BACKGROUND OF THE INVENTION

The problem of parameter estimation for electromagnetic signals remains a central one for geolocation, tracking, pattern recognition, medical imaging, threat detection and for the detection of gestures and other time varying behavior. Most geolocation techniques used rely on phase and angle measurements or line of bearing measurements which are known to be sub-optimal.

Recently, however, techniques have arisen which do not take the information lossy step of only saving time of arrival or angle of arrival, but instead consider the entire channel. These techniques have been extended for the pure geolocation application in the presence of interference. However, there are additional missing considerations for these techniques to achieve their full potential.

First and foremost an adequate model of the channels in question as well as an understanding of the model errors and non-idealities are needed. Indeed previous techniques are potentially very sensitive to calibration errors, and furthermore it does not address near field channels which need to be modeled in order to handle near field applications, such as gesture modeling, shape modeling, medical imaging and security applications. These simple channel models are also inadequate to handle radar, especially in the near field.

There is also a need to marry the extensive literature and novel techniques in the field of Bayesian estimation, since this allows one to naturally obtain error estimates and it is well suited for applying machine learning models for pattern recognition. In particular one or more embodiments of the invention look at models of Gaussian mixtures and solve them using techniques similar to Gaussian Sum particle filters. Gaussian sum filters have been used in part for the tracking problem, but using GPS to obtain geolocation. Techniques provided lieleni will apply to both the near and far field and be more accurate due to the removal of information lossy steps.

Adding some novel techniques for obtaining solutions to electromagnetic boundary problems, along with some cutting edge techniques in Bayesian estimation, and by retaining channel state information directly, one or more embodiments are directed to obtain a novel approach to the estimation and detection problem for multiple antennas/transmitters.

SUMMARY OF THE INVENTION

One or more emboidments of the invention are directed to the use of multi-antenna radar to identify and classify objects of interest as depicted inFIG. 1. However, the technology can apply to any device that transmits and receives over multiple antennas or that receives over diverse positions in space or transmits over diverse transmitters. An example of the latter would be a GPS application as shown inFIG. 2.

The baseline transceiver block diagram is shown inFIG. 3. The transceiver consists of a clock and tuner that phase synchronizes both the transmitter and the receiver. The transmit waveform is typically a stepped frequency tuner, that transmits pure tones over multiple frequencies, sweeping over those frequencies at a fixed rate. The receiver receives those tones after the signal is possibly reflected from an external object. For other applications the transmitter is not present and the receiver is simply channelized.

The direct path transmit waveform is isolated from the receivers via RF shielding, and delay filtering if needed (described below). If a change is detected in the environment, a position estimate is formed using Bayesian estimation with a likelihood function formed using a channel model that models the electromagnetic scattering over the presumed target type. The estimation is essentially performed using hypothesis testing on the target position, coupled with prior information about previous targets.

Both the target type (shape or dielectric constants etc.) are determined by computing the Bayesian posterior probability of that type category. The Bayesian estimation is described below in Parameter Estimation and Tracking. The channel model uses data obtained through careful calibration of both the antenna arrays, the antenna noise models, and the scattering type. The channel model is described in below in System Model and the calibration procedure is described in Calibration. A more detailed step by step description of the calibration process is illustrated inFIG. 4. Each target type must go through an independent calibration procedure, since the target type will determine the basis function coefficients.

Numerous other advantages and features of the invention will become readily apparent from the following detailed description of the invention and the embodiments thereof, from the claims, and from the accompanying drawings.

