Robust Weighted l1,2 Norm Filtering in Passive Radar Systems
Abstract
:1. Introduction
- Although a large number of studies focus on passive radars with mobile communication signals such as UMTS, these studies assume that the communication signal is demodulated perfectly. While this a valid assumption, it overlooks the errors caused by less than perfect demodulation. The demodulation causes an impulsive noise on the received signals, which will deteriorate the detection performance. Unlike other studies, perfect demodulation assumption is dropped in this study to provide an approach that is better suited for real life applications.
- The major novelty of the proposed algorithm is to construct a new objective function that solves the problem of detecting low RCS targets in the presence of impulsive noise by weighting the and norm. We aim to provide a general solution framework for the detection problem using the and the norms as a weighted form while preserving the superiority of these two norms in different environmental conditions. Thus, a new filtering concept is created by adopting weighted and norm optimization.
- There are studies in the literature where delay-Doppler is estimated by filters defined in the space. In these studies, the cross ambiguity function is calculated for a fixed p-value. Whereas, the new filtering concept that can adaptively adjust the filter characteristics according to the impulsiveness level of the noise is proposed in this paper. Adaptive adjustment of the proposed filtering concept will prevent performance degradation of the proposed algorithm in environments with different noise characteristics.
- In the few existing studies where the noise is modeled as impulsive, the performance analysis is limited to simulation results. However, in this paper, the performance analysis of the proposed algorithm under impulsive noise is also carried out on real data from an operation at passive radar platform.
2. Signal and Noise Models
2.1. Signal Model
2.2. Noise Model
Stable Noise Model
3. Conventional Methods
3.1. Cross Ambiguity Function
3.2. Fractional Lower Order Statistics Cross Ambiguity Fuction
4. Proposed Algorithm
5. Simulation and Experimental Results
5.1. Simulation Results
5.1.1. Performance Criteria
5.1.2. Scenarios
- It can be said that the PoD value of the proposed algorithm is higher than these of the other two algorithms throughout the range.
- While the proposed method and the FLOSCAF method produce consistent results throughout the entire range, the detection performance of the CAF method increases as the value increases.
- When RMSE graphs and PoD graphs are considered together, it is observed that the graphs show interrelated results. As the PoD values of the algorithms increase, the RMSE values decrease.
- Finally, it is concluded that as the RCS values of the targets increase, the detection performances of all three algorithms improve.
5.2. Experimental Results with Real Data
5.2.1. Scenarios
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Target | Time Delay Cell | Doppler Shift Cell | Radar Cross Section (RCS) (m2) |
---|---|---|---|
1 | 2 | −6 | 0.5/1/2 |
2 | 9 | −6 | 0.5/1/2 |
3 | 9 | −3 | 0.5/1/2 |
4 | 12 | −1 | 0.5/1/2 |
5 | 5 | 3 | 0.5/1/2 |
6 | 10 | 3 | 0.5/1/2 |
7 | 2 | 6 | 0.5/1/2 |
8 | 8 | 6 | 0.5/1/2 |
A1 | 12 | 0 | 0.5/1/2 |
A2 | 2 | 5 | 0.5/1/2 |
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Satar, B.; Soysal, G.; Jiang, X.; Efe, M.; Kirubarajan, T. Robust Weighted l1,2 Norm Filtering in Passive Radar Systems. Sensors 2020, 20, 3270. https://doi.org/10.3390/s20113270
Satar B, Soysal G, Jiang X, Efe M, Kirubarajan T. Robust Weighted l1,2 Norm Filtering in Passive Radar Systems. Sensors. 2020; 20(11):3270. https://doi.org/10.3390/s20113270
Chicago/Turabian StyleSatar, Baris, Gokhan Soysal, Xue Jiang, Murat Efe, and Thiagalingam Kirubarajan. 2020. "Robust Weighted l1,2 Norm Filtering in Passive Radar Systems" Sensors 20, no. 11: 3270. https://doi.org/10.3390/s20113270
APA StyleSatar, B., Soysal, G., Jiang, X., Efe, M., & Kirubarajan, T. (2020). Robust Weighted l1,2 Norm Filtering in Passive Radar Systems. Sensors, 20(11), 3270. https://doi.org/10.3390/s20113270