Chunbo Xiu

A model predictive control (MPC) strategy is proposed based on state observation and updating for image-based visual servoing (IBVS) tasks of micro aerial vehicles (MAVs). This control strategy enables precise pose adjustment of MAVs without relying on the global positioning system (GPS). Specifically, image features are first defined on a virtual image plane to decouple the translational motion of the MAV. Subsequently, a linear velocity observer is developed to provide high-quality real-time velocity information for the MAV during IBVS execution. Furthermore, the image dynamics on the virtual image plane are linearized using a first-order Taylor expansion, and a linear MPC controller is formulated to efficiently compute the optimal control inputs. Moreover, the state inputs to the MPC controller are updated at each control cycle to eliminate errors accumulated during the rolling optimization based on the linearized dynamics, thereby ensuring the precision of IBVS. Simulation and experimental results demonstrate the performance of the proposed observer and control strategy.

To solve the problem of decreased tracking performance of the Interactive Multiple Model (IMM) algorithm when tracking underwater maneuvering targets due to model switching delay, an improved adaptive IMM algorithm, named Neural Network Interactive Multiple Model Unscented Kalman Filter (NIMM-UKF), is proposed based on the Convolutional Neural Network-Gated Recurrent Unit (CNN–GRU) model combined with an attention mechanism. State estimates, observation data, and model probabilities are used as inputs to the network. A convolutional neural network (CNN) is used to extract spatial features from the input, and the attention mechanism assigns weights to these features to highlight the most important ones. A gated recurrent unit (GRU) is used to extract features from the time series, preserving key information while suppressing redundancy. Subsequently, the Transition Probability Matrix (TPM) is updated. The experimental results show that compared with the IMM-UKF algorithm, the proposed algorithm reduces the root mean square errors (RMSE) of position and velocity by 14.3% and 32.0%, respectively, and reduces the mean absolute errors (MAE) of position and velocity by 14.4% and 53.8%, respectively, when tracking underwater maneuvering targets. Therefore, this algorithm can effectively mitigate the tracking performance degradation caused by model-switching delay.

An improved adaptive constant false alarm rate (CFAR) detector based on fuzzy theory is proposed to address the issue of poor detection performance and robustness of the variability index (VI) class CFAR detectors due to the misjudgment of the background environment and other reasons. The integration of the order statistic threshold adjustable detection algorithm (OSTA) into the adaptive CFAR detector has the potential to address the aforementioned issue. Therefore, in a clutter edge environment, the ratio of the means of the leading and lagging windows is calculated separately, and the differences between these mean ratios and predefined thresholds are used as inputs to the fuzzy inference machine, and the background clutter estimation of the OSTA is determined based on the fuzzy output, which can extend the range of values of the background clutter estimation, and improve the detection performance of the OSTA in this environment. The Kaigh–Lachenbruch quantile detection algorithm (KLQ) exhibits robust detection performance in multiple-target environments. Therefore, KLQ is used to detect targets in this environment, further improving the detection performance of the detector. The experimental results show that in multiple-target environments with an average misjudgment rate of 27.48%, the proposed detector increases the detection probability by 15.58% compared to the recently proposed variability index heterogeneous clutter estimate modified ordered statistics CFAR detector (VIHCEMOS-CFAR), and in a clutter edge environment, the false alarm rate of the proposed detector was reduced by approximately 89.64% compared to VIHCEMOS-CFAR. Additionally, the effectiveness of the proposed detector is also validated by real clutter data. It can be seen that the proposed adaptive CFAR detector has better robustness to the misjudgment of the background environment and better overall detection performance regardless of the environment.

In order to improve the detection performance of the radar constant false alarm detector in a multiple-target environment, a Kaigh–Lachenbruch Quantile constant false alarm rate detector based on composite fuzzy fusion rules (CFKLQ-CFAR) is designed by combining fuzzy fusion rules and the Kaigh–Lachenbruch Quantile constant false alarm rate detector. Two sensors are used to collect environmental information, and the membership function value is calculated based on the collected information. Furthermore, the presence or absence of the target is judged compositely by four fuzzy fusion rules. CFKLQ-CFAR is applied to the variability index CFAR (VI-CFAR) detector, and an adaptive constant false alarm rate detector based on the composite fuzzy fusion rules (CFVI-CFAR) is designed to improve the performance of the radar constant false alarm detector in different environments. The simulation experiment results show that the average detection probability of CFKLQ-CFAR is 2.67% and 1.00% higher than that of KLQ-CFAR and the fuzzy logic fusion detector (FUMCA-CFAR) in a multiple-target environment. The average detection probability of CFVI-CFAR is 3.66% higher than that of the variability index heterogeneous clutter estimate modified ordered statistics CFAR (VIHCEMOS-CFAR) in a multiple-target environment, while in a clutter edge environment, the average false alarm probability of CFVI-CFAR is only 1.65% of that of VIHCEMOS-CFAR. Therefore, the performance of the radar constant false alarm detector has been effectively improved.

In modern radar systems, the Generalized compound distribution model is more suitable for describing the amplitude distribution characteristics of radar sea clutter. Accurately and efficiently simulating sea clutter has important practical significance for radar signal processing and sea surface target detection. However, in traditional zero memory nonlinearity (ZMNL) method, the correlated Generalized compound distribution model cannot deal with non-integral or non-semi-integral parameter. In order to overcome this shortcoming, a new method of generating correlated Generalized compound distributed clutter is proposed, which changes the generation method of Generalized Gamma distributed random sequences in traditional Generalized compound distribution models. Firstly, by combining with the Gamma distribution and using the additivity of the Gamma distribution, the Probability Density Function (PDF) of Gamma function is transformed into a second-order nonlinear ordinary differential equation, and the Gamma distributed sequence under arbitrary parameter is solved. Then the Generalized Gamma distributed sequence with arbitrary parameter can be obtained through the nonlinear transformation relationship between the Generalized Gamma distribution and the Gamma distribution, so that the shape parameters of the Generalized compound distributed sea clutter are extended to general real numbers. Simulation results show that the proposed method is not only suitable for clutter simulation with non-integral or non-semi-integral shape parameter values, but also further improves the fitting degree.

In the complex clutter background, the clutter center frequency is not fixed, and the spectral width is wide, which leads to the performance degradation of the traditional adaptive clutter suppression method. Therefore, an adaptive clutter intelligent suppression method based on deep reinforcement learning (DRL) is proposed. Each range cell to be detected is regarded as an independent intelligence (agent) in the proposed method. The clutter environment is interactively learned using a deep learning (DL) process, and the filter parameter optimization is positively motivated by the reinforcement learning (RL) process to achieve the best clutter suppression effect. The suppression performance of the proposed method is tested on simulated and real data. The experimental results indicate that the filter notch designed by the proposed method is highly matched with the clutter compared with the existing adaptive clutter suppression methods. While suppressing the clutter, it has a higher amplitude-frequency response to signals at non-clutter frequencies, thus reducing the loss of the target signal and maximizing the output signal-to-clutter and noise rate (SCNR).

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.