Autonomous vehicle technology is rapidly becoming the driving force in the automobile industry. As such, the interest in high-resolution radio detection and ranging (radar) for autonomous vehicle applications is increasing due to its affordability and high angular resolution. However, for Advanced Driver Assistance Systems (ADAS), the challenge of azimuth ambiguity caused by a large physical distance between radar antennas is prevalent. This causes false measurements in a direction different from the target’s true angle due to grating lobes. This challenge increases when extended targets are considered. This paper proposes a Pseudo-3D Assignment (P3DA) method based on a Pseudo Measurement Set (PMS) to resolve azimuth ambiguity in multiple extended target tracking. The proposed method can resolve mono (single) and split (duplicated) azimuth ambiguities common in extended target tracking. The proposed solution uses Lagrangian Relaxation based on a Flexible Search (LR-FS) algorithm to solve the P3DA-PMS problem efficiently. The performance of the proposed algorithm in a typical traffic scenario simulated in Unreal Engine 4, with an ego vehicle mounted with both 2D (unambiguous) and 3D (ambiguous) radars, is evaluated. Simulation and experiment results suggest that the proposed P3DA-PMS-based tracking algorithm can perform better than conventional methods.

In the literature, the problem of point target tracking with multipath detections has been studied. However, the case of extended target tracking in a multipath environment (e.g., tracking a submarine using a high resolution sonar, tracking a vehicle in an urban environment using an imaging radar) has not been adequately addressed. If the multipath detections from a single target can be modeled and used properly, better tracking performance can be obtained in terms of accuracy, false tracks and computing time. By integrating the Random Matrix (RM) theory and the random finite set (RFS) theory, an extension of the Probability Hypothesis Density (PHD) filter, called MP-ET-PHD, is proposed in this paper to address the multitarget tracking problem with an unknown number of targets in an uncertain multipath environment with clutter. In the proposed framework, a novel multipath measurement update equation is formulated and derived. Also, a Gaussian Mixture (GM) implementation of the proposed MP-ET-PHD is presented for practical applications. Simulation results show that the proposed MP-ET-PHD can effectively handle multipath detections and yield improved tracking performance over the traditional single-path extended target trackers.

To address the problem of extended target tracking (ETT) with measurement-origin uncertainty in a multipath environment with terrain-constrained motion model, a new generalized version of the standard probabilistic data association (PDA) filter, called MP-ET-PDA, based on random matrices (RM) is proposed in this paper. In the MP-ET-PDA filter, we assume that multipath detections and clutter are possible in the extended target tracking problem, which are prevalent in practical systems but barely addressed in the literature. Further, a clustering-aided MP-ET-PDA algorithm with a reduced computational complexity that makes use of the Variational Bayesian (VB) technique, called MP-ET-PDA-VB, is presented to provide near real-time processing capability in ETT problems with an uncertain multipath environment. In addition to using a constant velocity motion model, a new terrain-constrained motion model is presented for scenarios where terrain-following is required by motion-constrained autonomous vehicles. The posterior Cramér-Rao lower bound (PCRLB), which quantifies the best possible accuracy in realistic ETT problems with multipath detections and measurement-origin uncertainty, is derived as the benchmark for performance evaluation. Simulations results demonstrate the improved performance of the proposed algorithms.

A study was conducted to examine the operational use of multi-sensor data fusion for military airborne intelligence, surveillance and reconnaissance. Participants performed a simulated picture compilation task wherein they had to identify all ships and planes in their area of operation using various sensors. One group performed the task using only native sensor data. The second group had imperfect data fusion to help them resolve the kinematic information, but they still had to identify each contact. The results indicated that data fusion automation improved the identification of ships and planes over the native sensor group. However, over time, map clutter continually increased for the group with fusion automation, surpassing the clutter for the native sensor group. The results suggest that while multi-sensor data fusion has benefits for picture compilation, dealing with plot clutter from false and spurious tracks is a key concern. Interface suggestions are provided to mitigate the effects.

In this paper, the multi-vehicle tracking problem is revisited, with greater consideration being given to the interactions between vehicles. Traditionally, algorithms for tracking multiple vehicles in the multi-lane case assume that vehicles move independently of one another and that longitudinal and lateral vehicle dynamics are mutually independent. However, due to traffic volume, limited lane resources, and traffic heterogeneity, vehicles have to interact with neighboring vehicles for the purposes of maintaining a safe distance from the leading vehicle or improving their navigability by passing slower vehicles. To address the limitations in the literature, this paper proposes a novel multi-vehicle tracking algorithm that integrates the microscopic traffic models (MTM) for modeling interaction behaviors among vehicles in a 2-D road coordinate system. Due to the dependence between the longitudinal and later motions, their corresponding estimates are updated sequentially in a recursive manner. An adaptive deferred decision logic is proposed to improve the accuracy of lateral state estimates and thus improve overall performance. Simulation results show that the proposed MTM-based tracking algorithm can achieve better performance than a conventional multi-lane vehicle tracking algorithm with extension to multi-vehicle tracking, which does not consider interactions among vehicles but updates the longitudinal and lateral motion estimates independently.

In the last five years, the state-of-the-art in computer vision has improved greatly thanks to an increased use of deep convolutional neural networks (CNNs), advances in graphical processing unit (GPU) acceleration and the availability of large labelled datasets such as ImageNet. Obtaining datasets as comprehensively labelled as ImageNet for ship classification remains a challenge. As a result, we experiment with pre-trained CNNs based on the Inception and ResNet architectures to perform ship classification. Instead of training a CNN using random parameter initialization, we use transfer learning. We fine-tune pre-trained CNNs to perform maritime vessel image classification on a limited ship image dataset. We achieve a significant improvement in classification accuracy compared to the previous state-of-the-art results for the Maritime Vessel (Marvel) dataset.

The cardinalized probability hypothesis density (CPHD) filter was proposed as a practical approximation to the multi-target Bayes filter with tractable computational complexity. However, the CPHD filter has limitations in dealing with missed detections, extracting target state in its particle implementations, and in maintaining track continuity. In this paper, a new improved CPHD filter is proposed as a solution to address these limitations, with efficient track continuity and extraction. This filter inherits tractable computational complexity and addresses the drawbacks of the standard CPHD filter. The proposed filter is implemented using Gaussian mixtures, and simulation results demonstrate the effectiveness of the proposed filter compared to the conventional multi-taraet filter in challenging scenarios.

Conventional track-before-detect (TBD) usually considers remote target with constant Cartesian velocity following an approximate straight line motion in sensor coordinates. This model inaccuracy may lead to integrated energy loss especially in near scenario. In this paper, a multi-frame TBD technique in mixed coordinates (MC-MF-TBD) is proposed for weak target detection and tracking in non-Cartesian sensors. Predicted position of a cell in sensor coordinates is obtained by converting Cartesian-coordinate predicted position, achieved according to an assumed velocity in a Cartesian frame, back to sensor coordinates. Then, measurement of each cell is added onto the cell closest to the predicted position to realize energy integration. The procedure of multi-frame accumulation in mixed coordinates is derived in detail. To match the unknown target velocity, a mixed-coordinate-based velocity filter bank is presented and the filter mismatch loss is investigated. Simulation results demonstrate the superiority of MC-MF-TBD compared with other MF-TBD strategies.

Multi-vehicle tracking is crucial in many applications, such as traffic surveillance, intelligent transportation systems, and advanced driver assistance systems. Most conventional multi-target tracking algorithms are not ideal for multi-vehicle tracking, since they assume that the targets move independently of one another. However, due to traffic volume and limited lane resources, vehicles have to interact with their neighbors, resulting in highly dependent motions. To address this limitation, this paper proposes a novel multi-vehicle tracking algorithm for the single-lane case that considers motion dependence across vehicles by integrating the car-following model (CFM) into the tracking process with on-road constraints. A new CFM-based motion model that describes the dependent motion of vehicles in the single-lane case is proposed, and the notion of car-following clusters is defined. In order to exploit all available information in sensor measurements, the proposed algorithm updates the state estimates of car-following clusters by utilizing a stacked-update strategy. Furthermore, the variable structure interacting multiple model estimator is modified and integrated into the proposed algorithm to handle maneuvers that may violate the CFM. Simulation results demonstrate the superiority of the proposed multi-vehicle tracking algorithm over other state-of-the-art multi-vehicle tracking algorithms.

Sea clutter has a major impact on the detection performance of a shipborne high-frequency surface wave radar (HFSWR) system. Due to the platform motion of shipborne HFSWR, the Doppler spectrum of the first-order sea clutter suffers from some broadening so that the targets submerged in this broadening Doppler spectrum can be hardly detected. In this letter, an improved oblique projection (IOP) method, combining the oblique projection (OP) algorithm and the method of sea clutter suppression in the Doppler domain, is proposed to suppress sea clutter in both Doppler domain and spatial domain for shipborne HFSWR. Compared with the OP and the orthogonal weighting algorithms, the proposed IOP algorithm is shown to give far superior suppression results in the Doppler domain and can achieve better azimuth estimation results based on real data.

Beamforming is a fundamental technique in array signal processing. Many existing approaches are based on second-order statistics. However, their performance degrades significantly due to outliers in the received signal. In this letter, we propose an outlier-resistant beamformer design criterion based on minimizing the expectation of the modulus of the array output with an${\ell _1}$-regularization term being added for sidelobe suppression. By using the${\ell _1}$-modulus of complex numbers instead of the standard modulus, the resulting optimization problem can be efficiently solved by a simple iterative algorithm or linear programming. Simulation results in the presence of impulsive signals are provided to demonstrate its robustness and accuracy compared to existing techniques.

This paper considers the localization performance of MIMO radars with widely-separated antennas. A Multiple-Hypothesis (MH) based algorithm is proposed for multiple target localization problems where targets become unobservable in certain pairs of transmitters and receivers. In addition, the performance of MIMO radars in localizing multiple-scatterer targets is compared to that of multistatic radar systems. Finally, simulation results are provided in order to demonstrate the capability of MIMO radars in localizing multiple-scatterer targets.

Decentralized multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, a solution for one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms, is presented. A decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system is presented as well. A distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable is considered. In this study, the approach to use an information-based objective function is utilized. The objective function is based on the posterior Cramér-Rao lower bound and constitutes the basis of a reward structure for Markov decision processes that are used to control the data-fusion process. Three distributed data-fusion algorithms-associated measurement fusion, tracklet fusion, and track-to-track fusion-are analyzed. This paper also provides a detailed analysis of communication and computational load in distributed tracking algorithms. Simulation examples demonstrate the operation and the performance results of the system.

In this paper, we derive a theoretical performance evaluation scheme of Kalman filter based channel tracking and data decoding for multiple-input multiple-output orthogonal frequency division multiplexed (MIMO-OFDM) communication systems that are based on orthogonal space-time block codes. The derivation is approximate, however, it is novel and demonstrated accurate for practical scenarios. Assuming a prior distribution for the initial channel we have derived the instantaneous signal to interference and noise ratio (SINR) for consecutive transmission blocks in the absence of training by exploiting Kalman filtering to track the channel. A theoretical estimation of BER is then derived based on such instantaneous SINR values. The resulting analysis is able to study the effect of different parameters of the system such as the number of antennas, number of sub-carriers, mobile velocity and the assumed channel length on the BER performance of the system. Numerical examples confirm the validity of the theoretical analysis.

In this paper we present a multisensor-multitarget tracking testbed for large-scale distributed scenarios. The objective is to develop a testbed capable of handling multiple, heterogeneous sensors in a hierarchical architecture for maritime surveillance. The testbed consists of a scenario generator that can generate simulated data from multiple sensors including radar, sonar, IR and ESM as well as a tracker framework into which different tracking algorithms can be integrated. In the current stage of the project, the IMM/Assignment tracker, and the Particle Filter (PF) tracker are implemented in a distributed architecture and some preliminary results are obtained. Other trackers like the Multiple Hypothesis Tracker (MHT) are also planned for the future.

Since inexpensive passive sensors have become available, it is possible to deploy a large number of them for tracking purposes in Anti-Submarine Warfare (ASW). However, modern submarines are quiet and difficult to track with passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition to passive receivers (e.g., sonobouys), have the potential to improve the tracking performance. The performance can be improved further by moving the transmitters according to existing target states and any possible new target states. Even though a large number of passive sensors are available, due to frequency, processing power and other physical limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the transmitters and select a subset from the available passive sensors in order to optimize the tracking performance. In this paper, the Posterior Cramer-Rao Lower Bound (PCRLB), which gives a lower bound on estimation uncertainty, is used as the performance measure. An algorithm is presented to decide jointly the optimal path of the movable transmitters, by considering transmitters' operational constraints, and the optimal subset of passive sensors that should be used at each time steps for tracking multiple, possibly time-varying, number of targets. The effect of sensor location uncertainties, due to deployment error and possible sensor drifting, on the tracking performance is addressed in the sensor management algorithm. Simulation results illustrating the performance of the proposed algorithm are presented.

Passive Coherent Location (PCL), which uses commercial signals (e.g., FM broadcast, digital TV) as illuminators of opportunity, is an emerging technology in air defense systems. The advantages of PCL are low cost, low vulnerability to electronic counter measures, early detection of stealthy targets and low-altitude detection. However, limitations of PCL include lack of control over illuminators, limited observability and poor detection due to low Signal-to-Noise Ratio (SNR). This leads to high clutter with low probability of detection of target of interest. In this paper, multiple target tracking algorithms for PCL systems are analyzed to handle low probability of detection and high nonlinearity in the measurement model due to high measurement error. The converted measurement Kalman filter, unscented Kalman filter and particle filter based PHD filter are implemented and compared for PCL radar systems. The feasibility of using transmitters of opportunity for tracking airborne targets is shown on simulated and real data sets.

In this paper, the general problem of dynamic assignment of sensors to local fusion centers (LFCs) in a distributed tracking framework is considered. With technological advances, a large number of sensors can be deployed for multitarget tracking purposes. However, due to physical limitations such as frequency, power, bandwidth, and fusion center capacity, only a limited number of them can be used by each LFC. The transmission power of future sensors is anticipated to be software controllable within certain lower and upper limits. Thus, the frequency reusability and the sensor reachability can be improved by controlling transmission powers. Then, the problem is to select the sensor subsets that should be used by each LFC and to find their transmission frequencies and powers in order to maximize the tracking accuracies and minimize the total power consumption. The frequency channel limitation and the advantage of variable transmitting power have not been discussed in the literature. In this paper, the optimal formulation for the aforementioned sensor management problem is provided based on the posterior Cramer-Rao lower bound. Finding the optimal solution to the aforementioned NP-hard multiobjective mixed-integer optimization problem in real time is difficult in large-scale scenarios. An algorithm is presented to find a suboptimal solution in real time by decomposing the original problem into subproblems, which are easier to solve, without using simplistic clustering algorithms that are typically used. Simulation results illustrating the performance of sensor array manager are also presented.

The impact of delayed sensor alarm data upon a diagnostic inference engine appears not to be well appreciated. In this paper, we illustrate the effect of sensor latency, and we propose an inference approach to obviate it.

This correspondence presents an approach to the detection and isolation of component failures in large-scale systems. In the case of sensors that report at rates of 1 Hz or less, the algorithm can be considered real time. The input is a set of observed test results from multiple sensors, and the algorithm's main task is to deal with sensor errors. The sensors are assumed to be of threshold test (pass/fail) type, but to be vulnerable to noise, in that occasionally true failures are missed, and likewise, there can be false alarms. These errors are further assumed to be independent conditioned on the system's diagnostic state. Their probabilities, of missed detection and of false alarm, are not known a priori and must be estimated (ideally along with the accuracies of these estimates) online, within the inference engine. Further, recognizing a practical concern in most real systems, a sparsely instantiated observation vector must not be a problem. The key ingredients to our solution include the multiple-hypothesis tracking philosophy to complexity management, a Beta prior distribution on the sensor errors, and a quickest detection overlay to detect changes in these error rates when the prior is violated. We provide results illustrating performance in terms of both computational needs and error rate, and show its application both as a filter (i.e., used to "clean" sensor reports) and as a standalone state estimator.

Decentralized multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, we present a solution to one of the main problems of decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. This paper presents a hierarchial multi-level decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. The proposed approach, which is based on hierarchial Markov decision processes and decentralized lookup substrate, will control the information exchange and data fusion process based, among the other parameters, on maximizing performance metrics of individual platforms, thereby enhancing the whole distributed system's reliability as well as that of each participating platform. Simulation examples demonstrate the operation and the performance results of the system.

Over-the-horizon (OTH) radar and automatic identification system (AIS) are commonly used in the surveillance of maritime areas. This paper presents a method, which includes tracking and association algorithms, for fusing the information from these two types of systems into an overall maritime picture. Data to be fused consists of asynchronous track estimates from the OTH system and measurements obtained from AIS. The data available at the fusion center, as output of real world systems, contained incomplete information, compared to theoretical tracking and fusion algorithms. A method to estimate the missing information in the input data is described. Results obtained using real data as well as simulated data are presented. This type of fusion provides overall pictures of maritime areas, with benefits for surveillance against military threats, as well as threats to exclusive economic zones.

This paper presents a decision mechanism based on hierarchical Markov decision processes as a solution for two important problems in multitarget multisensor tracking - distributed data fusion and collaborative sensor management. In application to the distributed data fusion, this paper presents a hierarchical multi-level decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. In application to collaborative sensor management, this paper studies the problem of decentralized cooperative control of a group of unmanned aerial vehicles (UAVs) carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. Simulation examples demonstrate the operation and the performance results.

Prognostics, which refers to the inference of an expected time to failure for a system, is made difficult by the need to track and predict the trajectories of real-valued system parameters over essentially unbounded domains and by the need to prescribe a subset of these domains in which an alarm should be raised. In this paper, we propose an idea, one whereby these problems are avoided: Instead of physical system or sensor parameters, a vector corresponding to the failure probabilities of the system's sensors (which of course are bounded within the unit hypercube) is tracked. With the help of a system diagnosis model, the corresponding fault signatures can be identified as terminal states for these probability vectors. To perform tracking, Kalman filters and interacting multiple-model estimators are implemented for each sensor. The work that has been completed thus far shows promising results in both large-scale and small-scale systems, with the impending failures being detected quickly and the prediction of the time until this failure occurs being determined accurately.

In this paper, we are concerned with the problem of utilizing a large network of sensors in order to track multiple targets. Large-scale sensor array management has applications in a number of target tracking domains. For example, in ground target tracking, hundreds or even thousands of unattended ground sensors may be dropped over a large surveillance area. At any one time, it may then only be possible to utilize a very small number of the available sensors at the fusion center because of physical limitations, such as available communications bandwidth. A similar situation may arise in tracking sea-surface or underwater targets using a large network of sonobuoys. The general problem is then to select a small subset of the available sensors in order to optimize tracking performance. In a practical scenario with hundreds of sensors, the number of possible sensor combinations would make it infeasible to use enumeration in order to find the optimal solution. Motivated by this consideration, in this paper we use an efficient search technique in order to determine near-optimal sensor utilization strategies in real-time. This search technique consists of convex optimization followed by greedy local search. We consider several problem formulations and the posterior Cramer-Rao lower bound is used as the basis for network management. Simulation results illustrate the performance of the algorithms, both in terms of their real-time capability and the resulting estimation accuracy. Furthermore, in comparisons it can also be seen that the proposed solutions are near-optimal.

In this paper, we develop a state-space approach to the blind multiuser detection problem with robustness against mismatches in the desired user signature and the time-varying number of users in the channel. The solution is obtained adaptively using a second-order extended Kalman filter (EKF) and requires only O(L2) operations per iteration, where L is the dimension of the subspace containing the signatures of all the users. We also present a state-space approach to the decision directed multiuser detection problem and an algorithm for switching between robust blind and decision directed detection. The proposed switching algorithm is based on using the normalized innovation square (NIS) of the blind detector to test for its convergence and the NIS of the decision directed detector to detect nonstationarities. Thus, it combines the advantages of both these detection schemes and can achieve an output signal- to-interference-plus-noise ratio (SINR) comparable to that of the minimum mean square error (MMSE) detector without any training, even in the presence of mismatches in the desired user signature. Therefore, it is well suited to practical nonstationary environments where users repeatedly enter and leave the system making the cost of retraining un affordable.

Network-centric multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, we present a solution to one of the main problems of network-centric tracking, namely, decentralized information sharing among the platforms participating in the distributed data fusion. This paper presents a decision mechanism that provides each platform with the required data for the distributed data fusion process while reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogeneous, and potentially unreliable. The proposed approach, which is based on Markov decision processes and decentralized lookup substrate, will control the information exchange process based, among the other parameters, on tracking performance metrics of individual platforms, thereby enhancing the whole distributed system's reliability as well as that of each participating platform. Simulation examples demonstrate the operation and the performance results of the system

In this paper the problem of active object recognition/pose estimation is investigated. The principle component analysis is used to produce an observation vector from images captured simultaneously by multiple cameras from different view angles of an object belonging to a set of a priori known objects. Models of occlusion and sensor noise have been incorporated into a probabilistic model of sensor/object to increase the robustness of the recognition process with respect to such uncertainties. A recursive Bayesian state estimation problem is formulated to identify the object and estimate its pose by fusing the information obtained from the cameras at multiple steps. In order to enhance the quality of the estimates and to reduce the number of images taken, the positions of the cameras are controlled based on a statistical performance criterion, the Cramer-Rao lower bound (CRLB). Comparative Monte Carlo experiments conducted with a two-camera system demonstrate that the features of the proposed method, i.e. information fusion from multiple sources, active optimal sensor planing, and occlusion modelling are all highly effective for object classification/pose estimation in the presence of structured noise

In this paper, we propose two improved particle filtering schemes for target tracking, one based on a gradient proposal and the other based on the turbo principle. We present the basic ideas and derivations and show detailed results of three tracking applications. Favorable experimental findings have shown the efficiency of our proposed schemes and their potential in other tracking scenarios.

In this paper we address the problem of channel equalization and phase noise suppression in orthogonal frequency division multiplexing (OFDM) systems. For OFDM systems, random phase noise introduced by the local oscillator causes two effects: the common phase error (CPE), and the intercarrier interference (ICI). The performance of coherent OFDM systems greatly depends on the ability to accurately estimate the effective dynamic channel, i.e. the combined effect of the CPE and the time-varying frequency selective channel. The proposed approach uses a pilot tone aided particle filter to track/estimate the effective dynamic channel in the time domain and equalizes in the frequency domain. The particle filter is efficiently implemented by combining sequential importance sampling, principles of Rao-Blackwellization, and strategies stemming from the auxiliary particle filter. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.

This paper is concerned with the problem of appearance-based active multi-sensor object recognition/pose estimation in the presence of structured noise. It is assumed that multiple cameras acquire images from an object belonging to a set of known objects. An algorithm is proposed for optimal sequential positioning of the cameras in order to estimate the class and pose of the object from sensory observations. The principle component analysis is used to produce the observation vector from the acquired images. Object occlusion and sensor noise have been explicitly incorporated into the recognition process using a probabilistic approach. A recursive Bayesian state estimation problem is formulated that employs the mutual information in order to determine the best next camera positions based on the available information. Experiments with a two-camera system demonstrate that the proposed method is highly effective in object recognition/pose estimation in the presence of occlusion.

In tracking targets with less-than-unity probability of detection in the presence of false alarms (FAs), data association-deciding which of the received multiple measurements to use to update each track-is crucial. Most algorithms that make a hard decision on the origin of the true measurement begin to fail as the FA rate increases or with low observable (low probability of target detection) maneuvering targets. Instead of using only one measurement among the received ones and discarding the others, an alternative approach is to use all of the validated measurements with different weights (probabilities), known as probabilistic data association (PDA). This paper presents an overview of the PDA technique and its application for different target tracking scenarios. First, it describes the use of the PDA technique for tracking low observable targets with passive sonar measurements. This target motion analysis is an application of the PDA technique, in conjunction with the maximum-likelihood approach, for target motion parameter estimation via a batch procedure. Then, the PDA technique for tracking highly maneuvering targets and for radar resource management is illustrated with recursive state estimation using the interacting multiple model estimator combined with PDA. Finally, a sliding window (which can also expand and contract) parameter estimator using the PDA approach for tracking the state of a maneuvering target using measurements from an electrooptical sensor is presented.

This paper proposes a novel wideband structure for array signal processing. The method lends itself well to a Bayesian approach for jointly estimating the model order (number of sources) and the DOA through a reversible jump Markov chain Monte Carlo (MCMC) procedure. The source amplitudes are estimated through a maximum a posteriori (MAP) procedure. Advantages of the proposed method include joint detection of model order and estimation of the DOA parameters, and the fact that meaningful results can be obtained using fewer observations than previous methods. The DOA estimation performance of the proposed method is compared with the theoretical Cramer-Rao lower bound (CRLB) for this problem. Simulation results demonstrate the effectiveness and robustness of the method.

In this paper blind multiuser detection of Direct Sequence Code Division Multiple Access (DS-CDMA) signals over time-varying time-dispersive channels is considered. A number of methods for multiuser detection over time-dispersive channels have been proposed in previous research. It is shown in this paper that in a time-varying channel these methods will not perform satisfactorily and an adaptive multiuser detector for time-varying channels based on the Interacting Multiple Models estimator is proposed. It is shown by simulations that the proposed method outperforms the existing ones in a time-varying channel.

In this paper, a secure communication system using chaotic systems and Markovian jump systems (MJS) is proposed. MJS evolve by switching from one model to another according to a finite state Markov chain. In the proposed system the chaotic transmitter jumps from one chaotic map (model) to another while transmitting. This feature makes it difficult for someone to track the transmitter, i.e., to eavesdrop, without knowing the exact map parameters. It is assumed that the Markov chain transition matrix is known. The Interacting Multiple Model (IMM) estimator is used at the intended receiver to track the transmitter state. It is also shown that the proposed receiver framework can be used to eavesdrop an unknown chaotic transmitter with limited success. It is shown further that the IMM based receiver structure improves the chaotic parameter modulation receiver based on the extended Kalman filter.

We present a hidden Markov model (HMM) based algorithm for fault diagnosis in systems with partial and imperfect tests. The HMM-based algorithm finds the most likely state evolution, given a sequence of uncertain test outcomes over time. We also present a method to estimate online the HMM parameters, namely, the state transition probabilities, the instantaneous probabilities of test outcomes given the system state and the initial state distribution, that are fundamental to HMM-based adaptive fault diagnosis. The efficacy of the parameter estimation method is demonstrated by comparing the diagnostic accuracies of an algorithm with complete knowledge of HMM parameters with those of an adaptive one. In addition, the advantages of using the HMM approach over a Hamming-distance based fault diagnosis technique are quantified. Tradeoffs in computational complexity versus performance of the diagnostic algorithm are also discussed.