Mitigation for Missiles
June 1, 2011 By: Ahmed M. Kamel, Daniele Borio, John Nielsen, Gérard Lachapelle GPS WorldFuzzy Logic and Intelligent Tracking Loops Cope with Interference
A fuzzy tracking system performs as a narrow bandwidth tracking system in terms of noise reduction, and a wide bandwidth tracking system in terms of dynamic response, overcoming the contradiction between receiver bandwidth requirements using classical tracking techniques for either noise reduction or dynamic tracking.
Autonomous navigation systems onboard precision guided missiles or fighter planes depend on GNSS and its very weak signals for positioning and navigation. Performance of a GPS receiver usually depends on the phase-lock loops (PLLs) used to down-convert these weak signals and track their carrier phase and frequency. A PLL can properly work only if its bandwidth is wide enough to track the signal dynamics, which can be significantly high, given the extremely rapid movements, accelerations, and direction changes of a missile or plane. On the other hand, wide-loop bandwidths allow larger portions of noise and interference to enter the tracking loops and disturb the signal tracking process. Excessive noise and interference can lead to loss of lock.
Aiding from a frequency lock loop (FLL) allows reducing the PLL bandwidth. This cannot prevent, however, frequent loss of lock and can be strongly affected by interference. The tradeoff between bandwidth requirements motivates design of alternative tracking systems replacing conventional FLL-assisted-PLLs.
We used fuzzy systems to design and test an innovative FLL-assisted-PLL. The output of a fuzzy controller that replaced standard loop filters drives the numerically controlled oscillator (NCO). The proposed fuzzy frequency phase lock loop (FFPLL) uses both frequency and phase discriminator outputs to generate the required frequency changes to tune the NCO, which in turn generates the local carrier for signal down-conversion.
The main core of any fuzzy system is its fuzzy sets or membership functions (MFs) that map input/output parameters into defined linguistic variables describing the input/output states. Loop discriminator outputs mainly depend on the incoming signal carrier-to-noise power density ratio (C/N0) and have a probability density function (PDF) that, under lock conditions, can be accurately approximated by a Gaussian distribution. Although the mean of this Gaussian distribution is zero under normal tracking conditions, it can be affected by sudden changes in the presence of dynamics that can cause cycle slips and other phase errors. The standard deviation of this distribution is also dependent on the signal quality and hence on the interference level. For these reasons, the discriminator output values have been clustered into several overlapped Gaussian MFs that can linguistically describe their state. The variance of the Gaussian MFs assigned to the phase and frequency discriminator outputs are adaptively tuned according to the incoming signal quality. So any change in the interference power level leads to variations in the Gaussian MF variance to ensure accurate linguistic description of the discriminator output signal. The fuzzy rules are selected to tune the NCO and ensure accurate and robust signal tracking.
We assess performance of the fuzzy tracking system in the presence of different power levels of interference. To generate GPS signals corrupted by radio frequency (RF) interference, we used a hardware GPS signal simulator combined with two external signal generators, and applied different interference levels combined with missile harsh dynamics to test the proposed system. Results show that the fuzzy tracking system significantly improves system robustness and accuracy such that it is able to track very high dynamics with reduced tracking jitter. The system shows resilience against strong interference up to a certain extent where increasing jamming levels are compensated by the online adaptation of the MF distribution on the basis of a small amount of data or C/N0 information.
The system performs favorably against standard tracking loops that cannot sustain the same level of dynamics and interference. The adaptive FFPLL can sustain interference power levels up to J/S = 40 dB. Even when the algorithm loses lock, a fast, reliable reacquisition is obtained when the interference power is reduced.
Theoretical Basis
Most physical processes are nonlinear in nature. Linear approximations and models are employed because linear systems are simple, understandable, and can provide acceptable approx-imations of the actual processes. Unfortunately, most tracking problems are too complex, and their linear approximation does not provide sufficient insight on the system in all environmental conditions.
Standard tracking loop filters are obtained by solving an optimization problem where the noise characteristics and the order of the signal dynamics are known. Different loop orders are obtained for different orders of dynamics. Moreover, the optimization problem is usually solved by considering a linear approximation of the loop. These assumptions are strong, but the standard solution can fail to provide satisfactory performance when the loop is no longer working in its linearity region, or the noise characteristics are not completely known. In such conditions, an approach based on a linguistic description of the system variables may be preferable. In that sense, fuzzy control systems provide sufficient tools for designing a robust alternative to standard loop filter.
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