Traditional radar target recognition methods typically model only single echoes, neglecting the crucial information that domain knowledge can provide for understanding data. In this paper, we propose a knowledge embedding fusion (KEF) method for enhanced high-resolution range profile (HRRP) recognition, which utilizes the target state descriptions available during radar detection. KEF leverages a language model (LM) to integrate textual knowledge with echo features for fusion recognition. It consists of three components: HRRP feature extraction, measurement-based knowledge construction, and knowledge embedding fusion module. First, we perform feature extraction on the HRRP to obtain echo tokens. Next, in the knowledge construction module, the measurement statuses are standardized to a natural language format, and the LM is utilized to extract semantic information, resulting in text tokens. Finally, in the knowledge embedding fusion module, a cross-attention HRRP-text fusion strategy is employed to facilitate interaction between echo tokens and textual tokens. We also design a combination of HRRP-text matching loss and fusion classification loss to guide model training. Experiments are conducted on a real measured dataset, and the results indicate that KEF effectively enhances recognition performance across multiple scenarios compared with approaches that only utilize echoes. • We develop a knowledge embedding fusion based HRRP recognition method. • It converts measurement information into textual knowledge and utilizes a language model for representation modeling. • It employs a cross-attention mechanism to fully integrate measurement knowledge with HRRP features. • It uses classification loss and HRRP-text matching loss for joint optimization. • It can improve the HRRP target recognition performance in various scenarios.

Integrated Sensing and Communication (ISAC) systems are at the forefront of next-generation wireless technologies, enhancing high-precision target localization. Accurate prediction of localization performance is crucial for the design and optimization of these systems. Traditionally, the Cramer- Rao Lower Bound (CRLB) has been used as a theoretical benchmark for estimating localization errors, but it often does not reflect actual errors encountered in practice. The Monte Carlo simulation method, while accurate, is computationally intensive and less adaptable to varying parameters. To bridge this gap, we introduce LocNet-Mono, a novel approach based on monotonic neural networks, designed specifically for predicting localization errors. This approach maintains a consistent, monotonic relationship between input and output features, addressing the shortcomings of traditional methods. Our numerical experiments validate the high accuracy and efficiency of LocNet-Mono, confirming its potential as a superior tool for performance prediction in ISAC systems.

In this paper, a joint beam selection and power allocation (JBSPA) scheme for multi-target tracking is proposed in a collocated MIMO (C-MIMO) radar network. The goal of this scheme is to achieve better resource utilization efficiency with a given resource budget. Under the condition of sufficient resources, the scheme minimizes the total resource consumption of the C-MIMO radar network. When the sensor resources are insufficient, the scheme maximizes the number of tracked targets that meet the tracking requirements. To evaluate the performance of multi-target tracking, we normalize and utilize the Bayesian Cramér-Rao lower bound (BCRLB) as the performance evaluation criterion. The JBSPA scheme is formulated as a non-convex optimization problem involving integer and continuous variables that are coupled. To address this problem, we propose a fast and effective three-step solution technique. Simulation results demonstrate that the proposed JBSPA scheme can save resources, significantly increase the target capacity, and improve the resource utilization efficiency of the C-MIMO radar network.

Large-magnitude errors, known as outliers, have a significant impact on the accuracy of elliptic positioning (EP) for a distributed MIMO radar. Most of the existing EP techniques are developed in the least squares sense, susceptible to outliers. In this letter, we propose to leverage a sparse argument to model the outliers and decompose the measurement noise into two components, namely, inliers and outliers. By doing so, the robust EP is formulated as a$\ell _{0}$-norm minimization problem with non-convex constraints. By relaxing the objective$\ell _{0}$-norm with the convex$\ell _{1}$-norm, an alternating direction method of multipliers based algorithm is derived to efficiently solve the relaxed optimization problem. Numerical results illustrate the improved performance of the proposed method compared to the existing methods.

We address the multisensor multitarget tracking problem based on a hierarchical sensor network. In this setup, there is a fusion center, several cluster heads, and many sensors. Each sensor runs a Gaussian mixture probability hypothesis density (PHD) filter. The sensors send their locally calculated Gaussian components to the local cluster head in the presence of false data injection (FDI) and denial-of-service (DoS) attackers. We propose a hybrid PHD averaging fusion framework that consists of two parts: one uses the arithmetic average (AA) fusion to compensate for information shortage due to DoS and the other uses the geometric average (GA) fusion to suppress false information due to FDI. By integrating the respective zero forcing and avoiding behaviors of the two average fusion approaches, our proposed hybrid fusion scheme is proven resilient to both FDI and DoS attacks. Experimental results illustrate that our proposed algorithm can provide reliable tracking performance against FDI and DoS attacks.

Conditioned on measurement data from sensors, the joint probabilistic data association (JPDA) filter is a popular methodology for tracking multiple objects in clutter. The JPDA filter, however, suffers from the severe track coalescence effect, i.e., tracks following objects in close proximity tend to coalesce. To improve the tracking accuracy, we propose a novel Cauchy–Schwarz set JPDA (CSSJPDA) filter by optimizing the posterior Gaussian mixture density with an iterative successive component-based optimization (ISCO) algorithm in the Cauchy–Schwarz sense. The posterior Gaussian mixture density is marginalized by Gaussian densities to estimate object states at each time step. To improve the marginalization accuracy, the posterior density is optimized in the random finite set (RFS) family. We derive a closed-form expression of the Cauchy–Schwarz divergence between the posterior density and its Gaussian approximation and use it as a cost function for density optimization. To improve the optimization efficiency, we propose the ISCO algorithm to minimize the cost function successively along one Gaussian component at a time and prove its monotone convergence. Two indicative examples are used to illustrate the effectiveness of the CSSJPDA filter. With the iteration, the cost function decreases and the Gaussian approximation becomes more accurate. Simulation results demonstrate that the CSSJPDA filter provides a good tradeoff between the tracking accuracy and the computational efficiency compared to the existing methods. Numerical experiments with a real-world dataset further verify the performances of the proposed method.

This article considers the 3-D collaborative trajectory optimization (CTO) of multiple unmanned aerial vehicles to improve multitarget tracking performance with an asynchronous angle of arrival measurements. The predicted conditional Cramér–Rao lower bound is adopted as a performance measure to predict and subsequently control tracking error online. Then, the CTO problem is cast as a time-varying nonconvex problem subjected to constraints arising from dynamic and security (height, collision, and obstacle/target/threat avoidance). Finally, a comprehensive solution method (CSM) is presented to tackle the resulting problem, according to its unique structures. Specifically, if all security constraints are inactive, the CTO can be simplified as a nonconvex problem with convex dynamic constraints, which can be solved by the nonmonotone spectral projected gradient (NSPG) method. Oppositely, an alternating direction penalty method (ADPM) is presented to solve the CTO problem with some positive security constraints. The ADPM introduces auxiliary vectors to decouple the complex constraints and separates the CTO into several subproblems and tackles them alternately, while locally adjusting the penalty factor at each iteration. We show the subproblem w.r.t. the position vector is nonconvex but with convex constraints, which can be efficiently solved by the NSPG method. The subproblems w.r.t. the auxiliary vectors are separable and have closed-form solutions. Simulation results demonstrate that the CSM outperforms the unoptimized method in terms of tracking performance. Besides, the CSM achieves the near-optimal performance provided by the genetic algorithm with much lower computational complexity.

Bridge-over-water detection plays vital role in both civilian and military applications. Though widely studied previously, it is still a challenging problem. This is because bridges are with a high diversity of aspect ratios, shapes and orientations in practical. The detection performance is highly dependent on the annotation accuracy of training samples. To address these problems, this paper proposes a new coarse-to-fine strategy to detect oriented bridges over water. In coarse detection stage, a new backbone containing modulated deformable convolution is developed to enrich the diversity of receptive fields. The detection results are further refined by the prior knowledge. The oriented bounding boxes can be then obtained based on frequency domain analysis and edge detection. Different from previous works, oriented bounding box annotaions are not required in the training of the proposed method. Comparative experiments were conducted. The results demonstrate the effectiveness of the proposed method.

In this article, a composed resource optimization (CRO) scheme is developed for an active and passive radar network engaged in multiple target tracking (MTT). The motivation of the CRO scheme is to collaboratively optimize the transmit resources of active radars, as well as the receiving processing resources of passive radars, to improve the overall MTT performance. We utilize the predicted conditional Cramér–Rao lower bound to evaluate the impact of allocation strategies on tracking performance and formulate the CRO as a mixed-integer nonlinear program problem since the adaptable parameters w.r.t. the target selection process are in binary form. To solve the problem, we propose an alternating direction method of multiplier-based algorithm. This algorithm transforms the original problem into an equality constrained problem by introducing two auxiliary vectors. In such a case, the CRO problem can be tackled by alternately solving several simple subproblems. Specifically, the subproblem w.r.t. the resource vector is convex, and the subproblems w.r.t. the auxiliary vectors are separable. Simulation results demonstrate that the proposed CRO scheme outperforms the traditional allocation schemes in terms of MTT performance. In addition, the performance of the CRO scheme is close to the optimal performance provided by the exhaustive method, but the computation load of the CRO scheme is lower than that of the exhaustive method. Finally, physical interpretations are presented to support our conclusions.

The high orbit height and long coherent processing interval (CPI) of geosynchronous (GEO) targets lead to the problems of ultralow signal-to-noise ratio (ULSNR) and complex signal modulation, posing great challenges to the traditional radar target detection and imaging algorithms. To address the problems, this article proposes a novel model-data codriven integration algorithm of detection and imaging for GEO targets with wideband radar. In this technique, underpinned by the transformation relationships between multiple spatial coordinate systems and the orbit prior information of GEO targets, we deduce the analytical expressions of the effective rotational vector of GEO targets so as to accomplish the model-driven optimal subaperture selection for integration of detection and imaging (OSASIDI). This considerably improves the processing performance and algorithm efficiency compared with traditional data-driven methods at ULSNR. In addition, we derive the radar equation of GEO targets for integration of detection and imaging in detail, which guides OSASIDI by analyzing the impacts of different parameters on detection and imaging performance. Aiming at the complex signal modulation problem caused by ultralong CPI (ULCPI) during the optimal subaperture (OSA) at ULSNR, we innovatively propose a model-data codriven integration of detection and imaging algorithm (MDCDIDI), which can eliminate the complex spatial-time-variant motion errors caused by the dual time-variant characteristic (DTVC) of effective rotational vector, so as to realize the focus-before-detection and obtain the well-focused inverse synthetic aperture radar (ISAR) images. Extensive experimental results from simulated data, which are generated from actual GEO parameters and the computer-aided-design (CAD) model of the Tiangong-I (TG-I) satellite, corroborate the effectiveness of the proposed algorithm.

To solve target recognition under extended environments, this paper proposes sparse representation in the transformed domain. Since the signal energy in the frequency domain is mainly concentrated on a small portion of low frequencies, this part of spectrum therefore carry the vital information that distinguishes a class of target from the other. We intend to define a frequency descriptor by the bag of low frequencies. The defined descriptor is used to build sparse signal modeling. The frequency descriptors of the training are concatenated to form an over-complete dictionary. It is used to encode the counterpart of query as a linear combination of themselves. Sparsity has been harnessed to generate the optimal representation, from which the inference can be reached.

An important step in multi-sensor data fusion is sensor registration, namely, to estimate sensors' range and azimuth biases from their asynchronous measurements. Assuming the target moves in a straight line with an unknown constant velocity, we propose a two-stage nonlinear least square (LS) approach to this problem. More specifically, in stage I, each sensor first estimates its own range bias individually, and then in stage II, all sensors jointly estimate their azimuth biases. We show that both of the nonconvex LS problems can be solved to global optimality under mild conditions. Simulation results show that the root mean square error (RMSE) of the proposed approach is quite close to the Cramér-Rao lower bound (CRLB) when the level of the measurement noise is small.