Most previous studies in estimation of the Target position and velocity through Bearing Only Measurements (BOM) consider Targets with constant velocity moving along a straight line. In this paper, state and measurement equations are presented for moving Targets with constant acceleration by using the previously presented state vector in the Extended Modified Polar Coordinates (EMPC) system. In the BOM systems, by increasing the distance between Target and observer (Own ship) the estimation accuracy of the Target kinematic parameters degrades noticeably. In order to solve this problem, here the idea of hybrid data measurements is presented. In this approach both low rate range information, from active sensor, and high rate BOM are exploited. The improvement in the performance of the hybrid system compared to BOM system is represented through computer simulations.

The studies in aerial vehicles modeling and control have been increased rapidly recently. In this paper, a coordination of two types of heterogeneous robots, namely unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) is considered. The UAV plays the role of a virtual leader for the UGVs. The system consists of a vision- based Target detection algorithm that uses the color and image moment of a given Target. The modeling of the VTOL vehicle will be described by using Euler-Newton equations. Flight controller commands are directly generated based on the offset of the Target from the image frame. The image processing and intelligent control algorithms have been implemented on a latest computer. Matlab Simulink has been used to test, analyze and compare the performance of the controllers in simulations.

Maneuvering air Targets Tracking has many applications in defensive and non-defensive areas. Target Tracking requires the estimation of position, velocity, and acceleration, simultaneously. Common approaches for air Targets Tracking measures the distance to Target and Target heading angle which is a nonlinear function of system states. Since these measurements are noisy, using estimating and filtering methods for assessing the speed and acceleration of Target is essential. Although, extended Kalman filter works well with nonlinear systems and Gaussian noises; in practice we encounter with non-Gaussian noises such as Glint which particle filters show better performance in them. In addition, due to the high computational load of particle filters, implementing and applying them is impossible. In this paper, the evolutionary algorithm of particle swarm optimization has been used in the sampling step to reduce computational load and improve the real-time performance of particle filter in solving air Target Tracking problems. The proposed method is simulated and evaluated in a scenario involving all possible motions of the Target with high maneuvering. In addition, performance of an evolutionary particle filter with some particle filters and extended Kalman filter is compared. The simulation results indicate that the evolutionary particle filter has the capability of real time air Targets Tracking with maneuvering in comparison with the particle filter and extended Kalman filter while having high precision.

The superiority of multiple-input multiple-output (MIMO) radars over conventional radars has been recently shown in many aspects. These radars consist of many transmitters and receivers located far from each other. In this scenario, the MIMO radar is able to observe the Targets from different directions. One of the advantages of these radars is exploitation of Doppler frequencies from different transmitter-Target-receiver paths. The extracted Doppler frequencies can be used for estimation of Target velocity vector so that, the radar can be able to track the Targets by use of its velocity vector with reasonable accuracy. In this paper, two different processing systems are considered for MIMO radars. First one is the pulse Doppler system, and the second one is continuous wave (CW) system without range processing. The measurement of the velocity vector of the Target and its counterpart errors are taken into account. Also, the extended Kalman Target Tracking by using its velocity vector is considered. Besides, its performance is compared with those of MIMO Target Tracking without using the velocity vector and conventional radars. The simulation results show that the MIMO radars using velocity vector have superior performance over other above-mentioned radars in fast maneuvering Target Tracking. Since the range processing is ignored in CW MIMO radar systems, the complexity of this system is much lower than that of Pulse Doppler MIMO radar system, but has lower performance in Tracking fast maneuvering Target.

A usual Target-Tracking problem is the type of state estimation problem. The Target states must estimate from noisy, false measurements and dynamic state variable such as position, velocity and acceleration. State estimation needs algorithms called filter, which based on Bayesian estimation. The Tracking successful key is in extracting applicable, useful data from observation about Target states. Certainly, having a proper model of Target dynamics and measurement will make it much easier. Estimation filters and most of other algorithms used for estimation based on model since, there is knowledge about Target dynamics. In this paper, we investigate and compare various Target Tracking models for aerial Target Tracking using visual sensor and accurate laser finder. The results of this study show that Jerk and Markov model have better performance relative to other dynamic models.

We study the problem of power efficient Tracking interval management for distributed Target Tracking wireless sensor networks (WSNs). We first analyze the performance of a distributed Target Tracking network with one moving object, using a quantitative mathematical analysis. We show that previously proposed algorithms are efficient only for constant average velocity objects; however, they do not ensure an optimal performance for moving objects with acceleration. Towards an optimal performance, first, we derive a closed-form mathematical expression for the estimation of the minimal achievable power consumption by an optimal adaptive Tracking interval management algorithm. This can be used as a benchmark for energy efficiency of these adaptive algorithms. Second, we describe our recently proposed energy efficient blind adaptive time interval management algorithm called Adaptive Hill Climbing (AHC) in more detail and explain how it tries to get closer to the derived optimal performance. Finally, we provide a comprehensive performance evaluation for the recent similar adaptive time interval management algorithms using computer simulations. The simulation results show that using the AHC algorithm, the network has a very good performance with the added advantage of getting 9 % closer to the calculated minimal achievable power consumption compared with that of the best previously proposed energy efficient adaptive time interval management algorithm.

The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome these problems. The proposed method uses an adaptive unscented Kalman filter (AUKF) filter to generate the proposal distribution, in which the covariance of the measurement and process of the state are online adjusted by predicted residual as an adaptive factor based on a covariance matching technique. In addition, it uses the genetic operators based strategy to further improve the particle diversity. The results show the effectiveness of the proposed approach.

In this paper, the problem of distributed high-dimensional nonlinear Target Tracking over radar networks is investigated. Since radar performances are different in range and measurement functions, utilizing radar networks, data fusion, and data processing increase the accuracy of Tracking. Among all data fusion methods for radar networks, distributed manner gains the most reliability and less computation burden with high Tracking accuracy. First, nonlinear radar measurements are processed by a local cubature Kalman filter in each agent. Secondly, all processed data over all agents reach an agreement by utilizing a weighted average consensus filter. The proposed algorithm using the consensus on information method decreases the Tracking error and computation burden and guarantees the boundedness for any number of consensus steps. Finally, the efficiency of the proposed algorithm and superiority to traditional methods is demonstrated by performing a numerical simulation of Tracking a maneuvering Target.

In camera-based motion Tracking, the Target structure has a significant impact on the Tracking accuracy. In the present research, a new method is proposed for the optimization of the location of five markers on a visible Target for the camera. The proposed objective function for positioning of markers is equal to the total distance of each marker from all of the planes formed from the combination of triads made of other markers. To avoid the symmetry of the Target structure, which makes the labeling of markers on the image impossible, a constraint is applied that guarantees a minimum difference in the distance between the pairs of markers. The genetic algorithm is exploited for maximizing the proposed objective function with the mentioned constraint. The experimental results obtained from the positioning of markers on the Target using the proposed method (with or without the application of the constraint) and other methods are evaluated. These methods are compared in terms of error in estimating 3D pose and Tracking speed in the presence of noise. It is notable that the results confirm the applicability of the proposed method in the positioning of markers.