Lockheed Martin Corporation

James A Johnson

Carl Gerst

Steven V Czarnecki

Clifford M Gray

George A VerWys

Mark L Fowler

Craig A Hanna

Methods and systems for amplitude estimation and gain adjustment using noise as a reference are described. An example receiver can include an antenna and a front end amplifier coupled to the antenna. The receiver can also include a detector circuit coupled to the front end amplifier. The receiver can be configured to determine a power of a received signal at the antenna based on a gain of the receiver. The gain of the receiver can be determined based on a noise figure of the front end amplifier and a noise amplitude.

Noise-based gain adjustment and amplitude estimation system

A phasor-based pulse detection system includes a first multiplier stage configured to apply a first delayed conjugate multiplication operation to an input signal. The system can also include a second multiplier stage coupled to the first multiplier stage and configured to apply a second delayed conjugate multiplication operation to an output of the first multiplier stage, and an absolute value unit coupled to the second multiplier stage and configured to perform an absolute value operation on an output of the second multiplier stage. The system can further include video filter stage coupled to the absolute value unit and configured to perform a video filtering operation on an output of the absolute value unit. The system can also include a hysteresis detector coupled to the video filter stage, the hysteresis detector configured for detecting a signal in a filtered video signal received from the video filter stage, the detecting including determining a signal start when the filtered video signal exceeds a predetermined detection threshold for a first predetermined number of consecutive samples, and determining a signal end when the filtered video signal falls below a predetermined rejection threshold for a second predetermined number of consecutive samples.

Phasor-based pulse detection

A phasor-based pulse detection system includes a first multiplier stage configured to apply a first delayed conjugate multiplication operation to an input signal. The system can also include a second multiplier stage coupled to the first multiplier stage and configured to apply a second delayed conjugate multiplication operation to an output of the first multiplier stage, and an absolute value unit coupled to the second multiplier stage and configured to perform an absolute value operation on an output of the second multiplier stage. The system can further include video filter stage coupled to the absolute value unit and configured to perform a video filtering operation on an output of the absolute value unit. The system can also include a hysteresis detector coupled to the video filter stage, the hysteresis detector configured for detecting a signal in a filtered video signal received from the video filter stage.

Signals-of-interest are identified by distinguishing such signals from signals constituting environmental or internal receiver noise. A received signal is rapidly sampled in order to set a dynamic, system threshold. Signals above the threshold constitute signals-of-interest.

Methods and systems for identifying signals-of-interest

A phase-based approach to estimating the frequency of complex sinusoids in the presence of noise that simultaneously reduces the threshold level and makes it frequency-independent. The approach relies on a filter bank approach: apply the signal to a highly overlapped, four-channel filter bank, detect which channel the signal is in, and apply a phase-based frequency estimation technique to the selected channel output. The computational complexity of the method is kept low by deriving a closed form for the resulting estimator and showing that it leads to an inherent decimation. Further improvement in performance can be obtained by cascading several stages of the four-channel filter bank; the performance tends toward that of the ML method as the number of stages increases.

Phase-based frequency estimation using filter banks

In a system for locating the position of a transmitter, a platform containing an antenna is moved through a measurement path. The frequency received by the antenna is measured at measurement points distributed along a measurement path. The frequency is measured by cross correlating coherent pulses of the received frequency signal. An inertial navigation system on the platform indicates the position of the measurement path. A computer determines estimated locations by non-linear least squares convergence starting from trial locations. The non-linear least squares convergence is based on the frequency equation for the received frequency ##EQU1## in which f.sub.0, is the transmitter frequency, V is the antenna velocity and r is the range of the transmitter. The computer evaluates a cost function derived from the frequency equation, for each location estimated by the non-linear least squares convergence and selects the estimated location with the lowest cost function as the best solution.

Doppler triangulation transmitter location system

In a system for locating the position of a transmitter, a platform containing an antenna is moved through a measurement path. The frequency received by the antenna is measured at measurement points distributed along a measurement path. The frequency is measured by cross correlating coherent pulses of the received frequency signal. An inertial navigation system on the platform indicates the position of the measurement path. A computer determines estimated locations by non-linear least squares convergence starting from trial locations. The non-linear least squares convergence is based on the frequency equation for the received frequency ##EQU1## in which f.sub.0 is the transmitter frequency, V is the antenna velocity and r is the range of the transmitter. The computer evaluates a cost function derived from the frequency equation, for each location estimated by the non-linear least squares convergence and selects the estimated location with the lowest cost function as the best solution.

In a long base line interferometer system for determining the position of a transmitter, the phase differences between the signal received by the antennas at each end of the base line are monitored as the interferometer moves through a measurement path to obtain phase difference measurements distributed along the measurement path. A cost function involving measuring the sum of the squares of the differences between measured values and predicted values is determined for each of a set of trial location grid points and the grid point with the lowest cost function is selected as a starting point for determining the transmitter location by least squares convergence. In evaluating the cost function, the predicted values are obtained from the equation: ##EQU1## in which .o slashed..sub.0 is unknown phase offset and the X and Y coordinates of the transmitter are unknown. The least squares convergence is carried out by iteratively adding corrections to predicted values for the unknowns until convergence occurs.

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Owego, NY, United States of America

James A Johnson