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.

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.

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.

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.

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.