This paper describes a computationally efficient, high-performance, UWB radar interference suppression algorithm. An adaptive FIR (finite impulse response) prediction-error noise- whitening filter exhibits minimal computational complexity and achieves 30 dB interference reduction per pulse (1 microsecond(s) long) with 16-bit simulated interference. Using measured interference data digitized to 8-bits with a 6.5 effective bit digitizer, collected just north of Washington, DC at the Army Research Laboratory, the technique achieved 20 to 27 dB of reduction. To minimize the computational load, the filter weights are periodically determined from data collected during a fraction of a radar range sweep. These weights are found to be effective for hundreds of subsequent radar pulses. Previous work on an estimate-and-subtract, tone-extraction technique resulted in 20 dB average interference reduction on the same measured data with a computational load linearly related to the number of tones extracted. The adaptive filtering approach uses an over-determined system producing an FIR filter with N taps, independent of the number of interference signals. An iterative technique to reduce the range sidelobes caused by the filter's impulse response is also presented. The computational load of this iterative stage is, at worst, linearly related to the number of targets whose sidelobes are extracted. It is shown that, with a small reduction in performance, the sidelobe reduction can be accomplished with a relatively small increase in the overall computational load. The computational complexity of the proposed technique relative to the estimate-and- subtract technique depends on the signal and interference environment and on the acceptable sidelobe level. A comprehensive radio and TV interference simulator was developed to test the interference suppression algorithm. It avoids difficulties in memory requirements and code complexity typically encountered in high-sample rate, long duration, and UWB simulations. Data was generated for various population densities, sampling rates, and quantization levels. Results using the simulation data showed that the performance of the algorithm was related to the quantization level with more bits producing better results.