Motivated by the constant modulus property of frequency shift keying (FSK) based waveforms and the stabilisation of its radar performance with an increase in the number of subpulses, in this paper an FSK-based dynamic subpulse number joint communications and radar waveform design is proposed. From a communications point of view, the system operates based on traditional FSK modulation. From a sensing point of view, although the subpulses are continuously generated and transmitted, radar waveforms are dynamically formed by monitoring the flatness of the spectrum which in return guarantees the accuracy of the delay estimation. Other constraints on the waveform length are used to ensure satisfactory values of the root mean square time duration, ambiguity function sidelobe levels and prevent overly long waveforms. To provide an estimation of the probability of generating extremely long waveforms, the distribution of the number of subpulses is approximated using a Brownian motion process and an existing result on its one-sided exit density. Numerical examples are provided to evaluate the accuracy of the approximate distribution, as well as the ambiguity function sidelobe levels and the delay and Doppler shift estimation performance of the transmitted waveforms.

Recent developments in Multiple-Input-Multiple-Output (MIMO) technology include packing a large number of antenna elements in a compact array to access the bandwidth benefits provided by higher mutual coupling (MC). The resulting super-wideband (SW) systems require a circuit-theoretic framework to handle the MC and channel models which span extremely large bands. Hence, in this paper, we make two key contributions. First, we develop a physically-consistent Rician channel model for use with SW systems. Secondly, we express the circuit-theoretic models in terms of a standard MIMO model, so that insights into the effects of antenna layouts, MC, and bandwidth can be made using standard communication theory. For example, we show the bandwidth widening resulting from the new channel model. In addition, we show that MC distorts line-of-sight paths which has beamforming implications. We also highlight the interaction between spatial correlation and MC and show that tight coupling reduces spatial correlations at low frequencies.

In this paper, novel robust principal component analysis (RPCA) methods are proposed to exploit the local structure of datasets. The proposed methods are derived by minimizing the α -divergence between the sample distribution and the Gaussian density model. The α- divergence is used in different frameworks to represent variants of RPCA approaches including orthogonal, non-orthogonal, and sparse methods. We show that the classical PCA is a special case of our proposed methods where the α- divergence is reduced to the Kullback-Leibler (KL) divergence. It is shown in simulations that the proposed approaches recover the underlying principal components (PCs) by down-weighting the importance of structured and unstructured outliers. Furthermore, using simulated data, it is shown that the proposed methods can be applied to fMRI signal recovery and Foreground-Background (FB) separation in video analysis. Results on real world problems of FB separation as well as image reconstruction are also provided.

This paper presents a new joint radar and communication technique based on the classical stepped frequency radar waveform. The randomization in the waveform, which is achieved by using permutations of the sequence of frequency tones, is utilized for data transmission. A new signaling scheme is proposed in which the mapping between incoming data and waveforms is performed based on an efficient combinatorial transform called the Lehmer code. Considering the optimum maximum likelihood detection, the union bound and the nearest neighbour approximation on the communication block error probability is derived for communication in an additive white Gaussian noise channel. The results are further extended to incorporate the Rician fading channel model, of which the Rayleigh fading channel is presented as a special case. Furthermore, an efficient communication receiver implementation is discussed based on the Hungarian algorithm which achieves optimum performance with much less operational complexity when compared to an exhaustive search. From the radar perspective, two key analytical tools, namely, the ambiguity function and the Fisher information matrix are derived. Furthermore, accurate approximations to the Cramer-Rao lower bounds on the delay and Doppler estimation errors are derived based on which the range and velocity estimation accuracy of the waveform is analysed.

This paper presents a new waveform that is suitable for simultaneous data transmission and radar sensing. The approach considers a classical random stepped frequency radar waveform that is suitable for the emerging automotive radar application. The randomization in the waveform, which is achieved by using permutations of the sequence of frequency tones, is utilized for data transmission. More specifically, we propose a new Lehmer code based signaling model that modulates data based on the selection of the permutation. Considering maximum likelihood detection, the union bound on the communication block error probability is derived for baseband communication both in an additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. Using the Hungarian Algorithm, an efficient implementation method for the communications receiver is also presented. From the radar perspective, we derive the ambiguity function, which is a key analytical tool in radar waveform design, and characterize the behavior of the Lehmer code based random stepped frequency radar waveform. Numerical examples are used to illustrate the performance of the proposed waveform.

Robust variants of Wald, Rao and likelihood ratio (LR) tests for the detection of a signal subspace in a signal interference subspace corrupted by contaminated Gaussian noise are proposed in this paper. They are derived using the α- divergence, and the trade-off between the robustness and the power (the probability of detection) of the tests is adjustable using a single hyperparameter α . It is shown that when α→ 1 , these tests are equivalent to their well known classical counterparts. For example the robust LR test coincides with the LR test or the matched subspace detector (MSD). Asymptotic results are provided to support the proposed tests and robustness to outliers is obtained using values of . Numerical experiments illustrating the performance of these tests on simulated, real functional magnetic resonance imaging (fMRI), hyperspectral and synthetic aperture radar (SAR) data are also presented.

The problem of detecting a subspace signal in colored Gaussian noise with unknown covariance matrix is investigated when the training data may contain samples with target signal. The target signal is assumed that it lies in a subspace spanned by columns of a known matrix. To develop the test, an ad hoc approach, similar to the classical adaptive matched filter (AMF) is used where instead of the maximum likelihood (ML) estimator of the covariance, the minimum α-divergence based estimator is substituted in the likelihood ratio. This test just depends on the single parameter α and as a special case can be turned to the AMF. For a range of α, the proposed test has the benefits of being robust to outliers and the existence of other targets in the training data. Numerical examples illustrating that the proposed detector can achieve better detection rates in such a scenario while providing almost the same performance in a target free scenario are presented.

The problem of detecting a subspace signal in the presence of subspace interference and contaminated Gaussian noise with unknown variance is investigated. The target signal is assumed to lie in a subspace spanned by the columns of a known matrix. To develop the test, the same steps used in the generalized likelihood ratio test (GLRT) are used where instead of the maximum likelihood (ML) estimator of the parameters, the minimum α-divergence based estimator is substituted in the test to increase the robustness of the test against contaminations in noise. This test depends on the single parameter α and as the special case corresponds to the well known GLRT. Numerical examples illustrating that the proposed test can achieve better detection rates in such scenarios are presented. Moreover, the test is applied to real fMRI dataset to detect the active area of the brain for some task-related inputs.

This paper studies the complexity of solving two classes of non-cooperative games in a distributed manner, in which the players communicate with a set of system nodes over noisy communication channels. The complexity of solving each game class is defined as the minimum number of iterations required to find a Nash equilibrium (NE) of any game in that class with ∈ accuracy. First, we consider the class G of all N-player non-cooperative games with a continuous action space that admit at least one NE. Using information-theoretic inequalities, a lower bound on the complexity of solving G is derived which depends on the Kolmogorov 2∈-capacity of the constraint set and the total capacity of the communication channels. Our results indicate that the game class G can be solved at most exponentially fast. We next consider the class of all N-player non-cooperative games with at least one NE such that the players' utility functions satisfy a certain (differential) constraint. We derive lower bounds on the complexity of solving this game class under both Gaussian and non-Gaussian noise models. Finally, we derive upper and lower bounds on the sample complexity of a class of quadratic games. It is shown that the complexity of solving this game class scales according to Θ (1/∈2) where € is the accuracy parameter.

Adaptive subspace detectors (ASD) generalize matched subspace detectors (MSD) by accounting for possible correlation. Both ASD and MSD are derived using the generalized likelihood ratio test (GLRT). While MSD assumes there is no correlation between observations, ASD estimates a sample covariance matrix of possibly correlated samples using signal-free observations. In this paper, we address the performance of the ASD when the number of secondary data is insufficient and the observed signal lies in higher dimensional space. Such high dimensional spaces are frequently encountered in functional magnetic resonance imaging (fMRI) data for the analysis of brain activation detection. We propose a methodology that works based on the latent variables in a lower dimensional space. A low-rank decomposition of the sample covariance matrix is derived based on the singular value decomposition (SVD) and an adaptive basis selection method is used to decide which eigen-vectors are useful in data projection. Performing detection in the lower dimensional subspace has the benefit of reducing the number of parameters which need to be estimated. Simulation results show superiority of our proposed adaptive reduced subspace detector (ARSD) over conventional ASD in term of probability of detection.

The region of interest (ROI) of medical images in content-based image retrieval (CBIR) systems often require content authentication and verification. This is because adversarial modification of the stored image could have lethal effect on research, diagnostic outcome and the outcome of some forensic investigations. In this work, both robust watermarking and Fragile Steganography were combined with image search features to design a medical image retrieval system that incorporates ROI integrity verification. Original ROI features were pre-computed and embedded into archival images and utilised during retrieval for image integrity checks. The average global image PSNR was 38.36dB while the ROI PSNR was maintained at an average of 46dB with all watermark search features retrieved at zero bit error rate (BER) provided the attack on the image is not perceptible.

As the use of automotive radar increases, performance limitations associated with radar-to-radar interference will become more significant. In this paper, we employ tools from stochastic geometry to characterize the statistics of radar interference. Specifically, using two different models for the spatial distributions of vehicles, namely, a Poisson point process and a Bernoulli lattice process, we calculate for each case the interference statistics and obtain analytical expressions for the probability of successful range estimation. This paper shows that the regularity of the geometrical model appears to have limited effect on the interference statistics, and so it is possible to obtain tractable tight bounds for the worst case performance. A technique is proposed for designing the duty cycle for the random spectrum access, which optimizes the total performance. This analytical framework is verified using Monte Carlo simulations.

Doppler radars are low cost and light weight sensors that have a potential to find wide applications in building a large team of mobile vehicle platforms. Because of the nonlinearity associated with the measurement from Doppler radars, it is both interesting and challenging to extract meaningful information from the low cost sensors. Building upon the authors' previous work on self localization with a feature-based map with known landmark associations using Doppler radars and an Extended Kalman Filter (EKF), this paper investigates the effects of positioning and the number of landmarks in a feature-based map on the accuracy of the position estimation of a robot. The computations of Cramer-Rao Lower Bound (CRLB) at the terminating sample show that the CRLB has a drastic reduction when the number of landmarks is increased from 1 to 2 while the root mean square errors (RMSE) of EKF indicate a gradual error reduction for the first 4 landmarks. The results presented in this paper will provide an essential guideline on the experiment design for feature-based robot self-localization.

Stepped frequency radar technology, where the transmit waveform consists of a sequence of tones, has long been suggested for cost-effective and high-resolution applications. One recent use of this technology is in automotive application where, in addition to cost-effectiveness, a random stepped frequency (RSF) waveform can significantly reduce the interference between vehicles. In this paper we provide a generic framework for the range and Doppler measurements for multiple targets. We further suggest two possible methods for reducing the computational complexity of RSF waveforms processing, which is important for future automotive applications.

In distributed tracking, communication bandwidth is one of the most expensive resources when we require to send measurements to other locations for processing. Furthermore, bandwidth requirement increases when tracking in clutter is considered due to transmission of target as well as clutter measurements. This paper describes the tracking in clutter under bandwidth constraints. The main idea is that instead of sending all target and clutter measurements we combine them into a weighted sum and transmit the resultant measurement. A novel Bayesian filter is proposed utilising the received measurement information. We observe some loss of performance as compared to local tracker, however it is considerably small.

Volterra analysis of the ultra high-speed CMOS switched source follower (SSF) track and hold amplifier (THA) is presented based on a simplified RF model. Approximate expressions for the transfer function, the harmonic equations and third-order intermodulation distortion equation are derived respectively. Simulations based on a commercial available 65 nm CMOS process technology are performed with the SpectreRF circuit simulator. It is shown that when operating with ultra high input frequency, the total harmonic distortion (THD) linearity degrades progressively. Comparative SpectreRF simulated results for SSF THA have shown good agreement with Volterra analytical results by using the simplified BSIM4 RF model.

In this paper a model to calculate the capacity of millimeter wave integrated systems is proposed. The model includes Angle-of-Arrival information and is used to predict the static channel capacity. It is found that for millimeter wavelength systems, the LOS signal contributes significantly to the spatial MIMO channel capacity for short distance applications. Measurements of the spatial MIMO wireless channel in an indoor environment have been performed in the 55-57 GHz band and have been used to calculate the channel capacity.

The arctan function is a well-known Maximum Likelihood (ML) estimator of the phase angle α ∈ [-π,π) of a complex signal in additive white Gaussian noise. In this paper we revisit the arctan-based ML phase estimator and identify the bias problem for phase tracking. We show that the posteriori probability density function of α becomes a bi-modal distribution for small values of signal to noise ratio ρ and larger values of α. In such cases the mean and the mode differ from each other, and as a result when such ML phase estimates are used as an input to a linear system (LS), example for phase tracking, the resulting output (which is essentially the mean value of the phase) differs from its true value which is the mode. In such situations there exist a mean (tracking) error at the output of the LS from its true value α, and in (non-Bayesian) statistical terms there exist a bias in the estimates. In this paper, we provide some statistical analysis to explain the above problem, and also provide solutions for bias correction when a LS is used for tracking phase. Furthermore, we also provide two nonlinear phase tracking systems, 1) a Monte-Carlo based sequential phase tracking technique and 2) a second-order digital-phase locked loop based method, for bias-free phase tracking which eliminate the bias problem that occurs in the case of linear phase tracking with ML estimates.

An algorithm for trajectory optimization of autonomous aerial vehicles performing multiple target tracking is proposed. The problem is approached by formulating it as a partially observed Markov decision process (POMDP) and developing a moving-horizon solution taking into account short and long term costs. To evaluate the effectiveness of the approach a simulation involving multiple UAVs and targets is performed.

We investigate the problem of transmitting track state to a fusion center using minimum computational resources. Local track state is not updated and not transmitted, unless local measurements sufficiently decrease local track state uncertainty. Track state information is coded using equivalent innovations. To prevent the accumulation - random walk of coding/decoding errors (coding noise), with potentially unbounded errors, equivalent innovations incorporate the coding noise. Simulations demonstrate negligible information loss when using the equivalent innovations, compared to the optimal central fusion using unlimited bandwidth.

The emerging area of control with limited data rates incorporates ideas from both control and information theory. The data rate constraint introduces quantization into the feedback loop and gives the interconnected system a two-fold nature, continuous and symbolic. In this paper, we review the results available in the literature on data-rate-limited control. For linear systems, we show how fundamental tradeoffs between the data rate and control goals, such as stability, mean entry times, and asymptotic state norms, emerge naturally. While many classical tools from both control and information theory can still be used in this context, it turns out that the deepest results necessitate a novel, integrated view of both disciplines.

In this paper target tracking using measurements whose probability density function can be described (approximated) by a Gaussian Sum Mixture is described. The approach is illustrated for two classes of these measurements. One class is the measurements obtained by acoustic amplitude / time difference of arrival measurements typically obtained using motes. The other is measurements obtained by radar with large angular measurement errors. Such measurements in a Cartesian system cannot be accurately modelled by a single Gaussian pdf. By following a Bayessian approach and the target existence paradigm, two target tracking filters have been derived. A variant of IPDA - the single scan target tracking filter, and a variant of the ITS - the multi scan target tracking filter, have been derived for situations when Gaussian sum measurements are available.

In this paper we measure and characterize 55-65 GHz wireless channels for a typical desktop environment. An angle of arrival (AoA) modified Saleh-Valenzuela (S-V) model is used for the desktop environment. Key AoA modified S-V model parameters such as cluster decay factor, ray decay factor, cluster arrival rate, ray arrival rate and AoA ray angular standard deviation are extracted from the measured data

We study a space-time precoding technique for MISO wireless systems by employing a linear prefilter at each transmit antenna. The channel is Rician fading, where the mean and variance of the propagation paths are known to the transmitter. This model includes the Rayleigh fading channels as special cases. We use channel capacity as the optimizing criterion for the prefilter design. This criterion provides a unified design of the prefilters for both Rician and Rayleigh fading channels. The optimum prefilters are functions of the channel mean and variance. The solution ranges from beamforming for Rician channels with high K-factor, to unitary diversity for Rayleigh fading cases, where delay diversity is an example. The MMSE equalizer is then used to detect the signal at the receiver. Analysis of bounds on error rate performance and numerical simulations for 4QAM input signals show significant diversity gains and array gains. The result also illustrates that having partial channel knowledge at the transmitter can strongly enhance the system performance.

Multiple input and multiple output (MIMO) is a new technology that promises to significantly increase the capacity of wireless communication systems. MIMO technology increases capacity by exploiting the multipath channel. Unfortunately results presented in the literature (Gesbert et al., Smith et al.) neglect antenna effects. This paper investigates the effect that parameters such as number, spacing, coupling and far field antenna patterns of antennas have on the capacity of MIMO systems in Rayleigh channels.

This letter develops a sequential composite hypotheses test for detecting failure of a decision-directed recursive least square (RLS) adaptive multiuser detector (MUD) and convergence of a blind RLS (BLRS) MUD when used in the downlink of direct-sequence/code division multiple access (DS/CDMA) systems. Simulations are provided to demonstrate the effectiveness of the test and to characterize the SIR improvement achievable in a dynamic environment where the number of interferers is time-varying.

This letter presents a new method for identification of fast-fading mobile channels (for which combinations of vehicle speed and carrier frequency give rise to significant fading). Our new algorithm estimates both the channel statistics and the time-varying channel impulse response on-line. Simulation studies demonstrate the performance of the new estimator which couples an augmented Kalman filter with a recursive least squares algorithm.

We present a new method for on-line identification of time-varying FIR channels. Two conditionally coupled estimators are proposed. In both cases an augmented-state adaptive Kalman filter is employed for tracking the time-varying channel and estimating the mean channel response. Coupled to the Kalman filter is an algorithm for estimating the parameters of the underlying auto-regressive (AR) model which describes the time evolution of the channel. For the first coupled estimator, we propose a new recursive least squares algorithm for estimation of these AR parameters directly from the channel observations. An alternative algorithm based on estimation of the channel covariance is used in the second coupled estimator. A simulation example demonstrates the performance of the proposed estimators.

Investigates an adaptive waveform selection scheme where selection is based on overall tracking performance of the active sonar system. The assumption of an optimal receiver allows the inclusion of transmitted waveform specification parameters in the tracking sub-system equations, leading to a waveform selection scheme where the next transmitted waveform parameters are selected so as to minimise the total mean-square tracking error at the next time step. A simulation example is presented to illustrate the potential of the waveform selection scheme for the optimal utilisation of modern digital waveform generators.>

This paper presents a new motion estimation algorithm for inverse synthetic aperture radar imaging known as minimum motion error estimation (MMEE). Conventional cross-correlation motion compensation uses the magnitude of the target's range profiles to obtain a coarse estimate of the target's radial motion. This has to be followed by adaptive beamforming phase compensation before a focused ISAR image can be produced. However, MMEE uses the full complex frequency response to estimate the target's radial motion so accurately that no additional phase compensation is required to produce a focussed ISAR image. Even when high noise levels degrade the MMEEs radial motion estimates, errors in the the motion estimates are exact multiples of the wavelength of the radar's centre frequency and the image remains focussed in the cross-range direction.>

Investigates adaptive waveform selection schemes where selection is based on overall target tracking system performance. Optimal receiver assumptions allow the inclusion of transmitted waveform specification parameters in the tracking subsystem defining equations. The authors give explicit expressions for two one-step ahead optimization problems for a single target in white Gaussian noise when the tracker is a conventional Kalman filter. These problems may be solved to yield the most improvement possible in tracking performance for each new transmitted pulse. In cases where target motion is restricted to one dimension, closed-form solutions to the local (one step ahead) waveform optimization problem have been obtained. The optimal waveform selection algorithms in the paper may be included with conventional Kalman filtering equations to form an enhanced Kalman tracker. Simulation examples are presented to illustrate the potential of the waveform selection schemes for the optimal utilization of the capabilities of modern digital waveform generators, including multiple waveform classes. The extension of the basic waveform optimization scheme to more complex tracking scenarios is also discussed.>

Companion work on the design of envelope-constrained filters is extended and shown to provide an easily implementable adaptive filter with a structure quite similar to that of other adaptive filters based on least-squares techniques. Behavior of the new filter in noise is examined, and a variety of other extensions are discussed. An application to TV channel equalization is explored in some detail.

In the envelope-constrained filtering problem, one attempts to optimize a filter's impulse response subject to the constraint that its response to a specified input lies within a prescribed envelope. This constrained optimization problem is reduced to an unconstrained dual problem with a nondifferentiable cost and then solved using a steepest ascent algorithm based on directional differentials. The resulting technique provides a versatile alternative to the least-squares methods that are commonly applied to such problems. Applications to a wide variety of signal processing problems in areas such as communication channel equalization, television transmission, radar and sonar detection, filter and antenna design, seismology, and ultrasonic imaging are discussed, and some numerical results are presented.