Dwell Time Allocation Research Articles

Joint Target and User Assignment as well as Dwell Time and Spectrum Allocation in a Distributed Radar–Communication Coexistence Network

Joint Target and User Assignment as well as Dwell Time and Spectrum Allocation in a Distributed Radar–Communication Coexistence Network

A Fast Solver for Dwell Time Allocation in a Phased Array Radar

The dwell time resource is usually limited when a phased array radar (PAR) tracks multiple targets. In this paper, a fast solver is proposed for the dwell time allocation (DTA) in the cluttered environments. The optimization model is established as minimizing the worst case of position Bayesian Cramér-Rao lower bounds (BCRLBs) among multiple targets under the constraint of total dwell time budget. The BCRLB minimization is hindered by the intractable information reduction factor (IRF) term, which expresses the information loss due to clutter. By replacing the IRF with its upper bound, the probability of detection (PD), we formulate a closed-form objective function-based optimization problem. By using the strictly decreasing property of the BCRLB, two optimal conditions are proposed to obtain a unique subset of the optimal solutions. Then, the remaining parts can be directly set as the lower or upper bounds. Simulation results confirm the competitive performance and computation saving, compared with existing convex optimization-based methods and the sequential relaxation-based method, especially when facing a large number of targets. The target with a lower signal-to-noise ratio (SNR) will be given more dwell time resources.

Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks.

Resource saving based dwell time allocation and detection threshold optimization in an asynchronous distributed phased array radar network

The resource optimization plays an important role in an asynchronous Phased Array Radar Network (PARN) tracking multiple targets with Measurement Origin Uncertainty (MOU), i.e., considering the false alarms and missed detections. A Joint Dwell Time Allocation and Detection Threshold Optimization (JDTADTO) strategy is proposed for resource saving in this case. The Predicted Conditional Cramér-Rao Lower Bound (PC-CRLB) with Bayesian Detector and Amplitude Information (BD-AI) is derived and adopted as the tracking performance metric. The optimization model is formulated as minimizing the difference between the PC-CRLBs and the tracking precision thresholds under the constraints of upper and lower bounds of dwell time and false alarm ratio. It is shown that the objective function is nonconvex due to the Information Reduction Factor (IRF) brought by the MOU. A cyclic minimizer-based solution is proposed for problem solving. Simulation results confirm the flexibility and robustness of the JDTADTO strategy in both sufficient and insufficient resource scenarios. The results also reveal the effectiveness of the proposed strategy compared with the strategies adopting the BD without detection threshold optimization and amplitude information.

Beam Scheduling for Early Warning With Distributed MIMO Radars

Distributed multiple-input multiple-output (MIMO) radars achieve long-range detection by steering and focusing the narrow beams of all transmit and receive antennas on a single point. This paper proposes a beam scheduling scheme based on the joint optimization of three components: arrangement of focal points, beam dwell time allocation of each focal point, and scanning order of focal points. The purpose is to enable distributed MIMO radars to provide early warning of incursions entering a broad surveillance region. To this end, a mathematical model is first developed to evaluate radar detection capability in a cell under test (CUT) over a scheduling period. By integrating all the CUTs within the region, the detection time required to reach a specified performance in terms of cumulative detection probability is then derived. On this basis, a beam scheduling strategy is devised, considering the maximum speed and minimum average radar cross-section constraints of targets to be detected. The main mechanism of the strategy is to minimize the detection time without sacrificing performance. To address the optimization problem of beam scheduling, we propose a two-stage method using a detection capability threshold as an extra optimization variable. In our method, the first stage is used to obtain optimal schemes for different given thresholds, and in the next stage, the optimal threshold is determined from these schemes. The simulation results show that the proposed algorithm is effective.

Collaborative radar node selection and transmitter resource allocation algorithm for target tracking applications in multiple radar architectures

This article develops a collaborative radar node selection and transmitter resource allocation (CRNS-TRA) algorithm for target tracking applications in multiple radar architectures. The key mechanism of the CRNS-TRA strategy is to coordinate the radar node selection, transmit power, and dwell time of multiple radar architectures to enhance the target tracking accuracy under the constraints of several transmitter resource budgets. The analytical expression for the Bayesian Cramér-Rao lower bound (BCRLB) is derived and employed as the objective function to quantify the target tracking performance. It is shown that the CRNS-TRA problem is a mixed-integer, non-convex, and non-linear optimization problem, where the three involved parameters, that is, the radar node selection, transmit power and dwell time allocation, are all coupled in the objective function and the constraints. Subsequently, a fast and effective two-stage-based solution methodology is proposed for solving the resulting problem, which incorporates the interior point method and cyclic minimization framework. Extensive simulation results are provided to verify that the presented CRNS-TRA scheme is capable of effectively improving the target tracking performance with much lower operation time consumption when compared to other state-of-the-art resource allocation benchmarks.

Joint tracking sequence and dwell time allocation for multi-target tracking with phased array radar

Phased array radars (PAR) are demonstrated to provide enhanced performance for target tracking due to their agile beamforming. In this paper, a joint tracking sequence and dwell time allocation (JTSDTA) strategy is proposed for multi-target tracking (MTT) based on a PAR system. At its core, for the first time, the implementations of target tracking and searching are considered to be interleaved, and the effect of the revisit time on the tracking performance is emerged when arranging the sequence of target tracking. In this context, a time-aware posterior Cramér–Rao lower bound is presented to quantify the individual tracking performance. Then, the JTSDTA is formulated as an optimization problem with respect to the coupled integer and continuous variables. A two steps-based decoupling algorithm is proposed to solve the problem efficiently. Numerical simulations are presented to demonstrate the validity of the proposed JTSDTA strategy.

Dwell Time Allocation Algorithm for Multiple Target Tracking in LPI Radar Network Based on Cooperative Game.

To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low interception performance by optimizing the allocation of the dwell time of each radar under the constraints of the given target detection performance, minimizing the total dwell time of radar network. By introducing two variables, dwell time and target allocation indicators, we decompose the dwell time and target allocation into two subproblems. Firstly, combining the Lagrange relaxation algorithm with the Newton iteration method, we derive the iterative formula for the dwell time of each radar. The dwell time allocation of the radars corresponding to each target is obtained. Secondly, we use the fixed Hungarian algorithm to determine the target allocation scheme based on the dwell time allocation results. Simulation results show that the proposed algorithm can effectively reduce the total dwell time of the radar network, and hence, improve the LPI performance.

Joint Target Assignment and Resource Optimization Framework for Multitarget Tracking in Phased Array Radar Network

In this article, a joint target assignment and resource optimization (JTARO) strategy is proposed for the application of multitarget tracking in phased array radar network system. The key mechanism of our proposed JTARO strategy is to employ the optimization technique to jointly optimize the target-to-radar assignment, revisit time control, bandwidth, and dwell time allocation subject to several resource constraints, while achieving better tracking accuracies of multiple targets and low probability of intercept (LPI) performance of phased array radar network. The analytical expression for Bayesian Cramér-Rao lower bound with the aforementioned adaptable parameters is calculated and subsequently adopted as the performance metric for multitarget tracking. After problem partition and reformulation, an efficient three-stage solution methodology is developed to resolve the underlying mixed-integer, nonlinear, and nonconvex optimization problem. To be specific, in Step 1, the revisit time for each target is determined. In Step 2, we implement the joint signal bandwidth and dwell time allocation for fixed target-to-radar assignments, which combine the cyclic minimization algorithm and interior point method. In Step 3, the optimal target-to-radar assignments are obtained, which results in the minimization of both the tracking accuracy for multiple targets and the total dwell time consumption of the network system. Simulation results are provided to demonstrate the advantages of the presented JTARO strategy, in terms of the achievable multitarget tracking accuracy and LPI performance of phased array radar network.

Cognitive Dwell Time Allocation for Distributed Radar Sensor Networks Tracking via Cone Programming

For a dwell time limited radar network, the achievable tracking performance can be further enhanced by adaptively tuning in the dwell time allocation strategy to accommodate the future operation scenario. In this paper, considering this dynamical resource allocation problem, a cone programming-based dwell time allocation scheme is proposed for achieving the optimal tracking performance. The basic mechanism is to exploit the feedback of its operating environment based on the interrogation of received echoes for informing the decision of future optimal dwell time allocation. To achieve this purpose, the predicted conditional Cramer-Rao lower bound (PC-CRLB) of target state estimates is developed for evaluating the tracking performance of each candidate allocation strategy conditioned on the cognitive knowledge firstly. And, its trace is chosen as a scalar allocation strategy evaluation metric. On this basis, the closed-form expression of the scalar evaluation metric is derived, which enables us to transform the dwell time allocation problem into the second-order cone program (SOCP). Extensive simulations demonstrate the efficiency and superiority of the proposed allocation scheme.

Cognitive Resource Allocation for Target Tracking in Location-Aware Radar Networks

In this letter, a general framework of cognitive resource allocation for target tracking in radar networks is proposed. Firstly, an allocation strategy evaluation metric is established in this framework, i.e., the predicted conditional Cramer-Rao lower bound (PC-CRLB), for evaluating the future response of each candidate allocation strategy. Then, the optimal resource allocation strategy can be found by solving a constrained optimization problem. To specify the framework implementation details, a dwell time allocation problem is considered. For this given problem, the analytical expressions of PC-CRLB and its derived scalar strategy evaluation metric are derived, enabling us to convert the allocation problem to a second-order cone program (SOCP). Numerical results demonstrate the effectiveness of the proposed framework.

The role of stimulus predictability in the allocation of attentional resources: an eye-tracking study.

By allocating less attention to predictable events we are able to focus on novel, unpredictable and unexpected events that require more extensive processing. This strategy should result in improved performance by optimizing the use of brain's limited resources. Participants' task was to look at two types of stimuli presented simultaneously at the opposite sides of a computer screen: "static" stimuli, i.e. emotionally neutral photographs; and "dynamic" stimuli, i.e. video clips presenting a moving dot. The dot moved along a predictable, semi-predictable or random trajectory. This was followed by a memory test of the static stimuli. Participants spent more time looking at the dynamic stimuli when its trajectory was less predictable. Additionally, participants who readily adjusted their dwell time allocation to the dot trajectory performed better in the memory test, as demonstrated by a positive correlation between memory test sensitivity and the rate of eye movement patterns adjustment to stimulus predictability. This suggests that people adjust gaze duration to stimulus predictability and that doing so optimizes attentional resource allocation and improves performance. However, study design did not allow to distinguish between spatial and temporal predictability, so it is impossible to estimate the impact of each type of predictability specifically.

Neutral‐point voltage deviation control for three‐level inverter‐based shunt active power filter with fuzzy‐based dwell time allocation

Three-level inverters have emerged as the main alternative to replace the use of standard two-level inverters in current harmonics mitigation. Neutral-point diode clamped (NPC) inverter is the most attractive candidate due to its robustness. However, the inherent neutral-point voltage deviation problems have always been the most troublesome features of NPC inverter. Hence, voltage balancing of DC-link capacitors is highly essential, where it is usually achieved through proper switching control. Space vector pulsewidth modulation is the most desirable switching scheme, where the voltage balancing control is mostly accomplished by decently allocating the dwell time for all the switching states. In this study, a fuzzy logic controller is incorporated to systematically control the dwell time allocation for all the switching states based on the instantaneous voltage of splitting DC-link capacitors. The proposed method is called the fuzzy-based dwell time allocation algorithm. To validate effectiveness and feasibility of the proposed algorithm, simulation work in MATLAB-Simulink and experimental implementation utilising TMS320F28335 digital signal processor (DSP) are performed. Both simulation and experimental results are presented, confirming effectiveness of the proposed algorithm in reducing the inherent voltage deviation problems of NPC inverter to a minimum level.