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Abstract: The problem of frequency estimation of a real-valued sinusoid in colored noise is addressed. A new estimator using higher-order lags of the sample autocorrelation is proposed, and a statistical analysis is provided. Computer simulations are included to validate the theoretical development and to demonstrate that the proposed approach outperforms several existing frequency estimators in terms of estimation accuracy. [Copyright &y& Elsevier]

Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists. [ABSTRACT FROM AUTHOR]

In wireless communication systems, the correlation among the antennas of diversity systems can hardly be neglected. To the best of our knowledge, no previous studies have attended to address, thoroughly, this problem for the cross-correlation-based detectors. This will be the topic of this paper. Namely, in this paper, we consider the performance assessment of a second-order decision metric that is based on the cross correlation of antenna's output. The detector performance analysis has been made concerning the receiver operating characteristic curves, the robustness to estimation uncertainty, and the design parameters. This last introduces the minimum required sample number that guarantees a given detection performance. To this end, the probability distribution of the inner product of two complex non-zero-mean partially correlated random vectors is investigated. Next, we exhibited the relevance of our results through the analytical performance derivation of the cross-correlation-assisted sensing approach in terms of design parameters, spectral efficiency, and decision reliability. Monte Carlo simulations are achieved to prove the tightness of our derived results. [ABSTRACT FROM AUTHOR]

Abstract: A novel unbiased frequency estimator for a single real-valued sinusoid in white noise is proposed, which is based on a normalized second-order infinite impulse response (IIR) notch filter, and can be seen as an extension of the Pisarenko harmonic decomposer (PHD) estimator. The new estimator inherits the simplicity of the PHD estimator and the high accuracy and selectivity achieved by an IIR filter. As the coefficient determining the pole angle of the IIR notch filter depends on the unknown frequency value, an iterative scheme is proposed in which this coefficient is computed from the frequency estimate from the previous iteration. An analytic expression for the mean square error at each iteration is derived. Simulation results show that this iterative method can approach the Cramer–Rao lower bound in a few iterations under different signal to noise ratio levels, provided that the data record length is sufficiently large. [Copyright &y& Elsevier]