Adaptive techniques for multi-target tracking have primarily been based on prior assumptions for the target and its background distribution. The statistical distribution theory, on the other hand, demands more complex mathematical modeling, which turns out to be computationally intensive as well. It is hard to deny the role of distribution theory and probabilistic approaches to the Multi-Target Tracking (MTT) particularly within the last two decades. However, despite the strength of statistical techniques and Bayesian approaches, the number of sensor samples for accurate modeling of current highly dynamical targets and their complex maneuvering capabilities require rather unrealistic assumptions about target dynamics. Practical target maneuvers with today's technology can be so short in duration that constant and uniform acceleration models for several samples may easily result in loss of tracks. This means the target can be undetected for many samples while making sharp turns. In recent years, there has been a paradigm shift toward fuzzy logic and neural network techniques. The membership functions of a fuzzy controller and nonlinear mapping capability of a trained neural network have made these two different technologies a viable combined system. The objective of this paper is to conduct a survey in the fuzzy logic technology as applied to target tracking and discuss its relation to neural networks when combined together.
In this paper, a theoretical comparison is made of the mean carrier-to-noise ratio (CNR) for a coherent (heterodyne detection) equal gain (EG) optical array receiver system with that predicted by a conventional single-aperture monolithic coherent detector system. Our analysis shows that the mean CNR for an EG array receiver system improves significantly over that of a single aperture system. Experimental data taken from a recent outdoor experiment over a range up to 1 km between target and transceiver are also presented and compared with the theory for a particular eight-element EG system developed at the University of Central Florida. Optical signals received by the EG receiver array are launched into eight single mode optical fibers. Phase compensation between the individual receivers is accomplished by wrapping the fiber around PZT cylinders that are controlled by phase compensating electronics.
The detection and processing of laser communication signals are drastically affected by the fading induced onto these signals by atmospheric turbulence. One method of reducing this fading is to use an array of detectors in which each of the detector outputs are added together coherently. This requires measuring the phase difference between each of the receivers and co-phasing each of the detector outputs. This paper presents experimental verification at the Innovative Science and Technology Experimentation Facility over an outdoor range of a 1.06 micron eight element coherent receiver used to mitigate the effects of fading. The system is composed of a 60 mw Nd:Yag laser used as the transmitter and a 27 MHz AO modulator used to frequency shift the transmitted beam. The receiver is composed of eight 1 cm lenses launching the eight received optical signals into eight signal mode optical fibers. Phase compensation between each of the eight receivers is accomplished using single mode fibers wrapped around PZT cylinders that are controlled by phase compensating electronics. The carrier-to-noise (CNR) ratio was measured on a single channel and was then compared with the CNR obtained from the coherent sum of the eight channels. The improvement of the CNR for the coherent sum as compared to a single channel was then compared against theoretical predictions.
While the laser radar systems have high performance at short ranges and low altitudes, the atmospheric effects have been the major constraints of detection and parameter estimation of laser pulses at long ranges and high altitudes. The turbulence which depends on different atmospheric states is hard to quantify due to the wavelength dependent effects of various conditions at different layers of the atmosphere. The turbulence may also be caused by interaction of the atmosphere with other objects, such as the vortex flow due to the aerodynamics of the air targets, or the nonlinear propagation characteristic of the high energy laser pulses. These adverse effects of the atmosphere have been limiting the usefulness of the laser radar systems for a wide range of applications. If the atmosphere is considered as a nonlinear media with nonuniform index of refraction, then it can be thought of as a nonlinear distributed lens under diffraction limited conditions. In this paper, a neural network modeling of the ionosphere layer is presented and the laser pulse is characterized by a set of input features. The transient CO2 laser pulses is simulated to transmit through the atmosphere to a satellite-borne receiver. The satellite receiver model is composed of three stages, i.e., the filtering and processing of the ionospheric propagated waveform, the envelope extraction and channel simulation, and the detection and parameter estimation. The received signal is then evaluated against the background noise through Monte Carlo simulations.
KEYWORDS: Neural networks, Radar, Sensors, Target detection, Signal to noise ratio, Signal processing, Statistical analysis, Signal detection, Polarimetry, Polarization
In this paper, we introduce a neural network (NN) architecture that utilizes nonparametric as well as the conventional parametric statistics. Use of the Wilcoxon two-sample test along with the classical model (e.g. Gaussian) parameters provide a qualitative as well as a quantitative representation of the target and the background. On an ordinal scale the radar returns from the target background are ranked according to a specified order and the neural network is trained with a qualitative factor for deviation from the normal distribution. In addition, the actual background distribution also depends on the type of the sensor as well as the wavelength of operation. Accordingly, the independence of the neural network training from the background noise and the clutter distribution provides a unified design approach for the microwave and the laser radar detection systems.
In recent years, parallel distributed processing has provided a new paradigm for algorithms, such as in missile guidance, which requires a high degree of computational efficiency as well as reliability and smaller size hardware. A problem of particular interest to the guidance literature is the closed-loop optical solutions that can be achieved on-board the missile. Furthermore, a desirable guidance scheme should be robust to low signal-to-noise conditions that generally arise in long-range applications. In this paper we shall present a neural network- based guidance scheme which provides a real-time optimal control on-board the missile with the inclusion of noise in the LOS angular rate data. The neural network is trained in an off-line session using optimal solutions obtained from an optimal control software resulting in a real- time closed-loop guidance method. The performance of the proposed scheme is then evaluated for different levels of SNR of the Line-Of-Sight (LOS) angular rate in a tail-chase engagement. In doing so, similar tests were conducted for the currently used closed-loop proportional navigation method and the potentially available technique of iterative optimal open-loop control with and without the presence of noise in the LOS angular rate. Although we did not include the noise in the missile/target dynamical model, the results indicate that the neural network-based scheme shows more robustness to low signal-to-noise situations as compared with traditional proportional navigation methods. This superiority is due, among other things, to the elimination of some of the restrictive, and in many cases unrealistic assumptions made in the derivation of most current guidance laws in use such as, for instance, unbounded control, simplified dynamics and/or aerodynamics, and non-maneuvering targets, to name a few.
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