Adaptive tracking algorithm with variable observation based on the scheme of monitoring area segmentation

The receiver of a global navigation satellite system (GNSS) is likely to lose tracking for a GNSS signal in some degraded environments. To solve this problem, this paper designs a new adaptive tracking algorithm for GNSS signals. First, we design an error extraction module to extract phase errors so that a CTL can estimate phase errors without using a loop discriminator, which can reduce the requirements of the carrier-to-noise ratio (CNR) of GNSS signals. Second, we design a motion detection module to detect the real-time movement status of GNSS receivers. Then, using its detecting results as inputs, we design an algorithm to automatically make the CTL switch between a second-order loop and a third-order loop. Third, we design a bandwidth-adjusting module to adjust the bandwidth of CTL, according to the CNR and the movement status of a GNSS receiver. Finally, a simulation is performed to verify that our adaptive carrier tracking algorithm can effectively improve the precision of CTL, as well as enhance its dynamic range.

Visual tracking has been widely used in robot systems, and numerous approaches for visual tracking have been proposed. However, developing a robust and real-time visual tracking algorithm which can adaptively track the varying appearance of target under challenging conditions for mobile robot is still an open problem. This paper presents an adaptive probabilistic tracking algorithm with multiple cues integration. An effective evaluation function is proposed to evaluate each cue used for tracking based on their discriminating abilities between foreground and background. Then the likelihood functions of the cues are integrated in particle filter framework with different weights determined based on the evaluation scores. A novel target model updating strategy is proposed to adapt to the varying appearance of target resisting gradual drift which is still an unsolved problem in many adaptive tracking algorithms. Experimental results on a mobile robot demonstrate the robust performance of the proposed algorithm under challenging conditions.

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.

An adaptive template matching-based single object tracking algorithm with parallel acceleration

In order to improve the tracking accuracy of surface target with multiple cruise missiles, an adaptive fusion tracking algorithm based on multi-missile network is proposed. For this method, optimized arrangement of chief missiles and attack missiles is performed to improve the penetration capability of multiple cruise missiles firstly. Then time alignment and space alignment is adopted to reduce the difference multiple cruise missiles. Finally, adaptive fusion tracking algorithm is performed to further improve the tracking accuracy of multiple cruise missiles based on the statistic threshold metric. Simulation results show that, target tracking can be finished more effectively than existing methods by using the proposed algorithm.

For the tracking problem of moving target with scale changes, an adaptive bandwidth tracking algorithm based on principal component variation detection of histogram is proposed. Considering feature characteristics of target and background, such as conspicuousness and similarity, discrimination is estimated to select the feature which can differentiate the target and its background to its extent. The number of feature bins is determined by the discrimination. Target scale variation is analyzed by the number of pixels of principal component in histogram. A novel bandwidth criterion is defined to adjust the tracking window adaptively. Simulation shows that the algorithm can detect the scale change of the target and track the target more accurately.

In this paper an advanced anti-jam indoor adaptive GNSS signal acquisition and tracking algorithm is considered. Initially, we were able to determine that double-dwell structure (DDS) reduces the processing time penalty caused by false alarm. Nevertheless, the double-dwell structure is still vulnerable to interference and jamming. In order to determine a suitable advanced anti-jam indoor adaptive GNSS signal acquisition and tracking algorithm for DDS we first perform the Bayesian parameter estimation; i.e., we analytically compute the posterior Bayes probability density function (pdf) and cumulative distribution function (cdf) by applying the Bayes theorem in three steps. First, we compute the complex signal distribution and complex matrix variate signal distribution. This is an original new result never published before. Second, we provide an introduction of the complex Bessel or parabolic function interference distribution due to earlier assumptions of the DDS interference distribution models. We further explain that the process that is required to produce Complex Matrix Variate Bessel Interference Distribution or Complex Matrix Variate Parabolic Function Interference Distribution is an extremely daunting task let alone the Complex Matrix Variate DDS Bayesian posterior density it would seem at the moment nearly an impossible task because it may require the computation of functions such as Kampe de Feriet function and Jack functions of matrix arguments. Third, instead of performing the second step, we relax the assumption of interference to normal distribution. In both the scalar case and complex matrix variate cases we observe that the complex matrix variate Bayesian posterior pdf or cdf is invariant of the observation data or is identical to the prior complex matrix variate signal distribution model. This is an original new and very powerful result never published before. Why this result is so powerful is because up until now we never had a complete theoretical validation of our GNSS receiver design based on either autocorrelation or cross-correlation properties since, the complex matrix variate Bayesian posterior pdf or cdf is invariant of the observation data or is identical to the prior complex matrix variate signal distribution model. Future simulation results will illustrate that an anti-jam indoor adaptive DDS stable detection structure reduces by half the average acquisition time and significantly outperform its predecessor against interference and jamming. Moreover, future simulation results will show further advancements of the Giftet Inc. MATLAB library capability to perform advanced numerical computations based on closed form expressions of the generalized Bessel function distributions and generalized parabolic cylinder function distribution models via Kampe de Feriet function and Jack functions.

In this paper, we develop a robust visual servo system for object tracking applications of a nonholonomic mobile robot. The system mainly consists of an adaptive shape tracking algorithm and a robust visual servo controller. The adaptive shape tracking algorithm is designed to automatically detect the shape contours of moving objects, extract the shape parameters, and continuously track the object in shape parameter space. Based on direct measurements of the shape parameters, the visual servo controller is designed by using the sliding mode control technique and is robust to uncertainties of the object's motion. Through a Lyapunov-based stability analysis, a sufficient condition on selection of control gains to achieve the tracking goal in finite time is provided.

At endmost stage of flight for tracking, target often tends to fulfill whole scene. If the target maneuvers rapidly, it is easy to lose the target. In this paper, an adaptive template update tracking algorithm model based on graph-regularized salience is proposed to solve this problem. The proposed algorithm introduces the graph-regularized salience model into the target tracking framework. When the target’s size exceeds the setting threshold or the target will overflow the screen in the setting number of frames, that is, the target’s lifetime, salience model is used to extract the most prominent position of the target to update the template, in order to ensure that the template has a good response to the target and ensure the stability of target tracking.

The strong tracking filter (STF) can reduce adaptively estimate bias and thus has ability to track maneuvering target in nonlinear systems. However, STF achieves the perfect performance in maneuvering segment at a cost of the precision in non-maneuvering segment. So based on the strong tracking filter, a new adaptive tracking algorithm (modified strong tracking filter, MSTF) is derived in this paper, which is also suitable for tracking maneuvering target and has improved the precision of STF in non-maneuvering segment as well as maneuvering segment. The Monte-Carlo simulation results show that the MSTF algorithm has a more excellent performance than STF and can estimate efficiently

The SOPLAT (Single Observer Location And Tracking) technique has been used in bistatic systems, in which the transmitter only emits radar signal, and there is no need for synchronization between the transmitter and receiver. Under conditions of knowing PRF of radar signals emitted by the transmitter and the position of the transmitter, an algorithm, WMEKF (Weighted Modified Extended Kalman Filter) can be used to locate and track targets moving with constant speed by means of DOA and TOA measurement series. But for maneuvering targets, WMEKF algorithm is easily diverging, so it is not applicable. A new method for tracking maneuvering targets in bistatic systems with an independent receiver is described. Based on WMEKF algorithm, this paper introduces an adaptive tracking algorithm AWMEKF with a maneuvering detector for tracking maneuvering targets. Its performance for typical maneuvering target paths is evaluated by means of computer simulation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

English vocabulary recognition has certain applications in both learning and life. The existing English vocabulary recognition model is limited by a variety of factors, which will result in a more complicated recognition process and a low recognition accuracy. In order to improve the effect of English vocabulary recognition, based on natural language processing algorithms and corpus systems, this paper proposes a multi-feature fusion adaptive kernel-related filter tracking algorithm for the problems of kernel-related filtering algorithms. Moreover, based on the KCF algorithm, this paper improves the algorithm from three parts: feature fusion, adaptive change of update rate, and scale detection. In addition, this paper explores whether the vocabulary recognition of different rhythms will affect the reaction time and accuracy of the second language vocabulary recognition when the test subjects are in the experimental conditions with similar characters and different voices. The research results show that the model constructed in this paper performs well in the recognition of English words.

Nonlinear shear waves have a cubic nonlinearity which results in the generation of a unique characteristic odd harmonic signature. This behavior was first observed in a homogeneous gelatin phantom using ultrafast plane wave ultrasound imaging and a correlation-based tracking algorithm to determine particle motion. However, in heterogeneous tissue, like brain, the heterogeneities generate clutter that degrades motion tracking to the point where the shock waves and their characteristic odd harmonics are no longer observable. We present a high frame-rate ultrasound imaging sequence consisting of multiple focused emissions that improves the image quality and reduces clutter to generate high quality motion estimates of shear shock waves propagating in the brain. A point spread function analysis is used to characterize the improvements of the proposed imaging sequence. It is shown that the flash focus sequence reduces the side lobes by 20 dB while retaining the same spatial resolution translating to a sensitivity up to the 11th harmonic. The flash focus sequence are then used to acquire high frame-rate (6500 fps) ultrasound movies of an ex-vivo porcine brain in which a shear wave propagates. Using an adaptive tracking algorithm, we compute the particle velocity in a field of view as deep as the brain. It is therefore demonstrated that the proposed method can detect the nonlinear elastic motion and the odd harmonics with sufficient sensitivity to observe the development of a shear wave into a shock wave as it propagates in the brain.

The problem of passively tracking underwater maneuvering targets using time differentials of received sound waves is explored. The paper next develops a two-dimenslonal, adaptive tracking algorithm that gives an unbiased range estimate as a target makes random step changes in radial velocity.

Maneuver detection is an important issue in many adaptive tracking algorithms. In radar tracking system, for the lack of target acceleration measurement, maneuver detection is a complicated problem. A new maneuver detection method using multiple Doppler radars is presented. When radars are at different location, their Doppler measurements can be used to estimate the target's tangential and centripetal acceleration, which are the indicator of the maneuver. Compared with the widely used chi-square maneuver detection method, simulation results show that the new method can detect the maneuver more rapidly and has better tracking performance.