This paper presents Soli, a new, robust, high-resolution, low-power, miniature gesture sensing technology for human-computer interaction based on millimeter-wave radar. We describe a new approach to developing a radar-based sensor optimized for human-computer interaction, building the sensor architecture from the ground up with the inclusion of radar design principles, high temporal resolution gesture tracking, a hardware abstraction layer (HAL), a solid-state radar chip and system architecture, interaction models and gesture vocabularies, and gesture recognition. We demonstrate that Soli can be used for robust gesture recognition and can track gestures with sub-millimeter accuracy, running at over 10,000 frames per second on embedded hardware.

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Soli: ubiquitous gesture sensing with millimeter wave radar

High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.

Attributed scattering centers for SAR ATR

We present a parametric model for radar scattering as a function of frequency and aspect angle. The model is used for analysis of synthetic aperture radar measurements. The estimated parameters provide a concise, physically relevant description of measured scattering for use in target recognition, data compression and scattering studies. The scattering model and an image domain estimation algorithm are applied to two measured data examples.

A parametric model for synthetic aperture radar measurements

The paper addresses a crucial point in ultra-wideband (UWB) radio wave propagation, which is the spatial-temporal resolution of scattering objects into multiple frequency-dependent scattering centers. The effect contributes to the widely observed temporal dispersion of pulse-shaped transmit signals and their distortion, respectively. Particularly the latter is explained by (multiple) diffraction of the incident wave, leading to (multiple) band-limited impulse responses with characteristic frequency content, which in turn causes signal distortion and a degradation of the signal-to-noise ratio in a correlation receiver. We presented a new approach on UWB propagation modeling and optimum design of correlation receivers.

A study of the ultra-wideband wireless propagation channel and optimum UWB receiver design

A Bayesian approach is presented for model-based classification of images with application to synthetic-aperture radar. Posterior probabilities are computed for candidate hypotheses using physical features estimated from sensor data along with features predicted from these hypotheses. The likelihood scoring allows propagation of uncertainty arising in both the sensor data and object models. The Bayesian classification, including the determination of a correspondence between unordered random features, is shown to be tractable, yielding a classification algorithm, a method for estimating error rates, and a tool for evaluating the performance sensitivity. The radar image features used for classification are point locations with an associated vector of physical attributes; the attributed features are adopted from a parametric model of high-frequency radar scattering. With the emergence of wideband sensor technology, these physical features expand interpretation of radar imagery to access the frequency- and aspect-dependent scattering information carried in the image phase.

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Model-based classification of radar images

This paper presents a new approach to scattering center extraction based on a scattering model derived from the geometrical theory of diffraction (GTD). For stepped frequency measurements at high frequencies, the model is better matched to the physical scattering process than the damped exponential model and conventional Fourier analysis. In addition to determining downrange distance, energy, and polarization, the GTD-based model extracts frequency dependent scattering information, allowing partial identification of scattering center geometry. We derive expressions for the Cramer-Rao bound of this model; using these expressions, we analyze the behavior of the new model as a function of scatterer separation, bandwidth, number of data points, and noise level. Additionally, a maximum likelihood algorithm is developed for estimation of the model parameters. We present estimation results using data measured on a compact range to validate the proposed modeling procedure. >

A GTD-based parametric model for radar scattering

The distortion of a message due to channel noise can be alleviated significantly without redundant error control bits by judicious assignment of binary indices to message symbols. The nonredundant coding gain relies only on a notion of distance between symbols. In this paper, we consider the index assignment problem and adopt a minimax design criterion instead of the usual mean squared error (MSE) measure. The problem is found to be NP-hard, and an effective, heuristic, polynomial-time algorithm is presented for computing approximate solutions. The minimax criterion yields greatly improved worst case performance while maintaining good average performance. In addition, the familiar MSE criterion is shown likewise to yield an NP-hard index assignment task. The MSE problem is a special case of the classical quadratic assignment problem, for which computationally and theoretically useful results are available from the discrete mathematics literature. >

Da-Ming Chiang

Papers

A GTD-based parametric model for radar scattering

Minimax nonredundant channel coding