Abstract:In this paper, performance evaluation for various single model nonlinear filters and nonlinear filters with interacting multiple model (IMM) framework is carried out. A high gain (high bandwidth) filter is needed to response fast enough to the platform maneuvers while a low gain filter is necessary to reduce the estimation errors during the uniform motion periods. Based on a soft-switching framework, the IMM algorithm allows the possibility of using highly dynamic models just when required, diminishing unreali… Show more
“…The overall idea of a reliable multi-region automatic scene matching system is illustrated in Figure 2, where the NEC system provides the prior location of UAV , which may be derived from the last localisation result P k −1 ′ or the result of INS, GPS, or a combination of these (Jwo et al, 2013). Figure 2. The Idea of Multi-region Scene Matching.…”
Section: The Proposed Vision-based Localisation Systemmentioning
A multi-region scene matching-based localisation system for automated navigation of Unmanned Aerial Vehicles (UAV) is proposed. This system may serve as a backup navigation error correction system to support autonomous navigation in the absence of a global positioning system such as a Global Navigation Satellite System. Conceptually, the system computes the location of the UAV by comparing the sensed images taken by an on board optical camera with a library of pre-recorded geo-referenced images. Several challenging issues in building such a system are addressed, including the colour variability problem and elimination of time-varying details from the pairs of images. The overall algorithm is an iterative process involving four sub-processes: firstly, exact histogram matching is applied to sensed images to overcome the colour variability issues; secondly, regions are automatically extracted from the sensed image where landmarks are detected via their colour histograms; thirdly, these regions are matched against the library, while eliminating inconsistent regions between underlying image pairs in the registration process; and finally the location of the UAV is computed using an optimisation procedure which minimises the localisation error using affine transformations. Experimental results demonstrate the proposed system in terms of accuracy, robustness and computational efficiency.
“…The overall idea of a reliable multi-region automatic scene matching system is illustrated in Figure 2, where the NEC system provides the prior location of UAV , which may be derived from the last localisation result P k −1 ′ or the result of INS, GPS, or a combination of these (Jwo et al, 2013). Figure 2. The Idea of Multi-region Scene Matching.…”
Section: The Proposed Vision-based Localisation Systemmentioning
A multi-region scene matching-based localisation system for automated navigation of Unmanned Aerial Vehicles (UAV) is proposed. This system may serve as a backup navigation error correction system to support autonomous navigation in the absence of a global positioning system such as a Global Navigation Satellite System. Conceptually, the system computes the location of the UAV by comparing the sensed images taken by an on board optical camera with a library of pre-recorded geo-referenced images. Several challenging issues in building such a system are addressed, including the colour variability problem and elimination of time-varying details from the pairs of images. The overall algorithm is an iterative process involving four sub-processes: firstly, exact histogram matching is applied to sensed images to overcome the colour variability issues; secondly, regions are automatically extracted from the sensed image where landmarks are detected via their colour histograms; thirdly, these regions are matched against the library, while eliminating inconsistent regions between underlying image pairs in the registration process; and finally the location of the UAV is computed using an optimisation procedure which minimises the localisation error using affine transformations. Experimental results demonstrate the proposed system in terms of accuracy, robustness and computational efficiency.
“…In each model an UKF is running, and the IMM algorithm makes uses of model probabilities to weight the inputs and output of a bank of parallel filters at each time instant. The interacting multiple model (IMM) [11][12][13][14] algorithm has the configuration that runs in parallel several model-matched state estimation filters, which exchange information (interact) at each sampling time. The IMM algorithm has been originally applied to target tracking, and recently extended to navigation application.…”
A novel scheme using fuzzy logic based interacting multiple model (IMM) unscented Kalman filter (UKF) is employed in which the Fuzzy Logic Adaptive System (FLAS) is utilized to address uncertainty of measurement noise, especially for the outlier types of multipath errors for the Global Positioning System (GPS) navigation processing. Multipath is known to be one of the dominant error sources, and multipath mitigation is crucial for improvement of the positioning accuracy. It is not an easy task to establish precise statistical characteristics of measurement noise in practical engineering applications. Based on the filter structural adaptation, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the GPS navigation processing for time varying satellite signal quality. The uncertainty of the noise can be described by a set of switching models using the multiple model estimation. An UKF employs a set of sigma points by deterministic sampling, which avoids the error caused by linearization as in an extended Kalman filter (EKF). For enhancing further system flexibility, the fuzzy logic system is introduced. The use of IMM with FLAS enables tuning of appropriate values for the measurement noise covariance so as to obtain improved estimation accuracy. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.
“…-detecting unusual values or changes of powerrelated parameters: carrier-to-noise density ratio, absolute received signal power, power variations, L1/L2 band power ratio, -monitoring time-related parameters: length of interval between phase transitions, delay between signals transmitted on different frequencies, -analysis of sample values at correlator output, -spatial processingdetecting multiple signals with the same direction of arrival using a multi-antenna receiver or a mobile single-antenna receiver, -cryptographic protection, -using hybrid navigation systems (GNSS+INS) [4], and many others. Nevertheless each of them has some drawbacks, concerning either increased complexity and cost or effectiveness limited to a certain set of spoofing scenarios.…”
In this article authors present an application of spatial processing methods for GPS spoofing detection and mitigation. In the first part of this article, a spoofing detection method, based on phase delay measurements, is proposed. Accuracy and precision of phase delay estimation is assessed for various qualities of received signal. Spoofing detection thresholds are determined. Efficiency of this method is evaluated in terms of probability of false alarm and probability of detection when 4 to 8 GPS signals are received. It is shown that the probability of spoofing detection is greater than 99 percent if carrier-to-noise ratio is at least 46 dBHz. The second part of the article presents a GPS spoofing mitigation method which uses spatial filtering (null-steering) for excision of undesired signals. Performance of this method is analyzed in various conditions. Attenuation of undesired signals is estimated to be at least 60 dB when their signal-tonoise ratio is high. Furthermore, statistical analysis of the spatial filtering influence on the availability of true signals is provided. Eventually, a concept of practical anti-spoofing system implementation is proposed.
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