In this paper, we present new concepts for path planning and cooperative target assignment for unmanned vehicles in
uncertain environments. The approach is inspired by possible cognitive computing architectures used in mammalian
brains for navigation tasks. A dynamic map based on sensor input and a diffusion equation is used to generate paths
around obstacles to targets. Targets are assigned to each UAV based on simple priority logic or dynamic competition.
Simulation results for a simple scenario with two UAVs and multiple targets are presented.
We describe a concept in which an array of coupled nonlinear
oscillators is used for beamforming in phased array receivers. The
signal that each sensing element receives, beam steered by time
delays, is input to a nonlinear oscillator. The nonlinear
oscillators for each element are in turn coupled to each other.
For incident signals sufficiently close to the steering angle, the
oscillator array will synchronize to the forcing signal whereas
more obliquely incident signals will not induce synchronization.
The beam pattern that results can show a narrower mainlobe and
lower sidelobes than the equivalent conventional linear
beamformer. We present a theoretical analysis to explain the beam
pattern of the nonlinear oscillator array.
Proc. SPIE. 5559, Advanced Signal Processing Algorithms, Architectures, and Implementations XIV
KEYWORDS: Signal to noise ratio, Sensors, Interference (communication), Linear filtering, Signal processing, Nonlinear optics, Electronic filtering, Signal detection, Electromagnetism, Filtering (signal processing)
In this paper, we report on efforts to develop signal processing methods appropriate for the detection of man-made electromagnetic signals in the nonlinear and nonstationary underwater electromagnetic
noise environment of the littoral. Using recent advances in time series analysis methods [Huang et al., 1998], we present new techniques for detection and compare their effectiveness with conventional signal processing methods, using experimental data from recent field experiments. These techniques are based on an empirical mode decomposition which is used to isolate signals to be detected from noise without a priori assumptions. The decomposition generates a physically motivated basis for the data.