description of how to reproduce the module are given. System integration of the module is outlined and finally, we provide a basic performance assessment of the module. In summary, the module provides a modularization of the foot-mounted inertial navigation and makes the technology significantly easier to use.

Several approaches for blind estimation of reverberation time have been presented in the literature and decay rate estimation is an integral part of many, if not all, of such approaches. This paper provides both an analytical and experimental comparison, in terms of the bias and variance of three common decay rate estimators; a straight-forward linear regression approach as well as two maximum-likelihood based methods. Situations with and without interfering additive noise are considered. It is shown that the linear regression based approach is unbiased if no smoothing is applied, and that the estimation variance in the absence of noise is constantly about twice that of the maximum-likelihood based methods. It is shown that the methods that do not take possible noise into account suffer from similar estimation bias in the presence of noise. Further, a hybrid method, combining the noise robustness and low computational complexity advantages of the two different maximum-likelihood based methods, is presented.

The relation between voice radio communication and pedestrian localization is studied. 3D audio is identified as a linking technology which brings strong mutual benefits. Voice communication rendered with 3D audio provides a potential low secondary task interference user interface to the localization information. Vice versa, location information in the 3D audio provides spatial cues in the voice communication, improving speech intelligibility. An experimental setup with voice radio communication, cooperative pedestrian localization, and 3D audio is presented and we discuss high level tactical possibilities that the 3D audio brings. Finally, results of an initial experiment, demonstrating the effectiveness of the setup, are presented.

The initialization of the state estimation in a localization scenario based on ranging and dead reckoning is studied. Specifically, we treat a cooperative localization setup and consider the problem of recursively arriving at a unimodal state estimate with sufficiently low covariance such that covariance based filters can be used to estimate an agent's state subsequently. The initialization of the position of an anchor node will be a special case of this. A number of simplifications/assumptions are made such that the estimation problem can be seen as that of estimating the initial agent state given a deterministic surrounding and dead reckoning. This problem is solved by means of a particle filter and it is described how continual states and covariance estimates are derived from the solution. Finally, simulations are used to illustrate the characteristics of the method and experimental data are briefly presented.

We investigate a wireless network localization scenario in which the need for synchronized nodes is avoided. It consists of a set of fixed anchor nodes transmitting according to a given sequence and a self-localizing receiver node. The setup can accommodate additional nodes with unknown positions participating in the sequence. We propose a localization method which is robust with respect to uncertainty of the anchor positions and other system parameters. Further, we investigate the Cramér-Rao bound for the considered problem and show through numerical simulations that the proposed method attains the bound.

We consider the problem of estimating the K-factor of a Rician fading wireless channel based on observations of the envelope only, i.e., without phase-information. An approximate Rician power density function (pdf) is introduced to overcome the complexity of the exact pdf. A closed-form maximum-likelihood estimator is derived based on this approximate Rician pdf, while a maximum-likelihood estimator based on the exact Rician pdf appears to be infeasible. An improved estimator is also proposed that features less bias and less variance than the first estimator. The performance of the latter estimator is compared to moment-based estimators that were previously proposed in the literature and is found to have superior performance to closed-form moment estimators, especially for low sample numbers and/or large K-values. The estimator is applied to real wireless macro-cell urban-area measurements. The results show generally low K-factors (below 3dB), with occasional higher values in particular circumstances.

Due to the recursive and integrative nature of zero-velocity-update-aided (ZUPT-aided) inertial navigation systems (INSs), the error covariance increases throughout each ZUPT-less period followed by a drastic decrease and large state estimate corrections as soon as ZUPTs are applied. For dead-reckoning with foot-mounted inertial sensors, this gives undesirable discontinuities in the estimated trajectory at the end of each step. However, for many applications, some degree of lag can be tolerated and the information provided by the ZUPTs at the end of a step can be made available throughout the step, eliminating the discontinuities. For this purpose, we propose a smoothing algorithm for ZUPT-aided INSs. For near real-time applications, smoothing is applied to the data in a step-wise manner requiring a suggested varying-lag segmentation rule. For complete off-line processing, full data set smoothing is examined. Finally, the consequences and impact of smoothing are analyzed and quantified based on real-data.

A method is proposed to fuse the information from two navigation systems whose relative position is unknown, but where there exists an upper limit on how far apart the two systems can be. The proposed information fusion method is applied to a scenario in which a pedestrian is equipped with two foot-mounted zero-velocity-aided inertial navigation systems; one system on each foot. The performance of the method is studied using experimental data. The results show that the method has the capability to significantly improve the navigation performance when compared to using two uncoupled foot-mounted systems.

Despite the tremendous research effort spent on MIMO systems - very few papers address the impact of hardware imperfections. In this paper we analyze the distortion noise of the transmitter branches of a MIMO transmitter. In particular we analyze the cross-correlation of the distortion noises for the case that the transmitted signals are correlated. This case arises when there is just a single stream being transmitted with beamforming or in the case of multiple streams and linear precoding. We analyze the problem in two ways: using analytical derivations and using measurements on our testbed. In the analytical case we assume a transmitter impaired by a 3rd order non-linearity. For this scenario we find that the absolute correlation coefficient of the distortion noises is given by the absolute value of the correlation coefficients of the input signals to the power of three, while the phase is the same as that of the input signal. Our measurements also show good agreement between measurements and analytical results. It seems to suggest that the distortion noises can be regarded as practically independent whenever there are two or more modulation streams being transmitted (spatial multiplexing). In a single-stream scenario, the distortion noise will tend to have the same spatial distribution as the desired signal.

We consider the problem of estimating a random state vector when there is information about the maximum distances between its subvectors. The estimation problem is posed in a Bayesian framework in which the minimum mean square error (MMSE) estimate of the state is given by the conditional mean. Since finding the conditional mean requires multidimensional integration, an approximate MMSE estimator is proposed. The performance of the proposed estimator is evaluated in a positioning problem. Finally, the application of the estimator in inequality constrained recursive filtering is illustrated by applying the estimator to a dead-reckoning problem. The MSE of the estimator is compared with two related posterior Cramér-Rao bounds.

In a motorized vehicle a number of easily measurable signals with frequency components related to the rotational speed of the engine can be found, e.g., vibrations, electrical system voltage level, and ambient sound. These signals could potentially be used to estimate the speed and related states of the vehicle. Unfortunately, such estimates would typically require the relations (scale factors) between the frequency components and the speed for different gears to be known. Consequently, in this article we look at the problem of estimating these gear scale factors from training data consisting only of speed measurements and measurements of the signal in question. The estimation problem is formulated as a maximum likelihood estimation problem and heuristics is used to find initial values for a numerical evaluation of the estimator. Finally, a measurement campaign is conducted and the functionality of the estimation method is verified on real data.

This paper proposes a power estimation methodology which presents low complexity when implemented in hardware. Power estimation is done in digital and the radio signals are digitized using a 1-bit quantizer. An algorithm to estimate power is proposed. Power estimation of the signal is done while varying the threshold of the 1-bit quantizer. It is also shown that the proposed architecture can be used to estimate power of wideband radio channels.

The effects of data time synchronization errors in a loosely coupled Global-Positioning-System (GPS)-aided inertial navigation system (INS) are studied and quantified in terms of the increased mean square error (MSE) of the navigation solution. An expression for evaluating the MSE of the navigation solution, given the vehicle trajectory and the model of the INS error dynamics, is derived. Thereafter, a software-based time synchronization method, where the time synchronization error is included as a state to be estimated by the data integration filter, is proposed. A practical approach to the implementation of the proposed time synchronization method is also briefly described. Moreover, an expression for the MSE of the navigation solution in the system that employs the proposed synchronization method is derived. Finally, through simulations and tests with real-world data, the correctness of the derived MSE expressions is validated, and the application of the proposed synchronization method is shown. The test results show that, with the proposed synchronization approach, a data time synchronization, which is accurate to the order of a few milliseconds, can be achieved.

Foot-mounted zero-velocity-update (ZUPT) aided inertial navigation system (INS) is a conceptually well known with publications in the area typically focusing on improved methods for filtering and addition of sensors and heuristics. Despite this, the performance characteristics, which would ultimately justify and give guidelines for such system modifications of ZUPT-aided INSs and other related systems, are in some aspects poorly documented. Unfortunately, the systems are non-linear, meaning that the performance is dependent on the system set-up, parameter setting, and the true trajectory. This complicates the process of evaluating performance and partially explains the few publications with detailed performance characterisation results. Therefore in this article we suggest and motivate methodologies for evaluating performance of ZUPT-aided INS and other related systems, we apply them to a suggested baseline set-up of the system, and study some aspects of the performance characteristics.

Current advances in localization and tracking technology have the potential to develop into much-needed tools for the saving of lives in emergency response and rescue missions, and for the safe-keeping of lives in military operations. However, civilian and military users face different environments and consequently have different user requirements. Even within the two broad fields of civilian and military applications, different types of personnel and indeed different types of missions face different needs and constraints. For instance, firefighters engaged in the suppression of a forest fire have other requirements than firefighters suppressing a fire in a high-rise building, or a conflagration in a large industrial complex full of hazardous materials. Military personnel engaged in counter-insurgency operations face requirements different from those tasked to rescue hostages held in a closed environment. This paper aims to survey the different requirements for localization and tracking technology by mission type, so that users can more easily determine their own specific technology needs. Although primarily aimed to describe requirements for military personnel, law enforcement officers, and firefighters, needs and constraints for several types of civilian applications are covered as well. Despite differences in requirements, it makes sense to develop technologies that will target several of these end-user groups. A new joint facility for development of requirements together with representatives from the various branches, and the evaluation of existing and emerging localization and tracking systems, would assist in the enabling of reliable and user-friendly capability to respond to, and recover from, all-hazards emergencies and combat operations.

A study of the performance of four zero-velocity detectors for a foot-mounted inertial sensor based pedestrian navigation system is presented. The four detectors are the acceleration moving variance detector, the acceleration magnitude detector, the angular rate energy detector, and a novel generalized likelihood ratio test detector, refereed to as the SHOE. The performance of each detector is assessed by the accuracy of the position solution provided by the navigation system employing the detector to perform zero-velocity updates. The results show that for leveled ground forward gait at a speed of 5 km/h, the angular rate energy detector and the SHOE give the highest performance, with a position accuracy of 0.14% of the travelled distance. The results also indicate that during leveled ground forward gait, the gyroscope signals hold the most reliable information for zero-velocity detection.

In-car positioning and navigation has been a killer application for Global Positioning System (GPS) receivers, and a variety of electronics for consumers and professionals have been launched on a large scale. Positioning technologies based on stand-alone GPS receivers are vulnerable and, thus, have to be supported by additional information sources to obtain the desired accuracy, integrity, availability, and continuity of service. A survey of the information sources and information fusion technologies used in current in-car navigation systems is presented. The pros and cons of the four commonly used information sources, namely, 1) receivers for radio-based positioning using satellites, 2) vehicle motion sensors, 3) vehicle models, and 4) digital map information, are described. Common filters to combine the information from the various sources are discussed. The expansion of the number of satellites and the number of satellite systems, with their usage of available radio spectrum, is an enabler for further development, in combination with the rapid development of microelectromechanical inertial sensors and refined digital maps.

The effect of sampling and quantization on frequency estimation for a single sinusoid is investigated. Asymptotic Cramer-Rao bounds (CRB) for 1-bit quantization and for non-ideal filters are derived, which are simpler to calculate than the exact CRB while still relatively accurate. It is further investigated how many bits should be used in quantization to avoid the problems of 1-bit quantization, and it turns out that 3-4 bits are enough. Finally, oversampled 1-bit quantization is investigated. It is determined how much the signal should be oversampled, and in addition /spl Sigma//spl Delta/ modulators are investigated.

The effect of sampling and quantization on frequency estimation for a single sinusoid is investigated. The Cramer-Rao bound for 1 bit quantization is derived, and compared with the limit of infinite quantization. It is found that 1 bit quantization gives a slightly worse performance, however, with a dramatic increase of variance at certain frequencies. This can be avoided by using 4 times oversampling. The effect of sampling when using non-ideal antialiasing lowpass filters is therefore investigated. Cramer-Rao lower bounds are derived, and the optimal filters and sampling frequencies are found. Finally, fast estimators for 1 bit sampling, in particular correlation based estimators, are derived. The paper concludes with simulation results for 4 times oversampled 1 bit quantization.

A mathematical treatment of particle size and velocity estimation from two channel laser anemometry measurements is considered. Cramer-Rao bounds for the general case are derived, and the corresponding maximum likelihood estimator is analyzed through computer simulations. Low complexity correlation based estimators are derived and their performance is characterized. The results predicted by theory are illustrated by some numerical examples.

This paper presents a model-based approach for noise suppression of speech contaminated by additive noise. A Kalman filter based speech enhancement system is presented and its performance is investigated in detail. It is shown that with a novel speech parameter estimation algorithm, it is possible to achieve 10 dB noise suppression with a high total audible quality.

Recursive estimation of parameters of linearly or harmonically frequency modulated sinusoidal signals is considered. The algorithms simultaneously separate the measured signal to individual components and update signal parameters using estimated phase differences. The main advantages of the proposed algorithms over standard tracking schemes such as e.g. the adaptive notch filter is zero asymptotic bias (zero tracking delay) in estimating of the instantaneous frequencies.

It is well-known that sine waves produce harmonic overtones when passed through static nonlinear functions. This paper describes a new algorithm where an arbitrary periodic signal is estimated recursively. The estimated signal model is typically parameterised as a real sine wave with unknown frequency in cascade with a piecewise linear function. A recursive Gauss-Newton prediction error identification algorithm for joint estimation of the driving frequency and the parameters of the nonlinear output function can then be derived. The approach handles colored measurement disturbances and gives a direct measure of the size of the nonlinearity that corresponds to the harmonic spectrum. The Cramer-Rao bound (CRB) is also calculated in the paper.

A notch filter based on discounted recursive least squares sinusoidal modeling is presented. This gives analytical insight both into least squares modeling of sine waves in noise and the use of constrained notch filters. Especially, it is shown that the discounted least squares solution converges to a commonly used notch filter with constrained poles and zeros, where the forgetting factor in the criterion corresponds to the pole radii in the filter. Also a Kalman filter interpretation is given for the derived constrained notch filter.>

The asymptotic tracking properties of an adaptive notch filter with pole-zeros constraints for the cancellation or retrieval of multiple time-varying sine waves in additive noise are analyzed. The methods of Ljung and Gunnarsson (see Automatica, vol.26, no.1, p.7-21, Jan. 1990) are used in order to analyze the asymptotic mean-square parameter error, when the variations in the underlying frequencies and the gain of the algorithm are assumed to be sufficiently small. A closed-form expression for the mean square error (MSE) is derived as a function of the tuning variables of the algorithm. The results give insight into the operational properties of the algorithm and are used in order to minimize the MSE with respect to the tuning variables.>