For **maneuvering** **targets**, such as military aircraft, the ISAR image is blurred on cross-range domain when the RD algorithm is applied. RID imaging method is often used to resolve the Doppler ambiguity. In this paper, a new RID ISAR imaging method base on ADSE is proposed. The proposed algorithm ﬁrst utilizes the reassigned Gabor analysis on each range bin, whose translational motion has been compensated, to obtain the TF distribution. Then, the optimal Doppler spectrums are extracted by using the gradient energy function. Finally, all of the optimal Doppler spectrums are combined to obtain a 2D RID image of the target. The results obtained from simulated and ﬁeld-measured data verify the superiority of the proposed algorithm.

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The simulation results show the tracking of two **maneuvering** **targets**. Both the **targets** are in coordinated turn and also have sudden steering from their coordinated turns. The state vectors of two **targets** are having its position, velocity and acceleration in 2D as [x y x1 y1 x2 y2]. In this algorithm the tracking system is designed based on Singer’s acceleration model [6]. The transition matrix (a ) and input matrix (b ) of the system are considered by defining α to be the inverse of maneuver time constant and the matrices are defined as follows,

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end game [14]. Designers solve this problem by modifying the PNG law to an Augmented Proportional Navigation Guidance (APNG) one. This is achieved by adding a term of the target acceleration into the PNG law, the matter that enables PNG controller to be more effective against highly **maneuvering** **targets**. On the other hand, adding the corresponding term means that the target’s acceleration has to be estimated instantaneously. To overcome this difficulty an integrated fuzzy guidance controller namely IFG is proposed. It is based on the concept of PNG law and consists of two autonomous fuzzy controllers. Each of the two controllers has its own characteristics; together they can achieve the interception. A FSP controller is designed to switch between the two fuzzy controllers. The first fuzzy controller namely FG1 is designed to be with low sensitivity to the target **maneuvering** trying to minimize the control effort (C EFF ) and would be used in

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The modified adaptive Chirplet decomposition (MACD) is presented in this paper. It is based on the extension of the traditional Chirplet atoms to the form of the quadratic frequency-modulated signals. The accuracy of the signal decomposition is improved compared with the traditional Chirplet decomposition. A practical algorithm for the parameters estimation of monocomponent MACD atom and the decomposition method based on the CLEAN technique to the multicomponent signals are proposed. Finally, this algorithm is used in the ISAR imaging of **maneuvering** **targets**, and the quality of ISAR images improves greatly. The results of real data and simulated data demonstrate the validity of the method proposed.

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Figure 1 shows that the esteemed and the real trajectory for the three **targets** are superposable and almost identical even if an abrupt change occurs on the tracked target dynamic. This result is confirmed by the figures (2,3,4,5,6), from this we can say that the tracker based IMM-PF algorithm is a pertinent solution to the problem of visual-based tracking highly **maneuvering** **targets**. In the Other hand figure 2 shows also that the data association is correctly done even if the trajectories cross each other. Figure 7 shows a weak track loss rate, that is to say good performances of our algorithm. This should permit us to say that the GA-JPDA algorithm computes perfectly and its combination with the IMM-PF algorithm (GA-JPDA-IMM-PF) would be an efficient solution to the problem of highly **maneuvering** multi-target visual-based tracking.

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In this paper an algorithm of combining the sensor scheduling with energy efficient for the mobile sensor to track **maneuvering** **targets** is proposed. By taking the Monte Carlo simulation to verify the accuracy of the proposed algorithm, there are two **maneuvering** **targets** considered tracked by adopting the method proposed in this paper. The mobile sensors are randomly distributed in the scenario of the simulation. Thus, the EKF can be applied to estimate the predicted MSE of the estimated target state. On the other hand, the decision of optimal sensor path and the determination of the schedule of sensor sequence could minimize the predicted estimation error caused by tracking the **maneuvering** **targets**.

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In literature [14], the expected posterior covariance matrix of MM filtering method is evaluated as a weighted sum of that of elemental filters which can be further substituted by either MRE or HYCA approximations. Although this seems reasonable, it is only a heuristic one since the data dependent stochastic terms in the posterior covariance matrix of MM filtering method that accounts for the coupling among the elemental filters is neglected. Another important issue we have to keep in mind is that typical radar epoch duration may allow only a very short time for threshold optimization. In this paper, further focuses are devoted to prior threshold optimization for the **maneuvering** target tracking in clutter. Our work differs from that in [14] in that we compute the objective function of threshold optimization by approximating the multi- modal prior target probability density function with a best-fitting Gaussian (BFG) distribution [15] at each time step to estimate the performance measure for tracking **maneuvering** **targets** with linear Markovian switching dynamics. A more reasonable and efficient adaptive detection threshold optimization method for **maneuvering** target tracking in clutter is proposed and extended to the case with the nonlinear measurement equation. A closed-form solution for the NP detector can be also obtained by the functional approximation to the IRF. Obviously the proper choice of the detection threshold is related to the data association method and the method of tracking evaluation. Efficient use of data from a lowered detection threshold may require the more effective data association methods [17]. The probabilistic data association (PDA) is chosen to select measurements for use in the tracking filter in this work.

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Inverse synthetic aperture radar (ISAR) imaging is an eﬃcient method for providing high resolution images of **maneuvering** **targets** especially in many military applications such as target identiﬁcation, recognition, and classiﬁcation. Parameter estimation and motion compensation are the key issues of ISAR imaging [1–6]. In modern warfare, intensive multiple **targets** are often present in radar line-of- sight in various applications such as aircraft or ship formation, ballistic missile with multiple warheads, and space debris, which pose a challenge to the existing imaging techniques [7–9].

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Table 12 lists the results: miss distance (M D = time (F T ), and control effort (C Eff ) for a few of the pri- or scenarios, while the root-mean-square (RMS) values for the 16 scenarios are also added. An example which illustrates how the missile attacks the target is mapped in Fig. 10. Figure 10-a outlines the missile-target tra- jectories for scenario 1 in case of the upward target **maneuvering**, while Fig. 10-b plots the trajectories for scenario 5 in case of the downward **maneuvering**. Fig- ure 11 maps the time histories of the missile lateral ac- celeration for the mentioned two scenarios respectively, where the control effort is calculated as follows:

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Abstract: The maneuverability of modern **targets** becomes more and more complex and variable, which raises higher requirements on the tracking performance of detection systems. Especially the stable and accurate tracking of **maneuvering** **targets** is more critical. For the problem that statistical properties of detection system noise are unknown and the state of motion of **targets** is complex and variable, a new adaptive **maneuvering** target tracking algorithm is proposed. The algorithm adopts the combination of adaptive Kalman filtering under the spherical coordinate system and its counterpart under the Cartesian coordinate system. The adaptive Kalman filtering algorithm under the spherical coordinate system is based on Sage-Husa noise statistics estimator to estimate the statistical property of measurement noise. In the Cartesian coordinate system, the Singer model is used to describe the target motion. Relevant results of the adaptive Kalman filtering algorithm under the spherical coordinate system are used to achieve high-precision estimation of target motion information. Simulation results show that the proposed algorithm has satisfactory tracking accuracy.

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In this paper, we developed a new multiple **maneuvering** target tracker algorithm, referred to as the APPF- CBMeMBer tracker, to handle the presence of unknown and time-varying **maneuvering** parameter. In the pro- posed algorithm, the APE technique was incorporated to achieve online **maneuvering** parameter estimation, and the selected parameter particles were utilized to derive the approximation closed solution. Simulations showed that the newly proposed algorithm can offer higher tracking accuracy in the case of multiple **maneuvering** **targets** over the existing MMP-CBMeMBer algorithm. Furthermore, in order to obtain the individual target tracks, the particle labeling technique is introduced in the proposed algorithm.

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Nowadays passive systems applications, due to limitations of active systems, especially in surveillance applications have increased. In recent decades, the estimation problem of target kinematic parameters (position, velocity and acceleration) through bearing-only measurements (BOM), which is known as Target Motion Analysis (TMA) has been noticed [1-6]. In the bearing-only TMA, the target propagates either an electro-magnetic or an acoustic wave. Then the Directions of Arrival (DOA) of waves are measured by passive sensor(s) and these measurements are processed by tracking filters. Two types of tracking filters exist: і) processing a batch of data ii) processing the data recursively. In this study, the recursive filters are chosen. Extended Kalman Filter (EKF) has received considerable attention in recent years [7-9]. But previous studies have shown that the EKF in Cartesian coordinates exhibits unstable behavior characteristics when utilized for bearing-only TMA [10]. To solve this problem, target kinematic model is written in the so-called Modified Polar Coordinates (MPC) which leads to an EKF which is both stable and asymptotically unbiased [1].This model has been used in many researches on BOM-TMA [5], [11]. The MPC were originally conceived by K. R. Brown and significantly developed by H. D. Hoelzer and co-workers at IBM in the late 1970's [12]. Afterwards, in [1] state and measurement equations in MPC were derived for a constant velocity target moving along a straight line. Then State vector in MPC was extended to include target acceleration components in order to provide practical guidance for homing missiles with bearing only measurements [13]. We used this state vector in the Extended Modified Polar Coordinates (EMPC) system to derive exact state and measurement equations for **maneuvering** **targets**. In the BOM systems, by increasing the distance between the target and the observer (own ship) the estimation accuracy of the target kinematic parameters degrades noticeably. In order to solve this problem,

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At present, most methods to estimate **maneuvering** **targets** are based on one-dimensional linear model [4-5] . Some of them assumed the situation too ideally [6-7] , and lacked reality value. The measured information with seeker should be made the best of. Also the estimation state equation should be close to the reality intercept model. Then the guidance information estimated by the filter was meaningful. In this paper, the extended Kalman filter was constructed in polar coordinate system to estimate guidance signals with the information measured by phased array radar seekers was introduced at first. Then, the initialization parameters for the extended Kalman filter was also proposed. Also, under the typical condition, the simulation was done to verify the performance of methods.

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Target tracking is a key and difficult issue in the field of data fusion research. In particular, the subject of **maneuvering** target tracking has been widely used in military and civilian fields. In order to track the **maneuvering** target, many scholars have proposed many methods to improve Kalman filtering. Chinese scholar Zhou H proposed an adaptive filtering method based on the current statistical model of **maneuvering** **targets** [1], which has a high reputation internationally. To this end, Cai Q and others improved the algorithm and proposed a truncated normal probability density model for target acceleration and its adaptive filtering algorithm [2], which also obtained very good results. But the maximum **maneuvering** acceleration amax of the model system parameters cannot be adjusted adaptively during the tracking process, and the tracking between the rapidity and accuracy of the tracking system is difficult to satisfy.

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herent) MIMO radars are those with closely spaced antennas [3]. While distri- buted MIMO radars, also known as non-coherent MIMO radars, whose anten- nas are placed far from each other [4]. The latter conﬁguration exploits the ran- dom ﬂuctuation of the **targets**’ Radar Cross Section (RCS). This ﬂuctuation causes spatial decorrelation between target echoes, which prevents coherent processing. It is known that spatial resolution can be improved by taking advan- tage of coherent processing introduced by coherent MIMO radar. Several me- thods have been proposed for DOA estimation offering significant estimate per- formance without considering Doppler effects [5] [6]. As shown in [7] and [8], parameters of moving target can be measured by the maximum likelihood esti- mator for distributed MIMO radar. There are many efforts have been made on joint DOA and Doppler frequency estimation [9] [10], while few studies have yet been conducted on **maneuvering** **targets**.

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modify any algorithm and will help us to study any algorithm property and this simulation needs to add another algorithms to solve some tracking problem like **maneuvering** **targets** tracking detector or crossing target problem and in the next generation of this simulation we going to add more newest filters like Extended Kalman Filter (EKF), Particles Filter (PF). This simulation is very useful for any one wants to learn target tracking or wants to work in radar station because it gives him a real work like real radar. Finally, this simulation can be easley modified to work as real tracking radar by inject real data from radar receiver to computer (with synchronal signal) by any computer interface hard-ware like USB, RS485 or PCI acquisition card.

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utilize mismatching sub-models generated by the uncer- tainty of motion models. Although the Kalman filter is usu- ally used to track a target moving within a constant velocity, its tracking performance seriously degrades in tracking **maneuvering** **targets**. Moreover, it requires known process noises. A data-driven approach to tracking which has been presented in [13] uses the least squares fitting of a motion model to a segment of data. Considered process noises, the motion models fitted cannot exactly represent the real motion models of the target. The particle filter is wildly applied in various applications, particularly in **maneuvering** target tracking. An adaptive fuzzy particle filter (AFPF) method proposed in [14] is adapted to general object tracking in the field of computer vision. In the AFPF method, particle filtering samples are weighted using fuzzy membership functions and are applied to geometric and appearance features. The particle filter based on modified generalized probabilistic data association (PF-MGPDA) proposed in [15] combines the advantage of particle filter and generalized probabilistic data association to track **maneuvering** multi-**targets**. However, because the tracking performance of particle filter is combined in proportion to the number of the corresponding particles in target track- ing, the **maneuvering** target tracking methods based on particle filter are difficult to satisfy the real tracking require- ment of tracking systems. For this reason, here, the particle filter is not utilized to track **maneuvering** target in air surveillance. Although the H ∞ filter and its modified forms

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Multiple target tracking (MTT) is taken into account as one of the most important topics in tracking **targets** with radars. In this paper, the MTT problem is used for estimating the position of multiple **targets** when a 2-D radar is employed to gather measurements. To do so, the Joint Probabilistic Data Association Filter (JPDAF) approach is applied to tracking the position of multiple **targets**. To characterize the motion of each target, two models are used. First, a simple near constant velocity model is considered and then to enhance the tracking performance, specially, when **targets** make **maneuvering** movements a variable velocity model is proposed. In addition, a combined model is also proposed to mitigate the **maneuvering** movements better. This new model gives an advantage to explore the movement of the **maneuvering** objects which is common in many tracking problems. Simulation results show the superiority of the new motion model and its effect in the tracking performance of multiple **targets**.

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Ship **maneuvering** response with ship obstacle can be seen in figure 8. There is a little dissimilarity from ship **maneuvering** response without obstacle. At point E precisely at t = 114, route from both of ships crossed each other. Because of that anti ship collision controller change ship heading from ψ = -9° into ψ = 5° for 9 seconds before it change back to ψ = -5° at t = 123. Time here is simulation time from 0 to 318 second. Actual sailing time from Tanjung Perak to Karang Jamuang is approximately 3 hours normally. Intersection point between two ships happened at t = 114 s in simulation time around 70 minutes in real time. Ship response at point E quite decent, where actual heading can react when desired heading set point suddenly changed because route from both of ships crossed. But because of new desired heading at point E have a short time span (9 seconds), actual heading cant reached desired set point at - 9° but it only reached -1° before desired heading back to normal. After ship intersection at point E, there is no other intersection because both of ships sail in different direction. G. Close Loop Test With Ship Obstacle and With Wind

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The second variant of strategic **maneuvering** takes place where the rhetorical aims are both to propositionally distance oneself from the arguer’s position, as well as to disqualify and exclude the arguer from the discussion, or at least to depict the victim of the ridicule as lacking the required credibility. Part of the mechanism behind this **maneuvering** is that the exclusive kind of laughter, either produced by the critic or elicited from the audience, can be taken as expressing the implausibility of the arguer’s position as well as the utter lack of credibility and significance of the arguer himself. If the arguer himself cannot suppress a laugh, this can be interpreted as an acknowledgement that the critic is onto something. Another part of the mechanism is that the humorous effect is often generated by pushing at the boundaries of politeness: “humour at once permits, legitimizes and exonerates an insult” (Lockyer and Pickering, 2005, p. 12). Consequently, in many cases it will be impossible to decipher whether the message is excluding rather than

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