Target detection, tracking and classification are three essential and closely coupled subjects for most surveillance systems. In the finite set statistics (FISST) framework, this paper presents a Bayesian and recursive solution to joint detection, tracking and classification (JDTC) of a manoeuvring target in a cluttered environment, which is inspired by previous work on joint target tracking and classification in the classical Bayesian filter framework. The derived JDTC algorithm exploits the dependence of target state on target class by using class‐dependent dynamical model sets. The relative merits of this JDTC algorithm are demonstrated via a two‐dimensional example using a sequential Monte Carlo implementation. It is shown that handling those three closely coupled subjects jointly can achieve comparable detection and tracking performance to that of the exact filter in the FISST framework with a prior known class. The classification results are consistent with the previous work.

Radar micro-motion signatures help to judge the target’s motion state and threat level, which plays a vital role in space situational awareness. Most of the existing micro-motion feature extraction methods derived from time-frequency (TF) representation cannot simultaneously satisfy the requirements of high resolution and multiple component representation, which has limitations on processing intersected multi-component micro-motion signals. Meanwhile, as the micro-motion features extracted from the TF spectrograms only focus on the global characteristics of the targets and ignore the physical properties of micro-motion components, it leads to poor performance in micro-motion discrimination. To address these challenges, we empirically observed a decrease in the probability of intersection between the components within the time-frequency-frequency rate (TFFR) space, where components appeared as separated and non-intersecting spatial trajectories. This observation facilitates the extraction and association of multiple components. Given the differences in modulation laws among various micro-motions in the TFFR space, we introduced a novel micro-motion identification method based on scatterer-level TFFR representation. Our experimental evaluations of different targets and micro-motion types demonstrate the efficacy and robustness of this proposed method. This method not only underscores the separability of signal components but also expands the scope of micro-motion discrimination within the TFFR domain.

The water-filling (WF) algorithm is a widely used design strategy in the radar waveform design field to maximize the signal-to-interference-plus-noise ratio (SINR). To address the problem of the poor resolution performance of the waveform caused by the inability to effectively control the bandwidth, a novel waveform-related optimization model is established in this paper. Specifically, a corrected SINR expression is first derived to construct the objective function in our optimization model. Then, equivalent bandwidth and energy constraints are imposed on the waveform to formulate the waveform-related non-convex optimization model. Next, the optimal frequency spectrum is obtained using the Karush–Kuhn–Tucker condition of our non-convex model. Finally, the transmit waveform in the time domain is synthesized under the constant modulus constraint. Different experiments based on simulated and real-measured data are constructed to demonstrate the superior performance of the designed waveform on the SINR and equivalent bandwidth compared to the linear frequency modulated signal and waveform designed by the WF algorithm. In addition, to further evaluate the effectiveness of the proposed algorithm in the application of cognitive radar (CR), a closed-loop radar system design strategy is introduced based on our waveform design method. The experiments under real-measured data confirm the advantages of CR compared to the traditional open-loop radar structure.

The cognitive radar framework presents a closed-loop adaptive processing paradigm that ensures the efficient acquisition of target information while exploring the environment and enhancing overall sensing performance. In this study, instead of mutual information, we employed the squared Pearson correlation coefficient (SPCC) to measure the target information in observations specifically considering only linear dependency. A waveform design method is proposed that simultaneously maximizes target information and minimizes the integrated sidelobe level (ISL) under the constant modulus constraint (CMC). To enhance computational efficiency, we reformulated the constrained problem as an unconstrained optimization problem by leveraging the inherent geometric property of CMC. Additionally, we present two conditional equivalences associated with waveform design in relation to target information. The simulation results validate the feasibility and effectiveness of the proposed method.

Dempster's rule is a powerful tool for combining multiple distinct pieces of evidence in evidence theory. When conflict between pieces of evidence is high, the combination result of Dempster's rule is counterintuitive. Many alternative combination rules have been proposed. Most of these alternative combination rules are based on the redistribution of Shafer's conflicting mass. The quantitative definition of generalized conflict is given. Based on Proportional generalized conflict redistribution (PGCR), a new class of evidence combination rules is proposed and the links with some existed rules are established. A special instance of the PGCR rules referred to as PGCR-A is proposed by using the subsets' average support degrees as the proportional coefficients. PGCR-A rule can deal with the combination of high conflict evidence. During the process of combination, PGCR-A rule can also automatically determine the weights of the basic belief mass converging to the more specific subsets according to the focal elements' cardinalities. Since PGCR-A rule is not associative, a modified version is proposed to make it quasi-associative.

Parameter estimation is significant to prediction and estimation in the field of radar clutter characteristics. Therefore, it is necessary to study the problem of parameter estimation. The K-distribution is a commonly used model in sea clutter, which is a two-parameter model with shape parameters and scale parameters. The value of the shape parameters should be greater than 0. Moment estimation is usually used to estimate the parameters of the K-distribution. It overcomes the disadvantage of large computation compared with the maximum likelihood estimation method. However, the moment estimation usually uses two different order origin moments to solve the parameters. The joint solution of different order will cause large calculation errors, and sometimes the shape parameter is estimated to be less than 0. In the origin moment expression, the order k can be regarded as a continuous variable. By calculating the relationship between the k-order origin moment and its derivative, a parameter estimation method based on the origin moment derivative is proposed. The estimation efficiency and accuracy are compared with some moment estimation methods. Both simulation data and measured clutter data show that this method can achieve 100% estimation efficiency, can obtain higher estimation accuracy, and can also avoid the situation where the estimated value of the shape parameter is less than 0. Using the same idea to estimate the parameters in the two-parameter models, log–normal and Weibull distribution, we can also obtain the parameters with higher estimation accuracy. The experiments show that the higher-order origin moments are sensitive to the data, and the lower-order moments should be selected as far as possible. By selecting the appropriate order k, we can obtain ideal estimation parameters.