This work studies the performance of dimensional least mean square (TDLMS) adaptive filters as prewhitening filters for the detection of small objects in image data. The object of interest is assumed to have a very small spatial spread and is obscured by correlated clutter of much larger spatial extent. The correlated clutter is predicted and subtracted from the input signal, leaving components of the spatially small signal in the residual output. The receiver operating characteristics of a detection system augmented by a TDLMS prewhitening filter are plotted using Monte-Carlo techniques. It is shown that such a detector has better operating characteristics than a conventional matched filter in the presence of correlated clutter. For very low signal-to-background ratios, TDLMS-based detection systems show a considerable reduction in the number of false alarms. The output energy in both the residual and prediction channels of such filters is shown to be dependent on the correlation length of the various components in the input signal. False alarm reduction and detection gains obtained by using this detection scheme on thermal infrared sensor data with known object positions is presented.

This paper demonstrates the low voltage operation of a doubly balanced Gilbert mixer fabricated in a 0.8-m CMOS process and operating as both a down-converter and an upconverter. As a down-converter with an RF input of 1.9 GHz, the mixer has a single sideband noise figure as low as 7.8 dB and achieved down-conversion gain for supply voltages as low as 1.8 V. As an up-converter, the mixer demonstrates 10 dB of conversion gain at an RF frequency of 2.4 GHz with an applied local oscillator (LO) power of 07 dBm and LO-RF/LO-IF isolation of at least 30 dB. Up-conversion gain was achieved over a 5-GHz bandwidth and at supply voltages as low as 1.5 V. The mixer presented demonstrates the lowest single side band noise figure for a CMOS doubly balanced down-converting mixer and the highest frequency of operation for a mixer fabricated in CMOS technology to date.

Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.

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