It is often difficult to obtain sufficient training data for adaptive signal detection, which is required to calculate the unknown noise covariance matrix. Additionally, interference is frequently present, which complicates the detecting issue. We provide a two-step method, termed interference cancellation before detection (ICBD), to address the issue of signal detection in the unknown Gaussian noise and subspace interference. The first involves projecting the test and training data to the interference-orthogonal subspace in order to suppress the interference. Utilizing traditional adaptive detector design ideas is the next stage. Due to the smaller dimension of the projected data, the ICBD-based detectors can function with little training data. The ICBD has two additional benefits over traditional detectors. Lower computational burden and proper operation with interference being in the training data are two additional benefits of ICBD-based detectors over conventional ones. We also give the statistical properties of the ICBD-based detectors and demonstrate their equivalence with the traditional ones in the special case of a large amount of training data containing no interference.

We consider the adaptive target detection problem in colocated MIMO radar where disturbance covariance matrix is unknown, and no training data is available. A tunable detector is proposed, which includes the Rao and Wald tests as special cases. The selectivity or robustness of the proposed detector to mismatched signals can be flexibly tuned by adjusting the tuning parameter. An equivalent form of the proposed detector is obtained to provide a deeper insight into how the selectivity or robustness is adjusted by selecting the tuning parameter. We derive analytical expressions for the probability of false alarm and the detection probability to evaluate its detection performance in both matched and mismatched steering vector cases. It is shown that the proposed detector bears a constant false alarm rate property with respect to the disturbance covariance matrix. Numerical results reveal that the selectivity of the proposed detector can be enhanced by increasing the value of the tuning parameter.

In this part of the paper, we continue to study the problem of detecting a double subspace signal in Gaussian noise. Precisely, we address the detection problem in partially homogeneous environments, where the primary and secondary data share the same covariance matrix up to an unknown scaling factor. We derive the generalized likelihood ratio test (GLRT), Rao test, Wald test, and their two-step versions. We also introduce three spectral norm tests (SNTs). All these detectors possess the constant false alarm rate (CFAR) property. Moreover, various kinds of special cases of these detectors are given. At the stage of performance evaluation, we consider two cases. One is the case of no signal mismatch. The other is more general, namely, the case of signal mismatch, including the column-space signal mismatch and row-space signal mismatch.

The “defocused transmit-focused receive” (DTFR) mode in the distributed multiple-input multiple-output (MIMO) radar network is very effective in multitarget tracking. In this mode, a completely defocused beam is transmitted and a focused receive beam is synthesized so that the MIMO radar is capable of tracking targets independently. A joint target assignment and power allocation (TAPA) strategy is developed for multiple distributed MIMO radar networks in cluttered environment using the DTFR mode. Our aim is to achieve the better system tracking accuracy under the constraints of receive beam direction capability and power budget. We derive the posterior Cramer-Rao lower bound (PCRLB) and adopt it as the objective function, since it quantifies the precision of target state estimates. It is shown that the TAPA problem is a mixed integer programming and NP-hard problem, where two involved parameters, i.e., the target-radar assignment and power allocation, are both coupled in the objective and in the constraints. By introducing an intermediate variable, we propose an efficient two-step-based solution for solving this problem. The simulation results show the superior performance and adaptivity compared with existing algorithms.