Junkun Yan

Simultaneous Multibeam Resource Allocation Scheme for Multiple Target Tracking

A colocated multiple-input multiple-output (MIMO) radar system has the ability to address multiple beam information. However, the simultaneous multibeam working mode has two finite working resources: the number of beams and the total transmit power of the multiple beams. In this scenario, a resource allocation strategy for the multibeam working mode with the task of tracking multiple targets is developed in this paper. The basis of our technique is to adjust the number of beams and their directions and the transmit power of each beam through feedback, with the purpose of improving the worst tracking performance among the multiple targets. The Bayesian Cramér-Rao lower bound (BCRLB) provides us with a lower bound on the estimated mean square error (MSE) of the target state. Hence, it is derived and utilized as an optimization criterion for the resource allocation scheme. We prove that the resulting resource optimization problem is nonconvex but can be reformulated as a set of convex problems. Therefore, optimal solutions can be obtained easily, which greatly aids real-time resource management. Numerical results show that the worst case tracking accuracy can be efficiently improved by the proposed simultaneous multibeam resource allocation (SMRA) algorithm.

Power Allocation Algorithm for Target Tracking in Unmodulated Continuous Wave Radar Network

Unmodulated continuous wave (UCW) radar has been shown to have many unique features. With the recent development, UCW radar network has become an attractive platform for target tracking. In practice, to achieve better tracking performance, UCW radars are supposed to maximize their transmitted power, which may be in contradiction with the limited energy resources of themselves. Therefore, a performance-driven power allocation algorithm for Doppler-only target tracking in a UCW radar network is proposed. This algorithm can be viewed as a reaction of the cognitive transmitter to the environment perceived by the receiver to minimize the target state estimation mean square error with a given total power budget. The Bayesian Cramér-Rao lower bound gives a measure of the achievable optimum performance for target tracking and, importantly, it can be calculated predictively. Therefore, it is derived and utilized as an optimization criterion for the power allocation algorithm. The resulting optimization problem is proved to be convex, and hence, can be solved by gradient projection method. Numerical results show that the target tracking accuracy can be efficiently improved by the proposed algorithm.

Prior Knowledge-Based Simultaneous Multibeam Power Allocation Algorithm for Cognitive Multiple Targets Tracking in Clutter

In this paper, a power allocation scheme for tracking multiple targets, with radar measurements either target generated or false alarms, is developed for colocated multiple-input multiple-output (MIMO) radar system. Such a system adopts a multibeam concept, in which multiple simultaneous transmit beams are synthesized by different probing signals from various colocated transmitters. To ensure that the limited power resource can be exploited effectively, we adjust the transmit power of each beam according to the prior knowledge predicted from the tracking recursion cycle. Specifically, the whole algorithm can be viewed as a reaction of the cognitive transmitters to the environment, in order to improve the worst case tracking performance of the multiple targets. By incorporating an information reduction factor (IRF), the Bayesian Cramér-Rao lower bound (BCRLB) gives a measure of the best achievable performance for target tracking in clutter. Hence, it is derived and utilized as an optimization criterion for the simultaneous multibeam power allocation algorithm. The optimization problem is nonconvex and is solved by the modified gradient projection (MGP) method in this paper. Simulation results show that the proposed algorithm significantly outperforms equal power allocation, in terms of the worst case tracking root mean-square error (RMSE).

Joint Beam Selection and Power Allocation for Multiple Target Tracking in Netted Colocated MIMO Radar System

In this paper, a joint beam selection and power allocation (JBSPA) strategy is developed for multiple target tracking in netted colocated multiple-input multiple-output radar system. Each radar in this network adopts a multibeam working mode, in which multiple simultaneous transmit beams are synthesized. The basis of the JBSPA strategy is to use the optimization technique to control the limited beam and power resource of each radar in order to achieve accurate target state estimation. The Bayesian Cramér-Rao lower bound is derived, normalized, and subsequently utilized, as the optimization criterion for the JBSPA strategy. The resulting optimization problem consists of two adaptable parameters, one for beam selection and the other for power allocation. By introducing an auxiliary vector, a fast two-step solution technique is presented to jointly decide the number of beams generated by each radar, as well as the assignment and transmit power of each beam, subject to some resource constraints. Simulation results verify the superiority of the proposed JBSPA algorithm, in terms of the worst-case tracking accuracy of the multiple targets.

Compressive Sensing of Stepped-Frequency Radar Based on Transfer Learning

It usually suffers from long observing time and interference sensitivity when a radar transmits the high-range-resolution stepped-frequency chirp signal. Motivated by this, only partial pulses of the stepped-frequency chirp are utilized. For the obtained incomplete frequency data, a Bayesian model based on transfer learning is proposed to reconstruct the corresponding full-band frequency data. In the training phase, a complex beta process factor analysis (CBPFA) model is utilized to statistically model each aspect-frame from a set of given full-band frequency data, whose probability density function (pdf) can be learned from this CBPFA model. It is important to note that the numbers of factors and dictionaries are automatically learned from the data. The inference of CBPFA can be performed via the variational Bayesian (VB) method. In the reconstruction phase for the incomplete frequency data that “related” to the training samples, its corresponding full-band frequency data can be analytically reconstructed via the compressive sensing (CS) method and Bayesian criterion based on the transfer knowledge of the previous pdfs learned from the training phase. About the “relatedness” between each training frame and the incomplete test frequency data, we utilize the frame condition distribution of incomplete test frequency data to represent. The proposed method is validated on the measured high range resolution (HRR) data.

Joint Detection and Tracking Processing Algorithm for Target Tracking in Multiple Radar System

In this paper, a joint detection and tracking processing (JDTP) algorithm is proposed for target tracking in clutter using a multiple radar system. In this paper, the data association events are formed with a reasonable assumption that each radar can at most receive one measurement originated from a target. Moreover, we explore the idea of feeding the information from the tracker to the detector. In this scenario, the tracker can guide the detectors of multiple radars where to look for a target while keeping the constant false alarm rate property. From a practical point of view, the detection threshold is depressed near where a target is expected to be and elevated where it is unexpected. Simulation results demonstrate the efficiency of the proposed JDTP algorithm, in terms of the detection and the tracking performance, when compared with the existing works.

Benefit Analysis of Data Fusion for Target Tracking in Multiple Radar System

With the recent development in radar technology, a multiple radar system (MRS) has become an attractive platform for target tracking. Technically speaking, data fusion among multiple radars can definitely enhance the tracking performance. However, the enhancement may not always be significant, as the improvement depends on several factors, such as the signal-to-noise ratio, the deployment, and the resolution of each radar. In this paper, a benefit analysis of data fusion for target tracking in MRS is developed. In particular, the analysis is on whether, for a given target in a given environment, the fusion between two radars is worthy to be implemented. First, the performance enhancement achieved by individual radar, in terms of the Bayesian Cramér-Rao lower bound, is derived as a recursive procedure. On this basis, a scalar parameter is then defined, according to which the decision on whether to fuse the data from two radars or use individual radar instead to track a target can be made. Finally, simulation results demonstrate the correctness of fusion rule defined in this paper.

A fast efficient power allocation algorithm for target localization in cognitive distributed multiple radar systems

It is well-known that the power allocation can enhance the power utilization of the distributed radar systems. We first analyze two interesting non-increasing properties of Cramer-Rao low bound (CRLB) for target location via distributed multiple radar systems. On the basis of the classical power allocation methods 15, this paper proposes a fast efficient power allocation algorithm applied to cognitive distributed multiple radar systems, which depends greatly on an alternating global search algorithm(AGSA). In this paper, our aim is directly to minimize the non-convex CRLB 15 of target location estimation. The convergence of the proposed algorithm is theoretically analyzed by LaSalle invariance principle. We analyze the computational complexity of the two closely-related algorithms. The famous Pareto optimal set associated with power allocation is obtained by the proposed algorithm, and it can indirectly derive the solution to problem for minimizing total power budget. Experimental results demonstrate that our algorithm has quick convergence and good performance.

Cooperative Target Assignment and Dwell Allocation for Multiple Target Tracking in Phased Array Radar Network

Abstract Motivated by networked anti-missile defense applications, a cooperative target assignment and dwell allocation (CTADA) algorithm is developed for multiple target tracking (MTT) in phased array radar (PAR) network. The basis of the CTADA scheme is to not only optimize the target-to-radar assignment, but also effectively allocate the limited time resource of each PAR to its responsible targets, such that the MTT performance could be efficiently improved in overload situations (the number of targets greatly exceeds the number of PARs). We formulate the resource allocation framework as a mathematical optimization problem, and use the normalized Bayesian Cramer-Rao lower bound as its objective function. The resulting optimization problem consists of two adaptable parameters, one for target-to-radar assignment and the other for dwell allocation. By exploiting the unique relationship between these two adaptable parameters, an efficient two-step solution technique, which consists of a convex relaxation step and a heuristic dividing step, is developed for the CTADA optimization problem. Simulation results verify the superiority of the proposed CTADA algorithm, in terms of the worst case tracking accuracy of the multiple targets.

Joint Threshold Adjustment and Power Allocation for Cognitive Target Tracking in Asynchronous Radar Network

In this paper, a joint threshold adjustment and power allocation (JTAPA) algorithm is developed for target tracking in asynchronous radar network (ARN). The basis of the JTAPA strategy is to feed back the target track information from the fusion center to local radar sites to enhance both the target detection capability and the resource utilization efficiency of the ARN. For the detector, we develop a threshold adjustment (TA) algorithm for better detection performance, based on the predicted target location information fed back from the fusion center. For the transmitter, we build an asynchronous power allocation (APA) model based on the perceptual information, and use an optimization technique to control the limited power resource for the next period of time. The goal of the APA scheme is to achieve better target tracking accuracy with a given power budget. The Bayesian Cramér–Rao lower bound is derived, normalized, and subsequently utilized, as the optimization criterion for the APA strategy. The resulting nonconvex optimization problem is solved through relaxation incorporating the spectral projected gradient technique. Simulation results demonstrate that the integration of the TA and APA processes can evidently improve the tracking performance.