Stairs Detection for Enhancing Wheelchair Capabilities Based on Radar Sensors

Powered wheelchair users encounter barriers to their mobility everyday. Entering a building with non barrier-free areas can massively impact the user mobility related activities. There are a few commercial devices and some experimental that can climb stairs using for instance adaptive wheels with joints or caterpillar drive. These systems rely on the use for sensing and control. For safe automated obstacle crossing, a robust and environment invariant detection of the surrounding is necessary. Radar may prove to be a suitable sensor for its capability to handle harsh outdoor environmental conditions. In this paper, we introduce a mirror based two dimensional Frequency-Modulated Continuous-Wave (FMCW) radar scanner for stair detection. A radar image based stair dimensioning approach is presented and tested under laboratory and realistic conditions.Comment: 5 pages, Accepted and presented in 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE 2017

Time-Varying Autoregressions in Speech: Detection Theory and Applications

This article develops a general detection theory for speech analysis based on time-varying autoregressive models, which themselves generalize the classical linear predictive speech analysis framework. This theory leads to a computationally efficient decision-theoretic procedure that may be applied to detect the presence of vocal tract variation in speech waveform data. A corresponding generalized likelihood ratio test is derived and studied both empirically for short data records, using formant-like synthetic examples, and asymptotically, leading to constant false alarm rate hypothesis tests for changes in vocal tract configuration. Two in-depth case studies then serve to illustrate the practical efficacy of this procedure across different time scales of speech dynamics: first, the detection of formant changes on the scale of tens of milliseconds of data, and second, the identification of glottal opening and closing instants on time scales below ten milliseconds.Comment: 12 pages, 12 figures; revised versio

Range-Spread Targets Detection in Unknown Doppler Shift via Semi-Definite Programming

Based on the technique of generalized likelihood ratio test, we address detection schemes for Doppler-shifted range-spread targets in Gaussian noise. First, a detection scheme is derived by solving the maximization associated with the estimation of unknown Doppler frequency with semi-definite programming. To lower the computational complexity of the detector, we then consider a simplification of the detector by adopting maximization over a relaxed space. Both of the proposed detectors are shown to have constant false alarm rate via numerical or theoretical analysis. The detection performance of the proposed detector based on the semi-definite programming is shown to be almost the same as that of the conventional detector designed for known Doppler frequency.Comment: First author is Mai P. T. Nguye

Robust Burg Estimation of Radar Scatter Matrix for Autoregressive structured SIRV based on Fr\'echet medians

We address the estimation of the scatter matrix of a scale mixture of Gaussian stationary autoregressive vectors. This is equivalent to consider the estimation of a structured scatter matrix of a Spherically Invariant Random Vector (SIRV) whose structure comes from an autoregressive modelization. The Toeplitz structure representative of stationary models is a particular case for the class of structures we consider. For Gaussian autoregressive processes, Burg method is often used in case of stationarity for its efficiency when few samples are available. Unfortunately, if we directly apply these methods to estimate the common scatter matrix of N vectors coming from a non-Gaussian distribution, their efficiency will strongly decrease. We propose then to adapt these methods to scale mixtures of autoregressive vectors by changing the energy functional minimized in the Burg algorithm. Moreover, we study several approaches of robust modification of the introduced Burg algorithms, based on Fr\'echet medians defined for the Euclidean or the Poincar\'e metric, in presence of outliers or contaminating distributions. The considered structured modelization is motivated by radar applications, the performances of our methods will then be compared to the very popular Fixed Point estimator and OS-CFAR detector through radar simulated scenarios.Comment: 24 pages, submitted to IET Radar Sonar & Navigatio

Adaptive detection with bounded steering vectors mismatch angle

We address the problem of detecting a signal of interest (SOI), using multiple observations in the primary data, in a background of noise with unknown covariance matrix. We consider a situation where the signal signature is not known perfectly, but its angle with a nominal and known signature is bounded. Furthermore, we consider a possible scaling inhomogeneity between the primary and the secondary noise covariance matrix. First, assuming that the noise covariance matrix is known, we derive the generalized-likelihood ratio test (GLRT), which involves solving a semidefinite programming problem. Next, we substitute the unknown noise covariance matrix for its estimate obtained from secondary data, to yield the final detector. The latter is compared with a detector that assumes a known signal signature

Newtonized Orthogonal Matching Pursuit: Frequency Estimation over the Continuum

We propose a fast sequential algorithm for the fundamental problem of estimating frequencies and amplitudes of a noisy mixture of sinusoids. The algorithm is a natural generalization of Orthogonal Matching Pursuit (OMP) to the continuum using Newton refinements, and hence is termed Newtonized OMP (NOMP). Each iteration consists of two phases: detection of a new sinusoid, and sequential Newton refinements of the parameters of already detected sinusoids. The refinements play a critical role in two ways: (1) sidestepping the potential basis mismatch from discretizing a continuous parameter space, (2) providing feedback for locally refining parameters estimated in previous iterations. We characterize convergence, and provide a Constant False Alarm Rate (CFAR) based termination criterion. By benchmarking against the Cramer Rao Bound, we show that NOMP achieves near-optimal performance under a variety of conditions. We compare the performance of NOMP with classical algorithms such as MUSIC and more recent Atomic norm Soft Thresholding (AST) and Lasso algorithms, both in terms of frequency estimation accuracy and run time.Comment: Submitted to IEEE Transactions on Signal Processing (TSP

Detection via simultaneous trajectory estimation and long time integration

In this work, we consider the detection of manoeuvring small objects with radars. Such objects induce low signal to noise ratio (SNR) reflections in the received signal. We consider both co-located and separated transmitter/receiver pairs, i.e., mono-static and bi-static configurations, respectively, as well as multi-static settings involving both types. We propose a detection approach which is capable of coherently integrating these reflections within a coherent processing interval (CPI) in all these configurations and continuing integration for an arbitrarily long time across consecutive CPIs. We estimate the complex value of the reflection coefficients for integration while simultaneously estimating the object trajectory. Compounded with this is the estimation of the unknown time reference shift of the separated transmitters necessary for coherent processing. Detection is made by using the resulting integration value in a Neyman-Pearson test against a constant false alarm rate threshold. We demonstrate the efficacy of our approach in a simulation example with a very low SNR object which cannot be detected with conventional techniques.Comment: submitted to the IEEE Transactions on Aerospace and Electronic System

On Time-Reversal Imaging by Statistical Testing

This letter is focused on the design and analysis of computational wideband time-reversal imaging algorithms, designed to be adaptive with respect to the noise levels pertaining to the frequencies being employed for scene probing. These algorithms are based on the concept of cell-by-cell processing and are obtained as theoretically-founded decision statistics for testing the hypothesis of single-scatterer presence (absence) at a specific location. These statistics are also validated in comparison with the maximal invariant statistic for the proposed problem.Comment: Reduced form accepted in IEEE Signal Processing Letter

Multiple Antenna Cyclostationary Spectrum Sensing Based on the Cyclic Correlation Significance Test

In this paper, we propose and analyze a spectrum sensing method based on cyclostationarity specifically targeted for receivers with multiple antennas. This detection method is used for determining the presence or absence of primary users in cognitive radio networks based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. Analytical expressions for the probability of detection and probability of false-alarm under both spatially uncorrelated or spatially correlated noise are derived and verified by simulation. The detection performance in a Rayleigh flat-fading environment is found and verified through simulations. One of the advantages of the proposed method is that the detection threshold is shown to be independent of both the number of samples and the noise covariance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown to provide higher detection probability and better robustness to noise uncertainty than existing multiple-antenna cyclostationary-based spectrum sensing algorithms under both AWGN as well as a quasi-static Rayleigh fading channel.Comment: 26 pages, 8 figures, submitted to IEEE JSAC: Cognitive Radio Series. arXiv admin note: substantial text overlap with arXiv:1210.817

Computationally Efficient Calculations of Target Performance of the Normalized Matched Filter Detector for Hydrocoustic Signals

Detection of hydroacoustic transmissions is a key enabling technology in applications such as depth measurements, detection of objects, and undersea mapping. To cope with the long channel delay spread and the low signal-to-noise ratio, hydroacoustic signals are constructed with a large time-bandwidth product, $N$. A promising detector for hydroacoustic signals is the normalized matched filter (NMF). For the NMF, the detection threshold depends only on $N$, thereby obviating the need to estimate the characteristics of the sea ambient noise which are time-varying and hard to estimate. While previous works analyzed the characteristics of the normalized matched filter (NMF), for hydroacoustic signals with large $N$ values the expressions available are computationally complicated to evaluate. Specifically for hydroacoustic signals of large $N$ values, this paper presents approximations for the probability distribution of the NMF. These approximations are found extremely accurate in numerical simulations. We also outline a computationally efficient method to calculate the receiver operating characteristic (ROC) which is required to determine the detection threshold. Results from an experiment conducted in the Mediterranean sea at depth of 900~m agree with the analysis