Multi-vehicle tracking is crucial in many applications, such as traffic surveillance, intelligent transportation systems, and advanced driver assistance systems. Most conventional multi-target tracking algorithms are not ideal for multi-vehicle tracking, since they assume that the targets move independently of one another. However, due to traffic volume and limited lane resources, vehicles have to interact with their neighbors, resulting in highly dependent motions. To address this limitation, this paper proposes a novel multi-vehicle tracking algorithm for the single-lane case that considers motion dependence across vehicles by integrating the car-following model (CFM) into the tracking process with on-road constraints. A new CFM-based motion model that describes the dependent motion of vehicles in the single-lane case is proposed, and the notion of car-following clusters is defined. In order to exploit all available information in sensor measurements, the proposed algorithm updates the state estimates of car-following clusters by utilizing a stacked-update strategy. Furthermore, the variable structure interacting multiple model estimator is modified and integrated into the proposed algorithm to handle maneuvers that may violate the CFM. Simulation results demonstrate the superiority of the proposed multi-vehicle tracking algorithm over other state-of-the-art multi-vehicle tracking algorithms.

Geo-registration and geo-location of data collected by video sensors such as electro-optical and infrared cameras are two fundamental steps in the airborne surveillance of ground targets. With the availability of high resolution imaging sensors and detailed mapping or terrain data sources, video data plays an increasingly important role in modern surveillance platforms like unmanned aerial vehicles and airborne, ground or maritime surveillance systems. Surveillance systems without any compensation for the inevitable sensor registration errors, i.e., biases, may make geo-location erroneous and render the surveillance platform less effective for precision targeting. This paper deals with the modeling of sensor biases in geo-location and proposes a method to estimate them. The proposed method leads to a minimization problem with a nonlinear cost function. Detailed derivation of the bias model is given along with an algorithm to find the bias parameters. The achievable lower-bounds for debiased geo-location are provided and simulations are used to demonstrate the validity of the proposed method

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

In the multi-sensor localization problem, selecting sensors strategically to form an effective geometric configuration is crucial for enhancing both localization accuracy and system resource utilization. This study tackles the challenge of sensor selection for multi-target localization using time-of-arrival (TOA) measurements. Distinguishing from previous works that focus on single-target scenarios with only shared sensors, multi-target scenarios with both non-shared and shared sensors within sensor networks are considered in this paper. By adopting the A-optimal design criterion and introducing a Boolean matrix to indicate the sensor selection and allocation status, we formulate the sensor selection problem as an integer programming (IP) problem with non-convex functional constraints imposed by non-shared sensors. Semi-definite relaxation (SDR) technique is then employed to relax the rank-1 and integer constraints, transforming the IP problem into a semi-definite programming (SDP) problem. Further, a fast binary rounding algorithm is proposed to discretize the continuous solution of the SDP problem. Simulation results demonstrate that the proposed method significantly reduces the computational burden and provides superior performance compared to the exhaustive search method and other existing methods.

In recent years, with the development of deep learning, deep learning (DL)-based specific emitter identification (SEI) has outperformed the traditional methods in accuracy. However, DL-based methods often depend on huge labeled samples for training, which are not available in certain situations. Semi-supervised methods can be trained with a large number of unlabeled samples and few labeled samples, thereby reducing the need for manual labeling signals. In this article, a semi-supervised SEI method based on contrastive learning and data augmentation, SSMPCL-SEI, is proposed. SSMPCL-SEI modifies the self-supervised contrastive learning to cope with multiple positive samples and learns category information from both labeled and unlabeled samples. Furthermore, data augmentation for the classifier is used after pre-training to improve the classification ability. The experimental results with a UAV dataset and an ADS-B dataset show that SSMPCL-SEI yields better identification performance than other state-of-the-art semi-supervised SEI methods.

Based on air temperature, relative humidity, air pressure, and wind speed data from 4 buoy stations in the northern South China Sea, the homogeneity of the evaporation duct height (EDH) obtained from sea surface temperature (SST) from buoys and SST from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 Reanalysis (ERA-5) data are compared using the Naval-Postgraduate-School (NPS) model. The findings are given as follows: (1) the SST of the ERA-5 has highly accurate (the difference between the buoys and ERA-5 is mainly falling in –0.5°C~1.5°C) and shows strong correlations (≥0.95) with the observed data. (2) The probability of the EDH difference at –2m~2m obtained from the ERA-5 is lower than the actual observation, which underestimates the uniformity of the EDH. (3) The SST difference of buoys and ERA-5 at 0°C to 1.0°C has a more significant impact on the assessment of non-uniformity of EDH than the SST difference at –0.5°C~0°C and 1.0°C~1.5°C (take up above 60%). In conclusion, although the uniformity of the EDH may be underestimated based on the ERA-5 data, it still demonstrates the uniformity characteristic of the EDH quite well.