Multiple pulse repetition frequency (multi-PRF) radar is widely utilized due to its advantages in range-Doppler ambiguity suppression and anti-jamming capabilities. Most existing research focuses on intra-frame multi-PRF mode, which lacks flexibility in PRF scheduling and robustness against clutter and dynamic targets. In contrast, inter-frame multi-PRF mode allows PRF variation across frames, enhancing ambiguity resolution and target detection performance in complex environments. However, research on detecting and tracking ambiguous targets under the inter-frame multi-PRF mode remains limited. To address this gap, we propose a multi-frame track-before-detect (MF-TBD) and fusion disambiguation method based on inter-frame multiPRF radar systems, referred to as IF-MF-TBD. First, an efficient method for constructing a pseudo-measurement plane is proposed to mitigate the computational complexity of ambiguity resolution. Then, to estimate the target's ambiguous state across different PRFs, intra-PRF multi-frame energy accumulation is performed within the pseudo-measurement plane, generating multiple sets of ambiguous plot-sequences. Finally, ambiguity resolution is achieved through plot-sequences association and fusion method, yielding unambiguous target tracking results. Simulation results demonstrate that the proposed IF-MF-TBD algorithm effectively enhances target detection and tracking performance in interframe multi-PRF radar systems.

Millimeter-wave radar has been widely adopted in intelligent transportation systems. Modern millimeter-wave radars' high resolution makes a single target yield multiple measurements (point cloud measurements), turning target tracking into an extended target tracking (ETT) problem. The uncertainty in radar measurement sources coupled with the complex spatial distribution of measurements fundamentally challenges ETT algorithms. Conventional ETT algorithms with cluster-then-associate frameworks partition point cloud measurements predominantly through density, failing to exploit target shape information and resulting in suboptimal clustering efficacy. This leads to inaccurate associations between measurements and extended targets, ultimately degrading overall tracking accuracy. This paper proposes a novel approach to circumvent the performance limitations caused by clustering errors in traditional methods. First, we propose a clustering-free closed-loop ETT framework that incorporates the prior target's shape information as feedback. Subsequently, we develop a data association method that leverages the inherent correlation between point cloud measurements and target shape. The associated measurements are then probabilistically fused and integrated into a Kalman filter for state updating. In simulated and real-world datasets, compared with the traditional method, clustering before association, we have validated the effectiveness of the proposed method.

Active sensing, which requires signal transmission, offers high detection performance but suffers from poor stealth capability. In contrast, passive sensing offers strong stealth capability by exploiting non-cooperative illuminators of opportunity (IOs), but its detection performance is weaker due to the unknown IO signals. This paper proposes a target detector for multistatic hybrid active-passive sensing (HAPS) systems based on the generalized likelihood ratio test (GLRT) criterion. The proposed detector aims to combine active and passive sensing strengths to mitigate their respective limitations and enhance overall low-interception detection performance. A test statistic is formulated by integrating both active and passive observations, which is then decomposed into several likelihood functions to reduce computational complexity. Nuisance parameters are estimated within each function and replaced with their maximum likelihood estimates. A low-dimensional GLRT test statistic for HAPS is established by fusing these reduced-dimensional likelihood functions. Simulations show that the proposed detector outperforms purely active or passive detectors, highlighting its superior performance and robustness.

With the increasing demand for traffic situation awareness, extended traffic target (ETT) tracking is a significant yet challenging task especially in tough scenarios with dense and various ETTs. Due to the spatial proximity of ETTs and a noisy sensor, it is challenging for multi-target tracking algorithms to effectively distinguish and track ETTs. To improve the accuracy and robustness of ETT tracking in tough traffic scenarios, we analyze the interaction among ETTs and the lane constraint. Firstly, we develop an interactive motion model for collision avoidance to address trajectory confusion when ETTs are in close proximity. Additionally, we propose a lane constraint method that models lanes as pseudo measurements and constrains the motion of ETTs via pseudo update. Considering the complexity and extendibility, the extended target trajectory probability hypothesis density (ETTPHD) filter is adopted to achieve a more accurate estimation of ETT trajectories. Specifically, we realize the proposed interactive motion model and lane constraint method based on the ETTPHD (IC-ET-TPHD) filter. Performance comparisons between our proposed filter and other algorithms are conducted through both simulations and experiments.

Track-before-detect (TBD) and multi-sensor fusion are two popular methods of weak target detection which can improve the performance by increasing the number of measurements. In this paper, we combine these two methods, proposing a novel multi-sensor track-before-detect (MS-TBD) method for automotive platforms. It can utilize the information from both the spatial and temporal dimensions of the target by jointly processing the measurement from different radars. In particular, the traditional TBD method is often based on an implicit assumption: the presence of targets is unchanged in the sliding window. However, this assumption may not be applicable for automotive multi-sensor systems due to the time-varying fields of view (FOV). To solve the problems mentioned above, we first present an energy accumulation strategy for automotive multi-radar systems and then propose a multiple-hypothesis detection method with the adaptive threshold (AT). It is demonstrated by simulations that the proposed methods show superior performance.

This paper intends to improve the multilane-road target tracking performance by considering heading restrictions. Most existing tracking algorithms assume that vehicles move independently in an open-field environment. However, the movements of vehicles have to be restricted by the geometry of roads, traffic rules, or preset routes. The effective utilization of prior knowledge regarding such restrictions can help to significantly enhance tracking performance. In this paper, we investigate the problem of state estimation while taking into account the heading restrictions imposed by the road direction. To describe the target longitudinal and lateral maneuvering behavior, we design the heading restrictions within a 2-D road coordinate system. The target state vector is then augmented by the y intercept of the constraint straight line, and the measurement vector is augmented by constructing two pseudo-measurements. Consequently, the heading restrictions are incorporated into unscented Kalman filter based on two augmented vectors, called HR-UKF. Furthermore, we employ the singular value decomposition method to enhance numerical stability. Finally, the effectiveness of the proposed algorithm is validated through numerical simulations and real-measured data.

The Bayesian inference has a two-step recursion structure, i.e., prediction and updating, which can be viewed as a dynamic reasoning process. Based on this elegant structure, various multi-target tracking (MTT) algorithms have been invented and successfully applied in many areas. On the other hand, Bayesian inference MTT algorithms are model-based methods that rely on models’ accuracy and first-order Markov assumption. In recent years, the MTT algorithms based on deep learning have received much attention due to their model-free property and the ability to learn from data, although they have issues such as over-fitting, generalization, etc. In this work, we propose a Transformer-based multi-target tracker whose architecture mimics the Bayesian inference, referred to as the Bayesian inference-based Transformer (BAIT) for MTT. To deal with the model mismatch issues, BAIT uses neural networks instead of the pre-assumed motion and observation models while retaining the excellent architecture of Bayesian inference. BAIT can recursively complete accurate predictions and updates via Transformer by refining the estimation of target states in a Bayesian inference-like manner. Thus, BAIT can be viewed as a combination of model-based and data-based methods. The simulation results show that, because of combining the advantages of Bayesian architecture with intelligent data association structure, BAIT is competitive in simple scenarios and achieves superior performance when the data association task becomes complicated.

Deep learning (DL) approaches, which do not rely on models and can learn complex relationships within data, garner increasing attention in the model-free multi-target tracking (MTT) domain. However, the study of applying the DL method to multi-sensor fusion-based MTT is relatively less. In this paper, we propose a Transformer-based distributed multi-sensor MTT approach, which adopts a hybrid fusion structure with both feature-level and decision-level fusion. First, for each local sensor, the high-dimensional feature information is extracted from the measurements based on a Transformer-based tracking module, which enables continuous tracking of multiple targets and provides the predicted target states and corresponding uncertainties. Then, the outputs of local sensors are fused using the covariance interception (CI) fusion rule. Finally, to further improve the fusion performance, the decision-level information is fed into a fusion decoder with the feature-level information to obtain the predicted target state and uncertainties after deep fusion. In this way, we realize a deep utilization of different sensors’ information and achieve a feature-level decision-level hybrid multi-sensor fusion, namely, Transformer-based multi-sensor hybrid fusion (TMSHF). Simulation results show that the proposed fusion method outperforms the CI algorithm in various tracking scenarios.

Joint tracking and classification (JTC) of vehicles is a crucial yet challenging task in intelligent transport and automotive systems. The advent of high-resolution modern sensors necessitates treating vehicles as extended targets. Current extended target tracking (ETT) algorithms provide shape estimations for vehicles, making shape size the most intuitive and accessible feature for classification. This paper contributes two key elements to achieve the JTC of vehicles. For one thing, we introduce the rectangular constraints and customize distinguishable measurement models using modified Gaussian processes (GP). For another thing, based on the customized GP models, we strengthen the role of class in the conditional extended target probability hypothesis density (ET-PHD) filter. Subsequently, we propose a class-enhanced JTC-ET-PHD filter and its Gaussian mixture implementation, enabling simultaneous kinematic, shape, and class estimation of vehicles. Finally, numerical results validate the proposed shape estimation and JTC method, affirming their effectiveness in addressing JTC challenges.

Traditional centralized direct localization methods require the transmission of the complete baseband signal to the fusion center (FC) for target localization. Due to the limited communication bandwidth as well as energy required in transmission, this centralized framework is not suitable for largescale sensor networks. This paper proposes an information-driven decentralized direct localization framework. Firstly, a maximum-likelihood position estimator, based on the Gauss-Newton method, is derived. Then, a decentralized implementation framework is constructed. At its core, there is no dedicated FC while the sensors transmit information to their neighboring nodes only through single hops, achieving target localization through iterative processes based on the concept of consensus. Simulation results confirm the stability and robustness of the proposed method in different scenarios.

The swarm targets have shown great potential for both military and civilian applications, driving a high demand for reliable trajectory generation and accurate activity recognition. In this paper, we propose a trajectory generation method and establish an end-to-end deep learning model for dynamic continuous activity recognition of swarm targets. First, we devise an activity transition model of the drone swarm based on a continuous-time Markov chain (CTMC). Subsequently, the minimum snap trajectory generation algorithm is employed to generate the trajectories. After that, to recognize the dynamic continuous activity of targets, we develop an end-to-end neural network model to extract spatial and temporal features for swarm targets detected by radar across multiple frames. Finally, we demonstrate the effectiveness and robustness of our proposed method through simulation results.

Multi-frame detection has been widely researched in the scenario where the target signal-to-noise is low. However, it becomes a challenging problem under heterogeneous clutter environment. As strong clutter energy is accumulated along with the target in multiple frames, low SNR targets are still annihilated in clutter. To achieve effective clutter suppression and dim targets detection, a novel multi-frame procedure for energy accumulation under heterogeneous clutter is proposed in this paper. The presented architecture concerns a Space-Time Adaptive Processing (STAP) processor and a multi-frame detector. The STAP processor calculates the clutter covariance matrix using multi-frame training samples near the cell under test and extracts data contaminated by the target component. The multi-frame detector is developed to detect dim targets and output estimated target track sequences. Finally, simulation results are given to demonstrate the efficacy of the proposed algorithm.

Weak target recognition, tracking and track management with a low signal-to-noise ratio (SNR) are always tricky problems. Probability hypothesis density (PHD) filtering propagates the first-order multi-target moment to obtain the best Poisson approximation to multi-target density. The PHD filtering does not consider explicit associations between measurements and targets, which is computationally efficient. But it cannot distinguish different targets or extract the time series of track states. Based on track-before-detect (TBD) strategies, this paper proposes labeled PHD (LPHD) filtering and derives its close-form solution, which identifies targets with a unique label. It is derived based on rigorous Bayes criteria, finite set statistics and Kullback-Leibler divergence minimization approximation. The separable TBD-based observation likelihood is conjugate to the Poisson mixture prior for LPHD filtering. Under the point-target assumption, the multi-hypothesis assignments of pixel-to-target are implemented with Murty’s K-shortest path algorithm for LPHD filtering. Additionally, sequential Monte Carlo (SMC) implementations under the nonlinear non-Gaussian assumption are devised. Finally, simulations exhibit good performance in low SNR scenarios.

This paper proposes a power allocation strategy for multi-target tracking in netted radar system under suppression jamming. The aim is to achieve better tracking accuracy with limited power. The information reduction factor is introduced in the posterior Cramér-Rao lower bound (PCRLB) to indicate the uncertainty of the measurement caused by the jamming signal, which makes the detection probability $(P_{d})$ being less than unity. This bound is used as the tracking performance metric. Then, a non-convex optimization problem regarding power allocation is established by minimizing the worst case tracking PCRLB. The gradient projection algorithm is used to solve the formulated problem. Finally, a closed-loop feedback framework is established by using the timely feedback results of target tracking to guide the subsequent power allocation. The simulation results verify the effectiveness of the performance metrics and the superiority of the proposed power allocation strategy.

A key issue in multi-sensor surveillance is the capability to surveil a much larger region than the field-of-view (FoV) of any individual sensor by exploiting cooperation among sensor nodes. Whenever a centralized or distributed information fusion approach is undertaken, this goal cannot be achieved unless a suitable fusion approach is devised. This paper proposes a novel approach for dealing with different FoVs within the context of Generalized Covariance Intersection (GCI) fusion. The approach can be used to perform multi-object tracking on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic tracking scenarios demonstrate the effectiveness of the proposed solution.

In this paper, we address the problem of multisensor management for multi-target tracking via labeled random finite sets (LRFS) in sensor network systems which require both precision and real-time. Considering the optimal multisensor management strategy (proposed in [1] named joint decision making (JDM) algorithm) suffers from the high-dimensional computational complexity, to compromise between tractability and fidelity, an alternative multi-sensor management strategy is proposed. By sequentially calculating the Cauchy-Schwarz (CS) divergence between global generalized Covariance Intersection (GCI) fusion result and the GCI fusion result of two sensors, the JDM algorithm is simplified as a hybrid decision making with a two-dimensional optimization problem, which is referred to as the HDM algorithm, and meanwhile the proposed HDM algorithm is superior to the independent decision making (IDM) algorithm [1] in precision due to the IDM algorithm completely ignores the correlation among sensors. The computational complexity of the proposed method is also provided by comparison with the JDM and IDM algorithms. The efficiency as well as the performance of the proposed method is well demonstrated in a challenging multisensor multi-target tracking scenario by numerical results.

Existing dynamic programming based track-before-detect (DP-TBD) strategies suffer from merit function expansion phenomenon (MFEP), which aggravated the burden of designing the detection threshold. The traditional constant false alarm rate (CFAR) detection is ineffective because the noise energy can not be exactly estimated from the area of merit function expansion. the threshold setting of existing DP-TBD strategies usually resort to the traditional Monte-Carlo counting, the extreme-value theory or its generalized version. For the nonhomogeneous clutter background and the fluctuating target, all of these constant threshold setting strategies inevitably exist the target losing or higher false alarm rate. In addition, for the multi-target scenes, in order to avoid solving high-dimensional optimization problems, existing the most effective DP-TBD methods all use the additional heuristic procedures to extract target trajectories one-by-one from the merit function expansion area by assuming target tracks are always independent. To overcome the aforementioned challenges, a novel one-step greedy optimization TBD algorithm (OSP-TBD) is proposed in this paper. By constraining the physically admissible trajectories, such that the different targets do not occupy the same resolution cell during the same stage and the trajectory with higher merit function (MF) is estimated ahead of others, OSP-TBD can eliminate the MFEP intrinsically and traditional CFAR procedure can be used to detect target adaptively. Besides, the proposed OSP-TBD algorithm can be used to process multi-target situation directly and declare all of the target trajectories corresponding to the states whose MF at the final frame exceed the given detection threshold without any additional heuristic procedure. Numerical simulations are used to assess the performance of the proposed strategies.

This paper proposes a computationally efficient distributed fusion algorithm with multi-Bernoulli (MB) random finite sets (RFSs) based on generalized Covariance Intersection (GCI). The GCI fusion with MB filter (GCI-MB) involves the computation of the generalized MB (GMB) fused density determined by a set of hypotheses growing exponentially with object number. Hence, its applications with multiple targets are quite restrictive, which further motivates an efficient fusion algorithm. In this paper, we propose a novel approximation of the GCI-MB fusion. By discarding the hypotheses with negligible weights, the GCI-GMB fusion amounts to parallelized fusions performed with several smaller groups of Bernoulli components. As such, the computation of the GMB fused density is significantly simplified, with the number of hypotheses reduced dramatically and a practical appealing parallelizable structure achieved. Based on the proposed approximation, a computationally efficient GCI-MB fusion algorithm which can harness large amount of objects is devised. Furthermore, we present the analysis on both the characterization of the L1-error and the computational complexity of the proposed fusion algorithm compared with the standard GCI-GMB fusion. Our analysis shows that the proposed fusion algorithm can reduce the computational expense as well as memories dramatically with slight approximation error. Numerical experiments using the Gaussian implementation for a challenging scenario with twenty objects demonstrate the performance of the proposed fusion algorithm.

In this paper, the out of sequence measurement (OOSM) problem with arbitrary lags in nonlinear dynamic systems is considered. We develop an efficient particle filtering (E- PF) algorithm based on the exact Bayesian solution. Generally, by introducing some reasonable Gaussian assumptions, a general Gaussian smoother is derived to compute the expected smoothing pdfs instead of using the particle smoother, which makes the E-PF computation efficient and applicable for most nonlinear cases. Meantime, for E-PF, only the estimates and covariances for a predetermined maximum number of lags are stored, the storage resource is also effectively saved. In the simulation, a two-dimensional target tracking example is given, the numerical results show that the tracking performance of our algorithm is quite close to the A-PF algorithm proposed by Zhang et al., while the computation cost is significantly reduced.

Multi-frame detection (MFD) and multi-sensor fusion are two popular methods of target detection and estimation which can improve the performance by increasing the number of measurement samples. In this paper, we combine these two methods together, proposing a novel multi-sensor multi-frame detection (MS-MFD) method. On the one hand, MS-MFD can make use of the target information as much as possible through the multi-frame integration. On the other hand, it can acquire the target space-diversity gain by jointly processing the measurement samples on different observation orientations, providing more accurate estimates. In particular, the proposed method consists of two steps. First, it conducts the MFD processing in each sensor node, computing the local multi-frame jointly posterior probability density. Then, it transmits the local densities to the fusion center for further processing, calculating the global target estimates. Furthermore, in order to improve the implementation efficiency of MS-MFD, a Gaussian Mixture model based method is proposed to approximate the distribution of local posterior probability density, so that the transmission costs of local posterior probability density can be significantly reduced. It is demonstrated by simulations that the proposed methods show superior performance.

Medium pulse repetition frequency (MPRF) radar system is widely applied in practice since it combines the desirable features of both low and high PRF radars. However, the corresponding signal processing is more complicated due to range and Doppler ambiguities. N staggered PRFs are designed in the system with the intention of being able to solve ambiguities. Traditional target tracking method solves ambiguities initially by performing ambiguity resolutions over thresholded measurements, while it has a poor performance when target signal-to-noise ratio (SNR) is low. Multi-frame track-before-detect (MF-TBD) is an advantageous method to track dim targets. Unfortunately, it fails to be applied in multiple-PRF radar system directly as the target state space and the measurement space is not the one-to-one correspondence, which results in the increased complexity of the algorithm. In this paper, TBD strategies for multiple-PRF radar system with range and Doppler ambiguities are proposed to solve those issues. A set of new measurements (real measurements combined in a different manner) are firstly synthesized according to different PRF data. Then a cross-boundary search criterion of TBD designed for ambiguous measurements is applied over the N sets of new measurements, producing the ambiguous plot-sequences. Finally, the joint disambiguation is proposed to obtain unambiguous plot-sequences. Simulations show that the proposed method significantly outperforms the classical ambiguity resolution Kalman filter (CAR-KF) algorithm.

In this paper, a new power allocation framework is proposed with the task of multiple target tracking (MTT), in which an adaptive cost function (ACF) with respect to the transmit power and tracking accuracy requirements is first designed. Then we take the ACF as an objective function and formulate the proposed framework as a mathematical optimization problem. In this problem, the posterior Cramér-Rao lower bound (PCRLB) provides us with a lower bound on the estimated error of the targets state. Numerical simulation demonstrates that in the scenario where the common method is not applicable, an effective and robust power allocation scheme can be obtained by the proposed method.

This paper considers the location problem for Moving targets behind a corner. Exploiting multi-path and the algorithm based on phase comparison among the multiple channels can obtain the position of the target behind a corner. To localize the moving target, a scanning radar system with multiple channels is suggested. The false target range can be achieved by the fast Fourier transform(FFT) technique. In addition, the false target azimuth is derived via exploiting the phase differences between the return signals among the multiple channels. Due to false targets and real targets are geometric symmetry, true targets can be localized by the radar system. Finally the experiment results validate this method, and demonstrate the effectiveness.

This paper considers the detection problem and its realistic implementation for multiple-input multiple-output (MIMO) radar with widely separated antennas. In particular, since the range cells of different transmit-receive channels are not in superposition, but intersecting with each other, it is difficult to determine, by gathering measurements from all transmit-receive channels, whether a target is present in an interested resolution cell. Besides, because of the intersecting of transmit-receive channels, the direct thresholding processing, even with the ideal detector, will result in enormous false alarms, which we refer to as “ghost targets” in this paper. To address these realistic detection problems, we propose a two-steps detection procedure based on grid space searching. Specifically, the measurements are organized according to a carefully designed grid space. Then a two-steps detection procedure is performed. The first step is to design a detector and determine the target existence for each channel in a grid cell. The second step is devised to eliminate the ghost targets to decrease the false alarms. Eventually, extensive simulations are provided to validate the efficacy and feasibility of the proposed procedure.

This paper focuses on addressing the data fusion problems in asynchronous sensor networks using distribute particle filter (DPF). Generally, the type of the local information communicated between sensors and the time synchronization of the local information are two major issues for DPF algorithms, which have significant influence on fusion accuracy and communication requirements. To address these issues, in this paper, a likelihood-based asynchronous batch estimation (ABE) scheme is developed, wherein local likelihood function is regarded as the local information to ensure a high fusion accuracy, and the asynchronous likelihood functions of the multiple sensors during a predefined update period are fused to jointly estimate the target states. Then, to implement this framework distributively using particle filter, a likelihood-based ABE DPF (LB-ABE-DPF) algorithm is proposed. In addition, to achieve low communication requirements, the likelihood function is parametrically represented by polynomial approximation and least square (LS) approximation strategies. Numerical results show the efficiency of the proposed algorithm.

This paper considers the multi-target tracking (MTT) problem in multi-input multi-output (MIMO) radar systems with the “defocused transmit-focused receive” (DTFR) operating mode, in which each transmitter forms a defocused beam to illuminate the whole surveillance region and each receiver adopts a focused beam to acquire a high angular resolution. When MIMO radars work in the DTFR operating mode, asynchronous data fusion (ADF) becomes very challenging since the measurements from the same target are acquired by different receivers at different times. To address this problem, we develop a novel batch estimation approach. By incorporating a time-aligned strategy, the local measurement as well as the most recently received asynchronous measurements from other receivers during a predefined update period are fused to jointly estimate the target state. In addition, motivated by the benefits of decentralized fusion architecture, a decentralized batch estimation (DBE) approach and its particle filtering based implementation (DBE-PF) are both presented. Finally, a target tracking scenario is also provided to demonstrate the effectiveness of the proposed DBE-PF approach.

In this paper, we consider an adaptive node and power simultaneous scheduling (ANPSS) strategy for target tracking in distributed multiple radar systems. For all of the available nodes, with full resources allocation, minimizing estimation mean-square error (MSE) may exceed the predetermined system tracking performance goal and cause unnecessary resources consumption. Therefore, tracking performance driven resource allocation schemes for multiple radar systems are proposed. For a predefined estimation MSE threshold, the total transmitted energy is minimized by optimally scheduling node and power resources with the required tracking accuracy. For a given total power budget, the attainable tracking MSE is minimized by optimizing node and power allocation among the transmit radars. The Bayesian Cramer-Rao lower bound (BCRLB) is used as a performance metric. The resulting optimization problems are solved through Zoutendijk method of feasible directions (ZMFD). Numerical results demonstrate that significant resource savings could be obtained through the proposed schemes.

In this paper, we propose an adaptive node selection strategy for target tracking in passive multiple radar systems, with the objective of minimizing the number of nodes in the tracking task. Since the signal parameters are random in passive systems, we first take the expectation over the random parameters, and derive a new Bayesian Cramer-Rao lower bound (BCRLB) as the criterion. Then, we formulate a knapsack-based node selection problem with the required tracking accuracy constraint. This formulation can be solved optimally by an exhaustive search algorithm, but with high computational complexity. For real-time application, we propose an efficient heuristic algorithm to solve it, which offers considerable reduction in computational complexity. Numerical results demonstrate the superior performance of the proposed strategy and the effectiveness of the proposed solution.

In this paper, we address the target detection problem using multi-sensor dynamic programming based track before detect (DP-TBD) methods. First, we give two implementation methods of multi-sensor DP-TBD under the centralized processing and the distributed processing, respectively. Then, in order to improve the implementation efficiency of the multi-sensor DP-TBD, we further propose an improved DP-TBD method based on the approximation of local likelihood. Particularly, the proposed method first calculates the likelihood locally in the sensor nodes, then approximates the likelihood with a weighted sum of a number of basis functions, and finally transmits the weighted coefficients rather than all likelihood to the fusion center for further processing with DP-TBD. By this means, the proposed method can reduce the communication requirements of the system. In addition, since the likelihood are calculated locally, the computational burden of the fusion center can also be alleviated. The analysis and simulation results demonstrate that the proposed method can improve the implementation efficiency significantly with limited performance loss in comparison with the centralized/distributed processing DP-TBD.

In this paper, we consider the fluctuating targets detection problem in a distributed multi-sensor network. A multi-sensor multi-frame track-before-detect (MS-MF-TBD) procedure is proposed to sufficiently make use of the target energy diversity in space and time dimensions (space-time diversity). Two MS-MF-TBD methods, the multi-sensor maximum likelihood-probabilistic data association (MS-ML-PDA) and the multi-sensor dynamic programming based TBD (MS-DP-TBD), are derived, and a number of simulation experiments under different target fluctuation models are performed. Through these simulations, we demonstrate that by using the space-time diversity, MS-MF-TBD methods can efficiently detect the fluctuating targets, achieving significant detection performance gains in comparison to either the single sensor TBD methods or the conventional multi-sensor detection methods.

In this paper, we will investigate a joint beam and dwell time allocation strategy for multiple targets tracking based on the phased array radar system. We achieve the resources allocation by formulating and solving an optimization problem, which is to minimize the total dwell time on all targets with the tracking accuracy of each target satisfying a pre-designed requirement. Since the Bayesian Cramer-Rao lower bound (BCRLB) provides a lower bound on the error of any unbiased estimator, it is employed as the metric for the tracking performance. The optimization problem is proved nonconvex and we solve it through a two-step decomposition algorithm. Simulation results show that the optimization strategy we propose is effective in resource saving and favourable for achieving a better tracking performance of worse targets compared to the operating mode with uniform resources allocation.

In this paper, a joint revisit and dwell time management (JRDTM) strategy for single target tracking based on the predicted Bayesian Cramer-Rao lower bound (BCRLB) in phased array radar system is addressed. We achieve the time resources management by formulating and solving an optimization problem, which is to minimize the resource amount used for tracking with the tracking accuracy of the target meeting a predesigned threshold. As the BCRLB provides a lower bound on the estimated mean square error (MSE) of target state, the predicted BCRLB model with time resources is derived and employed as the metric for the tracking performance. We put forward a converted algorithm to settle the optimization problem subsequently. Simulation results demonstrate that the proposed joint management strategy can help to achieve the tracking performance with less resource consumed.

This paper considers the location and tracking problem for the indoor and outdoor targets with the single input multiple output (SIMO) radar. An effective algorithm based on phase comparison is presented to derive the target azimuth by exploiting the phase differences between the return signals among the multiple channels. In addition, the target range is derived via employing the fast Fourier transform (FFT) technique. Combined with the azimuth achieved, this method can be applied to accurately locate and track the moving targets whatever indoors or outdoors. Finally, the experiment results validate this method, and demonstrate the effectiveness.

The paper addresses the problem of distributed sensor fusion in the framework of random finite set. The Generalized Covariance Intersection (GCI) rule of multi-target densities is extensively used in multi-target Bayesian filtering scheme. But there are two problems in GCI which are unreasonable design of fusion weight and unable to tackle informative differentiation. In order to get rid of the bad influence of these two problems, we propose a heuristic Heuristic distributed fusion (HDF) method by two steps: fusion weight reconstruction and information difference preservation. Finally, we compare the GCI fusion with our proposed HDF method in two scenarios. The results show that HDF is more robust and can achieve better performance.